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Doctoral Thesis

Temperature dependence of activity coefficients in organicaerosols

Author(s): Ganbavale, Gouri

Publication Date: 2014

Permanent Link: https://doi.org/10.3929/ethz-a-010109026

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Diss. ETH No. 21374

Temperature Dependence ofActivity Coefficients in Organic

Aerosols

A dissertation submitted to

ETH ZURICH

for the degree of

Doctor of Sciences

presented by

GOURI GANBAVALE

M.Sc in Space Sciences, University of Pune

born 14. September 1983

citizen of India

accepted on the recommendation of

Prof. Dr. Thomas Peter, examiner

Dr. Claudia Marcolli, co-examiner

Dr. David Topping, co-examiner

2014

To loving memories of my parents..

Contents

Abstract ix

Zusammenfassung xiii

1 Introduction 1

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Vertical structure of Earth’s atmosphere . . . . . . . . . . . . . 6

1.2.1 Composition of the Earth’s atmosphere . . . . . . . . . 8

1.3 Aerosols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.3.1 Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.3.2 Sinks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.3.3 Size distribution . . . . . . . . . . . . . . . . . . . . . . 13

1.3.4 Chemical composition . . . . . . . . . . . . . . . . . . . 15

1.4 Radiative Forcing . . . . . . . . . . . . . . . . . . . . . . . . . . 16

1.5 Aerosol thermodynamics . . . . . . . . . . . . . . . . . . . . . . 19

2 Chemical Thermodynamics and Molecular Interactions 25

2.1 Thermodynamics of multicomponent systems . . . . . . . . . . 25

2.1.1 Homogeneous Open and Closed System . . . . . . . . . 26

2.1.2 Thermodynamic Equilibrium . . . . . . . . . . . . . . . 30

2.1.3 Chemical Potential of Ideal Gas . . . . . . . . . . . . . . 32

2.1.4 Ideal Solutions . . . . . . . . . . . . . . . . . . . . . . . 33

2.1.5 Non-ideal Solutions . . . . . . . . . . . . . . . . . . . . . 35

v

vi

2.1.6 Gibbs excess energy . . . . . . . . . . . . . . . . . . . . 38

2.2 Solubility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.2.1 Solid-liquid equilibria . . . . . . . . . . . . . . . . . . . 42

2.3 Intermolecular Interactions . . . . . . . . . . . . . . . . . . . . 47

2.3.1 Ion-dipole forces . . . . . . . . . . . . . . . . . . . . . . 49

2.3.2 Dipole-Dipole forces . . . . . . . . . . . . . . . . . . . . 50

2.3.3 Dipole-induced dipole interactions . . . . . . . . . . . . 50

2.3.4 Dispersion forces . . . . . . . . . . . . . . . . . . . . . . 51

2.3.5 Hydrogen bonds . . . . . . . . . . . . . . . . . . . . . . 53

3 Improved AIOMFAC temperature dependence 57

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3.2 AIOMFAC model . . . . . . . . . . . . . . . . . . . . . . . . . . 63

3.2.1 Group-contribution method . . . . . . . . . . . . . . . . 65

3.2.2 Short-range contribution . . . . . . . . . . . . . . . . . . 65

3.3 Experimental data . . . . . . . . . . . . . . . . . . . . . . . . . 71

3.3.1 Solid-liquid equilibrium . . . . . . . . . . . . . . . . . . 71

3.3.2 Water activity measurements . . . . . . . . . . . . . . . 74

3.3.3 Liquid-liquid equilibria data . . . . . . . . . . . . . . . . 74

3.3.4 Vapour-liquid equilibria . . . . . . . . . . . . . . . . . . 75

3.4 Objective function and model parameter estimation . . . . . . 77

3.4.1 Dataset weighting and temperature range . . . . . . . . 78

3.5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . 81

3.5.1 Aqueous organic mixtures . . . . . . . . . . . . . . . . . 82

3.5.2 Binary organic mixtures . . . . . . . . . . . . . . . . . . 86

3.5.3 Scope and limitations of the new parameterisation . . . 88

3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

3.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

3.7.1 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . 126

vii

4 Experimental temperature dependence of water activity 133

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

4.2 Measurement Techniques . . . . . . . . . . . . . . . . . . . . . 139

4.2.1 Differential Scanning Calorimetry (DSC) . . . . . . . . 139

4.2.2 Water activity measurements . . . . . . . . . . . . . . . 141

4.2.3 Electrodynamic Balance (EDB) measurements . . . . . 142

4.2.4 Total pressure measurements . . . . . . . . . . . . . . . 143

4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

4.3.1 1,4-Butanediol . . . . . . . . . . . . . . . . . . . . . . . 146

4.3.2 Methoxyacetic acid . . . . . . . . . . . . . . . . . . . . . 147

4.3.3 2-(2-Ethoxyethoxy)ethanol . . . . . . . . . . . . . . . . 148

4.3.4 M5 (multicomponent dicarboxylic acid) mixture . . . . 149

4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

4.4.1 Measurement techniques: scope and limitations . . . . . 155

4.4.2 Hydrogen bonding in aqueous solutions . . . . . . . . . 157

4.4.3 Atmospheric Implications . . . . . . . . . . . . . . . . . 159

4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

5 Conclusions 177

List of Figures 179

List of Tables 190

Bibliography 197

Acknowledgements 233

Curriculum Vitae 235

Abstract

Submicrometer-sized aerosol particles are typically mixtures of organic and in-

organic substances originating from natural and anthropogenic sources. While

the prevalent inorganic aerosol constituents are relatively small in number, the

organic fraction is highly complex, containing hundreds of compounds with

a large fraction still unidentified. The organic aerosol fraction is expected to

be present in liquid or amorphous state since a large number of organic com-

pounds depresses the temperature at which crystalline solid formation takes

place. The properties of tropospheric aerosols in terms of their hygroscopic-

ity, phase transitions and light scattering are of great interest in view of their

cloud forming and climatic characteristics.

Semi-volatile organic and inorganic aerosol species partition between the gas

and aerosol particle phases to maintain thermodynamic equilibrium. The

gas-particle partitioning of semi-volatile organic species, water content and

the phase state of the particles can be calculated when the vapour pressures

and the activities of the involved species are known. To study the hygro-

scopicity and phase equilibria of mixed aerosol particles we use the Aerosol

Inorganic-Organic Mixtures Functional groups Activity Coefficients (AIOM-

FAC) developed by Zuend et al. (2008, 2011). AIOMFAC is a group contribu-

tion model used for computing the activity coefficients of inorganic, organic,

and organic-inorganic interactions in aqueous solutions over a wide composi-

tion range. Activity coefficients of aqueous organic solutions may exhibit a

considerable temperature dependence that has to be explicitly parameterised

in thermodynamic models in order to achieve accurate predictions at tem-

peratures other than room temperature. However, most water activity data

of aqueous organic solutions has been acquired at room or elevated temper-

atures. If temperature dependence of the activity coefficients is neglected,

errors on the order of 10-15% in aw at the homogenous freezing temperature

ix

x Abstract

may be expected which in turn may induce large uncertainties in estimating

the direct and the indirect effects of aerosols.

This thesis develops a new improved parameterisation of the temperature

dependence of activity coefficients at low temperatures. With the aim to

describe a wide variety of organic mixtures and aqueous organic solutions

of atmospheric importance we focused on organic compounds containing the

functionalities typically found in tropospheric aerosols such as alcohol/polyol,

carboxylic acids, ketones, ethers, esters, aromatic rings and aldehydes. Reli-

able parameterisation of the temperature dependence and estimation of group

interaction parameters requires a comprehensive and broad distribution of ex-

perimental database covering a wide variety of mixtures with compounds con-

sisting of the targeted functional groups. Different thermodynamic data types

such as vapour-liquid equilibria (VLE), liquid-liquid equilibria (LLE), solid-

liquid equilibria (SLE), and water activity (aw) measurements are needed to

cover a wide temperature range. To assess the performance of AIOMFAC and

to establish parameters for a new improved AIOMFAC version an extensive

literature search was therefore essential.

Since there were apparent gaps in the database compiled from the literature,

especially in the low temperature range, for which data was missing or of

insufficient quality, own measurements were performed for selected aqueous

organic systems. For performing aw measurements over a wide composition

range while focusing on low temperature, we used different measurement tech-

niques such as differential scanning calorimetry (DSC) and electrodynamic

balance (EDB) measurements to obtain aw data at low temperatures. The

direct aw measurements around room temperature were obtained by a dew-

point water activity meter. To complement these measurement techniques we

developed a setup to measure total gas phase pressure over solutions at low

temperatures for mixtures with low vapour pressures of organics. Measure-

ments were conducted over the concentration range of 10-90 wt% and temper-

ature range of 190 K to 313 K using these different measurement techniques.

The measured aw data obtained through different measurement techniques

are consistent with each other and show diverse temperature dependence at

low temperatures. The aqueous organic systems with 1,4-butanediol and 2-

methoxyacetic acid as the organic component showed a moderate decrease in

xi

aw with decreasing temperature. The aqueous M5 system (a multicomponent

system containing five different dicarboxylic acids) containing five different

dicarboxylic acids as the organic component showed almost no temperature

dependence of aw for T > 285 K and a strong increase of aw at lower tem-

peratures for high solution concentrations (> 75 wt%). For aqueous solutions

of 2-(2-ethoxyethoxy)ethanol a decrease in aw with decreasing temperature

was observed for temperatures from 290 K to 265 K. The temperature de-

pendence was reversed at higher concentrations of 2-(2-ethoxyethoxy)ethanol

(>70 wt%) and lower temperatures (T < 265 K) showing a strong increase

of aw with decreasing temperature. Water activity data obtained from own

measurements are used in the temperature dependence parametrisation of

AIOMFAC model.

The AIOMFAC model with the implementation of the new improved tem-

perature dependence parameterisation, shows an overall good agreement with

most of the experimental datasets and enables the calculation of activity coef-

ficients of a wide variety different aqueous/water-free organic solutions. Due

to lack of data for a wider temperature and concentration range or due to

inaccuracy in the datasets, some mixtures may show deviations. Such inter-

actions might be readjusted in future provided new reliable measurements are

available. AIOMFAC can be used for studying the temperature dependence in

wide variety organic mixtures, compute phase separations, and ice nucleation

studies. Since the present thesis only concentrates on aqueous organic mix-

tures, one of the further tasks is to develop the AIOMFAC model to study the

temperature dependence at low temperatures also in case of aqueous inorganic

solutions and organic-inorganic solutions.

Zusammenfassung

Mikrometergrosse Aerosol Teilchen bestehen typischerweise aus Mischungen

von organischen und anorganischen Substanzen, die aus naturlichen und an-

thropogenen Quellen stammen. Wahrend die Anzahl der verschiedenen anor-

ganischen Aerosolbestandteile relativ klein ist, bleibt der organische Anteil

sehr komplex mit mehreren hundert verschiedenen Substanzen und einem

noch nicht identifizierten Anteil. Es wird davon ausgegangen, dass der or-

ganische Anteil in flussiger oder amorpher Form vorliegt, da eine grosse An-

zahl der organischen Substanzen die Kristallisationstemperatur herabsenkt.

Die Eigenschaften der tropospharischen Aerosole betreffend Hygroskopizitat,

Phasenumwandlungen und Lichtstreuung sind von grossem Interesse im Hin-

blick auf die Wolkenbildung und klimatischen Auswirkungen.

Semi-volatile organische und anorganische Substanzen der Aerosolteilchen

teilen sich auf die Gasphase und die Partikelphase auf, um ein thermody-

namisches Gleichgewicht herzustellen. Die Gas-Partikel Aufteilung von semi-

volatilen organischen Substanzen, der Wassergehalt und der Phasenzustand

der Partikel kann berechnet werden, wenn der Dampfdruck und die Ak-

tivitaten der involvierten Substanzen bekannt sind. Um die Hygroskopizitat

und die Phasenumwandlungen von gemischten Aerosol Partikeln zu unter-

suchen, wird in dieser Arbeit das Aerosol Inorganic-Organic Mixtures Func-

tional groups Activity Coefficients (AIOMFAC) Modell verwendet, welches

von (Zuend et al., 2008, 2011) entwickelt wurde. AIOMFAC ist ein Modell,

welches auf Beitragen der funktionalen Gruppen der Molekule basiert und

verwendet wird um die Aktivitatskoeffizienten von anorganischen, organis-

chen und organisch-anorganischen Wechselwirkungen in wassrigen Losugen

uber einen grossen Konzentrationsbereich zu berechnen.

Aktivitatskoeffizienten in wassrigen organischen Losungen konnen starke

Temperaturabhangigkeiten aufweisen, welche explizit in thermodynamischen

xiii

xiv Zusammenfassung

Modellen parametrisiert sein mussen damit auch bei Nicht-Raumtemperatur

exakte Berechnungen moglich sind. Jedoch fanden aber die meisten

Wasseraktivitatsmessungen bei Raumtemperatur statt. Wenn die Tem-

peraturabhangigkeit der Aktivitatskoeffizienten vernachlassigt wird, konnen

sich Fehler in aw in der Grossenordnung von 10-15% bei der homogenen

Gefriertemperatur erwartet werden, was wiederum grosse Unsicherheiten bei

der Einschatzung der direkten und indirekten Aerosoleffekte auf das Klima

zur Folge hat.

In dieser Arbeit haben wir eine verbesserte Parameterisierung der Tem-

peraturabhangigkeit der Aktivitatskoeffizienten bei niedrigen Temperaturen

entwickelt, mit dem Ziel, die grosse Vielfalt von organischen Mischungen

und wassrigen Losungen, welche von Interesse fur die Atmosphare sind,

zu beschreiben. Deshalb liegt der Fokus hier auf organischen Substanzen

welche funktionale Gruppen besitzen, die typischerweise in tropospharischen

Aerosolen gefunden werden, wie Alkohole/Polyole, Carbonsauren, Ketone,

Ethern, Estern, Aromatische Ringe und Aldehyde. Die verlassliche Param-

eterisierung der Temperaturabhangigkeit und die Abschatzung der Wechsel-

wirkungsparameter der verschiedenen funktionalen Gruppen setzt eine um-

fassende und breite Abdeckung der experimentellen Datensatze voraus, welche

die grosse Vielfalt der Mischungen mit den gewunschten funktionalen Grup-

pen beinhalten. Um die Korrektheit von AIOMFAC zu beurteilen und neue

Parameter fur eine verbesserte Version von AIOMFAC einzufuhren, war de-

shalb eine detaillierte Literaturrecherche notwendig.

Um Lucken in der Datenbank, speziell bei tiefen Temperaturen wo Daten

nicht verfugbar oder von schlechter Qualitat sind, zu uberbrucken, wurden im

Rahmen dieser Arbeit eigene Messungen fur ausgewahlte wassrige organische

Systeme durchgefuhrt. Um die Wasseraktivitat uber einen grossen Konzen-

trationsbereich bei niedrigen Temperaturen zu messen, wurden verschiedene

Techniken wie die Differential-Scanning-Kalorimetrie (DSC) oder die elektro-

dynamische Teilchenfalle (EDB) angewandt. Bei Temperaturen im Bereich

der Raumtemperatur wurde die Wasseraktivitat mit einem Taupunktspiegel

gemessen. Erganzend zu diesen Techniken wurde ein Versuchsaufbau entwick-

elt, bei dem der Gesamtdruck uber einer Losung bestehend aus einer wassrigen

Mischung mit organischen Substanzen mit niedrigem Dampfdruck gemessen

xv

wird. Die Messungen wurden uber einen Konzentrationsbereich von 10-90

wt% und einen Temperaturbereich von 190 K bis 313 K mit den genannten

Techniken durchgefuhrt. Die mit den verschiedenen Techniken gemessenen

aw Werte sind miteinander konsistent und zeigen Temperaturabhangigkeiten

bei tiefen Temperaturen auf. Die wassrigen organischen Systeme mit 1,4-

butandiol und Methoxyessigsaure (2-methoxyacetic acid) als organische Kom-

ponente zeigten eine moderate Abnahme in aw mit abnehmender Temper-

atur. Die wassrigen M5 Systeme, welche funf verschiedene Dicarboxylsauren

beinhalten, wiesen eine sehr geringe Temperaturabhangigkeit fur aw fur T >

285 K und eine starke Zunahme von aw fur hochkonzentrierte (> 75 wt%)

Losungen auf. Bei wassrigen Losungen mit Diethylenglycolmonoethylether

[2-(2-ethoxyethoxy)ethanol] konnte eine Abnahme von aw mit abnehmender

Temperatur von 290 K bis 265 K beobachtet werden, wahrend bei hoheren

Konzentrationen (> 70 wt%) und niedrigeren Temperaturen (T < 265 K) eine

Zunahme in aw mit abnahmender Temperatur gemessen wurde.

Das AIOMFAC Modell mit der neu implementierten Parametrisierung der

Temperaturabhangigkeit zeigt im Allgemeinen eine gute Ubereinstimmung

mit den meisten Datensatzen und ermoglich die Berechnung von Ak-

tivitatskoeffizienten uber eine Vielfalt von verschiedenen wassrigen und

wasserfreien organischen Losungen. Aufgrund von fehlenden Daten uber einen

grosseren Temperatur- und Konzentrationsbereich oder aufgrund von Unge-

nauigkeiten in den Datensatzen konnen bei einigen Mischungen Abweichun-

gen auftreten. Diese fehlenden Wechselwirkungen konnen in einem weiteren

Schritt zu Entwicklung von AIOMFAC angepasst werden, sofern neue und

verlassliche Datensatze erhaltlich sind. AIOMFAC kann fur die Untersuchung

der Temperaturabhangigkeit einer grossen Menge von organischen Mischun-

gen, fur die Berechnung von Phasenseparationen und fur Eisnukleationsstu-

dien angewendet werden. Die vorliegende Arbeit fokussiert auf wassrigen

organischen Losungen, wahrend die Weiterentwicklung von AIOMFAC zur

Anwendbarkeit fur die Temperaturabhangigkeit von wassrigen inorganischen

und organisch-inorganischen Losungen bei tiefen Temperaturen in nachfolgen-

den Arbeiten behandelt werden konnen.

Chapter 1

Introduction

1.1 Motivation

Atmospheric aerosols are a complex mixture of organic and inorganic com-

ponents which significantly influence the Earth’s climate. Knowledge about

the composition and physical state of aerosols is essential since they play sig-

nificant roles in atmospheric processes such as heterogeneous and multiphase

chemistry in the troposphere and stratosphere, cloud formation, scattering

and absorption of visible light and infrared radiation. Changes in aerosol

loading and properties affect the Earth’s climate by altering the radiative

balance by means of direct and indirect mechanisms. The direct mechanism

involves the absorption and scattering of solar radiation by aerosol particles

which modifies the radiative balance of the atmosphere. The Earth’s mean

temperature and climate is controlled by the incoming short wave radiation

and the outgoing long wave emission of infrared radiation from the top edge

of the atmosphere. The indirect mechanism refer to the role of aerosols as

cloud condensation nuclei (CCN) or ice nuclei (IN) for cloud formation and

their influence on cloud properties i.e. cloud droplet size and number density.

Alteration of the CCN and IN concentration affects the drop size distribution,

cloud size, formation and coverage over temporal and spatial scale (Jacobson

et al., 2000; Kanakidou et al., 2005).

For accurate and reliable predictions of climate effects and implications, ad-

equate knowledge about atmospheric processes such as the linkage between

aerosol particles and cloud properties is required. Since atmospheric aerosols

1

2 Chapter 1. Introduction

act as nuclei onto which cloud droplet formation takes place, the radiative

effects of clouds can only be assessed provided that the relationship between

the physicochemical properties of atmospheric aerosols and their ability to

act as CCN is established. Organic and inorganic species present in aged

tropospheric aerosols show molecular interactions affecting the water uptake

and release (hygroscopicity), and may lead to liquid-liquid phase separation,

alteration in the efflorescence and deliquescence relative humidity of inor-

ganic species and gas-particle partitioning of semivolatile compounds (Choi

and Chan, 2002; Marcolli et al., 2004; Pankow, 2003; Marcolli and Krieger,

2006; Martin et al., 2008; Zuend et al., 2008, 2010; Ciobanu et al., 2009; Song

et al., 2012).

The prevalent inorganic aerosol constituents are relatively small in number

and are relatively well characterized in comparison to the organic fraction

which is highly complex and contains hundreds of compounds with a large

fraction still unidentified (Rogge et al., 1993; Jacobson et al., 2000; Hallquist

et al., 2009; Fuzzi et al., 2006; Goldstein and Galbally, 2007). The organic

aerosol fraction is expected to be present in the liquid state and to retain

water even at low relative humidity since the large fraction of organic species

depresses the temperature at which solids form (Marcolli et al., 2004). The

inorganic salts dominate the water uptake at high relative humidity (Ansari

and Pandis, 1999; Colberg et al., 2003). Experimental studies show that inter-

actions between the inorganic ions and organic species in aerosol particles may

induce a liquid-liquid phase separation during humidity cycles (Marcolli and

Krieger, 2006; Zuend et al., 2010; Song et al., 2012; Ciobanu et al., 2009). Ac-

curate description of the physical state of aerosol phases is important for the

estimation of the gas-particle partitioning of water and semivolatile substances

(Zuend et al., 2010; Zuend and Seinfeld, 2012). Phase equilibrium calculations

based on activity coefficient models allow to determine whether the aerosol

phase is a liquid, solid or mixture of solid and liquid phases. Partitioning of

semi-volatile species between gas and condensed phases, water content and

the physical state of the particles can be calculated when the vapour pressure

and activities of the involved species are known. To study the hygroscopicity

and phase equilibria of mixed aerosol particles we use the Aerosol Inorganic-

Organic Mixtures Functional groups Activity Coefficients (AIOMFAC) group

1.1. Motivation 3

contribution model developed by (Zuend et al., 2008, 2011). AIOMFAC is

based on the group contribution model LIFAC by (Yan et al., 1999) and in-

cludes the semi-empirical group contribution model UNIFAC (Fredenslund

et al., 1975; Hansen et al., 1991) for the description of organic mixtures and

aqueous organic solutions. The group contribution concept has the advantage

of being able to represent thousands of organic compounds using a limited

number of functional groups. The AIOMFAC model is able to calculate activ-

ity coefficients covering inorganic, organic and organic-inorganic interactions

in aqueous solutions over a wide composition range. The original UNIFAC

(Fredenslund et al., 1975) was developed for VLE (vapour-liquid equilibria)

calculations within a limited temperature range from 275 K to 400 K which

may result in poor predictions of real phase behaviour for mixtures at tem-

peratures lower than 290 K (Lohmann et al., 2001). To overcome this defi-

ciency, a modified UNIFAC model (UNIFAC Dortmund) has been developed

(Gmehling et al., 1998, 2002; Jakob et al., 2006) which includes temperature

dependent parameters. However, they are not optimized for the low temper-

atures present in the atmosphere. Moreover, water activity predictions for

atmospherically relevant organic solutions show poor performance when the

organic fraction consists of molecules typically carrying several strongly polar

functional groups (Saxena and Hildemann, 1997).

For atmospheric applications accurate physicochemical description of mix-

tures of organic and inorganic compounds at atmospherically relevant low

temperature is required. A number of studies have shown that at tropospheric

low temperature/or lower water content complex organic aerosols may form

highly viscous liquids (Marcolli et al., 2004) and may undergo glass transi-

tion to an amorphous state (Zobrist et al., 2008, 2011; Virtanen et al., 2010;

Cappa and Wilson, 2011; Vaden et al., 2011; Poschl, 2011; Koop et al., 2011).

Glasses are disordered materials that lack the periodicity of crystals but be-

have mechanically like solids (Debenedetti and Stillinger, 2001). This may

impede gas-particle mass transfer, water uptake, aerosol growth and evap-

oration behaviour, multiphase chemistry and may affect the ice nucleation

efficiency of aerosol particles (Zobrist et al., 2008; Koop et al., 2011; Knopf

and Rigg, 2011; Baustian et al., 2012).

In the upper troposphere heterogeneous ice nucleation and subsequent cirrus


cloud formation take place on aerosols which grow into ice crystals by dis-

sipating supersaturated water vapour (Knopf and Rigg, 2011; Poschl, 2011).

Homogeneous ice nucleation in supercooled aqueous solutions is independent

of the nature of the solute but depends on water activity (aw) (Koop et al.,

2000). The aw of a solution is defined as the ratio of the solution’s water

vapour pressure to the vapour pressure of pure water at the same tempera-

ture and pressure conditions (Koop et al., 2000; Koop, 2004; Knopf and Rigg,

2011). If aqueous aerosol particles are in equilibrium with the surrounding

gas phase, water activity and ambient relative humidity over a liquid solu-

tion correspond. Thus knowing the aw of solutions at low temperature is

a crucial parameter for the prediction of homogeneous ice nucleation. The

uncertainty in predicted homogeneous ice nucleation temperatures is stated

as ± 0.025 for higher temperatures and ± 0.05 for lower values (Koop et al.,

2000; Koop, 2004; Knopf and Rigg, 2011). These uncertainties may result

into significantly lower or higher values of homogeneous nucleation rate coef-

ficients (Jhom) (Knopf and Rigg, 2011). A change of aw by 0.025 may result

in a change of Jhom by 6 orders of magnitude which may significantly affect

predictions of the onset of ice crystal formation in cloud microphysical models.

For e.g. a difference of 3 orders of magnitude in Jhom could delay or acceler-

ate homogeneous ice nucleation by about an hour in a simulation (Knopf and

Rigg, 2011).

In most cases, aw measurements for the metastable range are not directly

available, although aw could be predicted using thermodynamic models such

as Pitzer ion-interaction models for aqueous electrolyte solutions (Pitzer, 1991;

Clegg et al., 1998; Zuend et al., 2008, 2011). For the mixtures of organics and

water UNIQUAC model (Abrams and Prausnitz, 1975) or its group contribu-

tion version UNIFAC (Fredenslund et al., 1975; Hansen et al., 1991) are used.

However, due to the experimental data scarcity at low temperature only a

limited number of models are available to predict aw at freezing point. In

absence of low temperature, (Koop et al., 2000) suggests that aw at the freez-

ing point is obtained from the corresponding aw determined at the melting

point of the solution with the assumption that aw does not change signifi-

cantly within this temperature range. This approach is valid for a variety of

aqueous inorganic solutions but may lead to significant errors in predictions

1.1. Motivation 5

of the homogeneous freezing temperatures for aqueous organic solutions. For

example, organic solutions composed of ethylene glycol and levoglucosan un-

dergo significant changes in aw with temperature (Zobrist et al., 2008; Knopf

and Lopez, 2009). Activity coefficients of organic compounds in solutions

may exhibit a considerable temperature dependence that has to be explicitly

parameterised by models in order to achieve accurate predictions at tempera-

tures other than room temperature. Neglecting the temperature dependence

of activity coefficients may lead to errors on the order of 10-15 % for water

activity estimations at the homogeneous freezing temperature (Zobrist et al.,

2008) thus indicating the need for an improved temperature dependence pa-

rameterisation. However, the modified UNIFAC model within AIOMFAC

shares the simple temperature dependence formulation as the standard UNI-

FAC (Zuend et al., 2008, 2011).

Considering the mentioned gaps in knowledge on several aspects related to

aerosol thermodynamics, this PhD thesis aims to improve the temperature

dependence parameterisation at low temperatures of AIOMFAC for aqueous

organic mixtures containing the functionalities typically found in tropospheric

aerosol components such as alcohol/polyol, carboxylic acids, ketones, ethers,

esters, aromatic rings and aldehydes. Reliable estimation of group interac-

tion parameters and correctly parametrising the temperature dependence re-

quires a comprehensive and broad distribution of experimental data covering a

wide variety of mixtures with compounds consisting of the targeted functional

groups. Different thermodynamic data types such as vapour-liquid equilibria

(VLE), liquid-liquid equilibria (LLE), solid-liquid equilibria (SLE), and water

activity (aw) measurements are needed to cover a wide temperature range.

To assess the performance of AIOMFAC and to establish parameters for a

new improved AIOMFAC version an extensive literature search is therefore

essential.

Since there were gaps in the database compiled from the literature, especially

in the low temperature range, for which data were missing or of insufficient

quality, own measurements were performed for selected aqueous organic sys-

tems. For performing water activity measurements over a wide composition

range while focusing on low temperature, we use different measurement tech-

niques such as differential scanning calorimetry (DSC) and electrodynamic


balance (EDB) measurements to obtain aw data at low temperatures while

direct aw measurements around room temperature were obtained by Aqualab

dewpoint water activity meter (model 3TE,Decagon devices, USA). To com-

plement these measurement techniques we developed a setup to measure total

gas phase pressure of solutions at low temperatures for mixtures with low or-

ganic vapour pressures.

This thesis is structured into five main chapters. The remaining part of the

current chapter gives an introduction to the general context and a brief charac-

terization of atmospheric aerosols. Chapter 2 introduces the thermodynamic

background and a brief description of intermolecular interactions. Chapter 3

describes the model framework, parameterisation and database implemented

and model results. Chapter 4 discusses the measurement techniques used to

perform water activity measurements at low temperature over wide concen-

tration range and also provides new data for selected aqueous organic systems.

Chapter 5 finally summaries the conclusions and outlook of this PhD thesis.

1.2 Vertical structure of Earth’s atmosphere

The atmosphere is a thin layer of gases that envelopes the Earth’s surface,

retained by the gravitational force. Figure 1.1 shows the temperature and

pressure of the atmosphere as a function of altitude. As the altitude increases

the air pressure decreases exponentially since there are fewer numbers of gas

molecules and atoms exerting pressure.

The Earth’s atmosphere is made up of several different layers, and in broad

terms each layer differs in chemical composition and vertical temperature pro-

file. The troposphere is the lowest layer of the Earth’s atmosphere. Up to

85 % of the atmospheric mass is contained in the troposphere where most

of the daily weather phenomena (e.g. clouds, precipitation, wind, etc.) and

most of Earth/atmosphere interactions occur (e.g. hydrological cycle). In the

troposphere the temperature decreases with about 6.5 K km−1 as altitude in-

creases due to adiabatic expansion and reaches a minimum at the tropopause.

The reason for this progressive decrease is the increasing distance from the

sun-warmed Earth (Seinfeld and Pandis, 1998). Depending on the season and

1.2. Vertical structure of Earth’s atmosphere 7

Figure 1.1: Vertical temperature structure of the atmosphere extending from the

surface of the Earth to approximately 110-km altitude as given in the U.S. Standard

Atmosphere, 1976. Source: Brasseur et al. (1999).

latitude the height of the tropopause reaches values up to 18 km in the trop-

ics and up to approximately 8 km in the polar regions. The lowest region of

the troposphere consists of the planetary boundary layer where most of the

primary trace gases and particle emissions enter the atmosphere. The tropo-

sphere is also the layer which contains most of the aerosols. The stratosphere

extends from the tropopause to the stratopause (∼ 45 to 55 km altitude),

here temperature increases with altitude which is due to the photolysis of

ozone into molecular and atomic oxygen, which recombine again to regener-

ate ozone. The ambient temperature increases when the rapid molecular and

atomic products of these reactions thermalise via collisions. Tropics where

the solar irradiance is the highest are the main source region of stratospheric

ozone. The air masses are transported from there to higher latitudes by

the Brewer-Dobson circulation. In the mesosphere, which extends from the


stratopause to the mesopause (∼ 80 to 90 km altitude) temperature decreases

with altitude. The thermosphere is located above the mesopause, and is char-

acterized by an increase in the temperature with height due to the absorption

of short wavelength radiation by N2 and O2 (Seinfeld and Pandis, 1998). The

ionosphere is a region of the upper mesosphere and lower thermosphere where

ions are produced by photoionization. The exosphere is the outermost region

of the atmosphere where gas molecules with sufficient energy can escape from

the Earth’s gravitational attraction.

1.2.1 Composition of the Earth’s atmosphere

It is believed that when the Earth was formed, He and H2 were the domi-

nant gases, but were mostly lost to space. The chemical composition of the

Earth’s atmosphere has undergone several changes during its evolution pro-

cess which lead to the cooling of the surface, formation of the Earth’s inner

core and generation of the magnetic field, condensation of water vapour and

other gases, ozone layer formation, photosynthesis by plants. The present at-

mosphere mainly consists of nitrogen (N2), oxygen (O2), water vapour (H2O),

argon (Ar) and carbon dioxide (CO2). Water vapour concentration is highly

variable, reaching concentrations as high as 3 % near the surface (Seinfeld

and Pandis, 1998) and is averaged over the full atmosphere of about 0.25

%. Almost all water vapour and condensed liquid water is confined to the

lower atmosphere and their abundance is controlled by evaporation and pre-

cipitation processes. The remaining gases which comprise less than 1 % of

the atmosphere are so-called trace gases; they include gases such as Ar, O3,

NO2, N2O, CO, CH4 and CO2. These trace gases play an important role in

the Earth’s radiative balance, acting as greenhouse gases and as reactants in

oxidation processes and in ozone chemistry, and also participate in the produc-

tion of condensable material for the formation of secondary aerosol particles.

Depending on chemical reactivity, meteorological conditions and atmospheric

life time, trace gases can exhibit an enormous range of spatial and temporal

variability. Inert gases such as the chlorofluorocarbon (CFC) which rise up to

the stratosphere and higher regions get converted into reactive species, formed

by the breakdown of their molecular bonds by the intense solar radiation at

1.3. Aerosols 9

high altitudes. The stratospheric ozone sometimes also referred as “good

ozone” plays a beneficial role by absorbing most of the harmful ultraviolet

radiation and thus protecting life on Earth.

1.3 Aerosols

Atmospheric aerosols are suspensions of solid and/or liquid particles in a gas.

Aerosols are ubiquitous in air and are observable as dust, smoke and haze.

Aerosol particles directly released into the atmosphere through natural and

anthropogenic processes such as volcanic emissions, dust storm, desert dust,

pollen released by plants, biomass burning, industrial processes and fuel emis-

sion are known as primary aerosols while those produced in the atmosphere by

gas-to-particle conversion processes such as nucleation, condensation and het-

erogeneous and multiphase chemical reactions are called secondary aerosols

(Poschl, 2005; Hallquist et al., 2009). Atmospheric aerosols cover a wide range

of particle types having different compositions, size distributions and optical

properties.

1.3.1 Sources

Natural and anthropogenic processes contribute to the concentration of

aerosols in the atmosphere. The composition, shape, mass and number den-

sity of the aerosols vary significantly for urban and rural/remote, coastal and

continental, desert and forest regions. Oceans are one of the major sources

of atmospheric aerosols (∼ 1000-5000 Tg per year) (Wallace et al., 2006).

Volcanic eruptions which transport large amount of sulphur dioxide into the

stratosphere are of atmospheric significance. In the stratosphere, the sulphur

dioxide gets converted into sulphuric acid aerosols which causes a net radiative

cooling effect. A classic example for such an event is the Pinatubo eruption in

1991 which significantly affect the global climate. Table 1.2 lists estimates (in

Tg per year) for the year 2000, the magnitudes of the principle sources of (a)

direct emission of aerosol particles into the atmosphere and (b) in-situ sources


of secondary aerosol formation in both hemispheres. Aerosol formation in the

atmosphere through gas-to-particle conversion takes place by condensation of

semivolatile and low volatile species onto existing particles, thereby increasing

the mass (but not the number) of particles. On the other hand, new parti-

cle formation by nucleation and condensation of gaseous precursors, increases

the particle numbers substantially. The major families of chemical species

involved in gas-to-particle conversion are: sulphates, nitrates, organic com-

pounds (Wallace et al., 2006; Seinfeld and Pandis, 1998).

Figure 1.3 shows the seasonal and geographical changes of anthropogenic and

natural aerosol loading, illustrating the global fields of aerosol optical depth

(AOD), separated into fine mode (red) and coarse mode (green) components

observed by MODIS/Terra instrument(550nm) on 13th April and 22nd Au-

gust 2001. The fine mode AOD mainly consists of pollution and biomass

burning aerosols while the coarse mode consists of dust and sea salt aerosols.

Transport of dust and pollution from Asia to North America and the cross-

Atlantic transport of dust from Africa to central America can be seen on the

13th April 2001. On the 22nd August, large smoke plumes from South Amer-

ica to the southern Atlantic and from southern Africa to the Indian Ocean

are evident. The implications of such large-scale transport on the air qual-

ity of the receptor region depends on the perturbation of the surface aerosol

concentration (Chin et al., 2007).

1.3.2 Sinks

Aerosols undergo various physical and chemical interactions and transforma-

tion (atmospheric ageing) which modifies the particle size, structure and their

composition. Coagulation is one of the processes by which small particles are

converted into larger particles. Since the mobility of a particle rapidly de-

creases as its particle size increases, coagulation is essentially confined to par-

ticles less ∼ 0.2 µm in diameter (Wallace et al., 2006). Although coagulation

does not remove particles from the atmosphere, it modifies the size spectra of

aerosols and shifts small particles into size ranges where they can be removed

from the atmosphere by other mechanisms such as wet and dry deposition.

In the troposphere, precipitation and dry deposition on surfaces are the most

1.3. Aerosols 11

Figure 1.2: Estimates (in Tg per year) for the year 2000 of (a) direct particle

emissions into the atmosphere and (b) in-situ production. aSizes refer to diame-

ters. [Adapted from Intergovernmental Panel on Climate Change, 2001, Cambridge

University Press, pp.297 and 301, 2001.] Source: Wallace et al. (2006)


Figure 1.3: shows composites of MODIS/Terra, Aerosol optical Depth (AOD) by

the MODIS/Terra (550 nm) for the April 13 (top row) and August 22 (bottom row),

2001. Red colour indicates fine mode aerosols and green colour coarse mode aerosols.

On April 13, 2001, heavy dust and pollution is transported from Asia to the Pacific

and dust is transported from Africa to Atlantic. On August 22 large smoke plumes

from South America and South Africa are evident. Adapted from Chin et al. (2007);

(original figure from Yoram Kaufman and Reto Stockli).

1.3. Aerosols 13

effective sink mechanisms of aerosols. Depending on the aerosol properties

and the atmospheric conditions, the residence time of aerosol particles in the

troposphere range from hours to weeks (Seinfeld and Pandis, 1998; Poschl,

2005).

1.3.3 Size distribution

Particle size of atmospheric aerosols varies from a few tens of nanometers

(nm) to several hundreds of micrometers (µm). Depending on particle di-

ameter they are classified as fine mode (≤2.5 µm) and coarse mode (≥ 2.5

µm). The fine mode particles are subdivided into nuclei (Aitken) mode and

the accumulation mode (Seinfeld and Pandis, 1998). There is an additional

particle size mode, known as the ultra fine mode for particles below 0.01 µm

and originates from nucleation events of low volatility vapour (Whitby and

Cantrell, 1976). The fine and coarse mode aerosol particles differ in chemi-

cal composition, optical properties, origin, transport mechanism, and removal

mechanisms from the atmosphere and also differ significantly in their ability

to enter different levels of the respiratory tract of humans. Hence classifica-

tion of aerosols between fine and coarse mode particles is essential for studying

physicochemical properties, atmospheric implications and health effects.

Figure 1.4 shows the typical distribution of surface area of atmospheric

aerosols. Fine aerosols are further classified into the nucleation mode ranging

from about 0.005 to 0.1 µm diameter and the accumulation mode ranging

from 0.1 to 2.5 µm. Particles in the nucleation mode are formed by gas-to-

particle conversion and nucleation of fresh particles and are lost mainly due

to coagulation with larger particles (Seinfeld and Pandis, 1998). Due to their

small size, nucleation mode particles rarely account for more than a few per-

cent of the total mass of airborne particles. Accumulation mode particles are

formed by the coagulation of nucleation mode particles as well as from con-

densation of vapour onto existing particles. The coarse mode aerosol particles

are usually generated by natural sources (e.g. sea salt and mineral dust) and

man-made processes (e.g. mining). Coarse mode aerosols have a relatively

large sedimentation velocity and thus a short life time ranging from hours to

a few days. Nucleation mode and coarse mode particles are removed more


Figure 1.4: Schematic representation of distribution of particle surface area of

atmospheric aerosols. Principle modes, formation and conversion processes, and

removal mechanisms are indicated. Source: Whitby and Cantrell (1976).

1.3. Aerosols 15

efficiently from the atmosphere in comparison to accumulation mode parti-

cles, and hence, the accumulation mode particles tend to have a considerably

longer residence times than those in either the nuclei or coarse mode.

1.3.4 Chemical composition

Aerosol particles depending on their natural or anthropogenic origin have

varying chemical compositions. The overall composition and the physical

structure of aerosol particles undergo changes due to physicochemical in-

teractions between different components over a period of time, also known

as (chemical) aging process. Regional and seasonal variations also cause a

change in the typical aerosol composition due to the influence of the differ-

ent biogenic and anthropogenic sources, transport and removal mechanisms

in the atmosphere. A significant fraction of the tropospheric aerosol con-

stituents are anthropogenic in origin (e.g. biomass burning, fuel combustion,

industrial processes, sulphates and nitrates). Tropospheric aerosols consists

of highly water soluble inorganic salts, inorganic acids, insoluble mineral dust

and carbonaceous material which includes organic compounds ranging from

very soluble to insoluble in water, plus elemental carbon. Numerous indi-

vidual organic compounds present in the ambient aerosol samples have been

identified such as n-alkanes, dicarboxylic acids, polycyclic aromatic hydrocar-

bons (PAH) and some nitrogen-containing compounds (Rogge et al., 1993;

Pio et al., 2001; Tsapakis et al., 2002). Experimental studies also suggest

the presence of additional compounds such as organic sulphates (Saxena and

Hildemann, 1996; Blando et al., 1998; Fuzzi et al., 2001).

Primary organic aerosols (POA) are emitted directly into the atmosphere

while secondary organic aerosols (SOA) are formed as a result of transforma-

tion and condensation of organic precursors, i.e., gas-to-particle conversion

(Farina et al., 2010; Jathar et al., 2011; Lin et al., 2012). Biomass burning

and fossil fuel combustion are considered to be responsible for most POA mass

that is emitted into the atmosphere (Liousse et al., 1996; Seinfeld and Pan-

dis, 1998; Hallquist et al., 2009). The process leading to SOA formation is a

sequential process: gas emissions → gas phase chemistry ↔ gas-particle par-

titioning/nucleation↔ aerosol chemistry/cloud processing (Kanakidou et al.,


2005). In case of semivolatile organic species, gas-particle partitioning de-

scribes the equilibrium fractions in the gas phase and particle phases (one or

several liquid, semi-solid, or solid phases) at ambient conditions.

Organic aerosols form a significant fraction of the fine atmospheric aerosol

mass and are extensively studied by using climate models to determine their

global impact (Zhang et al., 2007; Robinson et al., 2007; Hallquist et al.,

2009; Pye and Seinfeld, 2010; Lin et al., 2012). Distinction between the im-

plications and properties of POA and SOA are under debate, recent studies

show that the volatility of emitted particles can change due to evaporation

and gas-phase oxidation of primary emissions which could subsequently pro-

vide an additional source of SOA (Robinson et al., 2007; Lin et al., 2012).

Despite the significance of organic aerosols in the environment, data sets to

constrain models are limited. However based on the available data it appears

that models tend to underestimate SOA concentrations in the boundary layer

(Johnson et al., 2006; Volkamer et al., 2006; Kleinman et al., 2008; Simpson

et al., 2007; Jimenez et al., 2009). Recently, instruments such as Aerosol

Mass Spectrometer (AMS), Particle-Into-Liquid Sampler (PILS) and tech-

niques such as radiocarbon isotope analysis provide insight into the sources,

composition and reactivity of organic aerosol typically unavailable from mass

measurements (Jathar et al., 2011; Zhang et al., 2005). The AMS results sug-

gest that organic aerosols are dominated by SOA from the oxidation products

of gas-phase organic precursors (Robinson et al., 2007; Zhang et al., 2007;

Jathar et al., 2011). This indicates the significant difficulties in simulating

organic aerosols in global models. Although a lot is being done towards un-

derstanding the SOA formation, targeted chamber and field experiments are

needed to allow evaluation and provide confidence in chemical mechanisms

used in regional and global models that treat both gas phase chemistry and

SOA formation.

1.4 Radiative Forcing

Radiative forcing is referred to as a measure of how the energy balance of the

Earth-atmosphere system is influenced when the factors that affect the cli-

1.4. Radiative Forcing 17

mate are altered (Forster et al., 2007). Fig 1.5 illustrates and summarizes the

direct and indirect effects of aerosols and aerosol-cloud interactions, respec-

tively. Aerosols can absorb and scatter short wave and long wave radiation,

thereby changing the Earth-atmosphere radiative balance. This is known as

the direct effect while the indirect effect involves the role of aerosols as cloud

condensation nuclei (CCN) or ice nuclei (IN) in cloud formation. The indi-

rect effect furthermore also includes the effects of increased aerosol particle

number concentration affecting cloud properties, such as mean droplet size

which is related to the numbers of CCN and IN. As seen in Figure 1.5 an

unperturbed cloud contains fewer but larger cloud droplets while a perturbed

cloud contains a greater number of smaller cloud droplets as both natural and

anthropogenic aerosols participate in cloud formation and increase the num-

ber of CCN. An increase in the CCN concentrations results in an increase of

cloud droplet concentration with smaller drop size radii which leads to more

reflective clouds. A decrease in the cloud drop effective radius may lower co-

alescence rates leading to a decrease in the precipitation and a longer cloud

Figure 1.5: Figure illustrates the direct and various indirect aerosol effects. The

aerosol particles are represented as small black dots; cloud droplets are represented

by the larger open circles. Straight lines represent the incident and reflected solar

radiation, and wavy lines represent long wave radiation. The vertical grey dashes

represent rainfall, and LWC refers to the liquid water content. [Source: IPCC AR4

Report Forster et al. (2007)]


life time and greater spatial extent, and is referred to as second indirect effect

(Albrecht, 1989) or the cloud lifetime effect. Hansen et al. (1997) identified a

so called semi-indirect effect: Aerosol solar absorption (e.g. by black carbon)

may reduce cloud cover and liquid water content by heating the cloud and

environment in which the cloud forms. These effects can alter the heat budget

and the hydrological cycle, including precipitation patterns, on a variety of

length and time scales (Ramanathan et al., 2001; Zhang et al., 2006).

Overall the current aerosol radiative forcing relative to preindustrial times is

estimated to be around -1 to -2 Wm−2 as opposed to a greenhouse gas forcing

of about +2.4 Wm−2 (Poschl, 2005). The values reflect the total forcing rel-

ative to the start of the industrial era (∼ 1750). Anthropogenic contributions

to aerosols (sulphate, organic carbon, nitrate and dust) together produce a

cooling effect, with a total direct radiative forcing of -0.5 (-0.9 to 0.1) Wm−2

and an indirect cloud albedo forcing of -0.7(-1.8 to -0.3) Wm−2 (Forster et al.,

2007). Fig 1.6 shows the radiative forcing contributions of some of the cli-

mate agents influenced by human activities. The increase of greenhouse gases

especially CO2 concentrations are responsible for the largest positive forcing

over this period. There is a net increase in the tropospheric ozone leading

to warming (positive forcing), while stratospheric ozone decreases have con-

tributed to cooling in the stratosphere (and warming in the troposphere). In

case of aerosols, some cause a positive forcing (black carbon) while others

cause a negative forcing (organic carbon, mineral dust). Best estimates indi-

cate that the indirect effects of aerosols on climate overall constitute a negative

radiative forcing contribution. Compared to the well-established effects from

greenhouse gases with longer atmospheric life time, there are still considerable

gaps in the understanding concerning the absorption and scattering and cloud

interactions of aerosol particles.

1.5. Aerosol thermodynamics 19

Figure 1.6: Summary of the principal components of the radiative forcing of climate

change. [Source: IPCC AR4 Report Forster et al. (2007)]

1.5 Aerosol thermodynamics

Ambient temperature and relative humidity have a strong influence on the

aerosol morphology and stable thermodynamic phases of particles. Tropo-

spheric aerosols are complex mixtures of organic substances and inorganic

salts such as ammonium sulfate and nitrate. Inorganic salt aerosols are hy-

groscopic in nature and exhibit deliquescence and efflorescence properties de-

pending on the change in the relative humidity (RH). The phase transfor-


mation from a solid particle to a saline droplet usually occurs spontaneously

when the RH in the surrounding air reaches a level, known as deliquescence

humidity, that is specific to the chemical composition of the aerosol particle

(Tang and Munkelwitz, 1994; Zardini, 2007). The reverse process that is re-

lease of water to the air to form solid crystal below the RH threshold value is

known efflorescence relative humidity.

Figure 1.7 illustrates a pure ammonium sulphate (NH4)2SO4 aerosol parti-

cle showing a distinict hystersis behaviour during hygroscopic cycles. With

the initial increase in the RH the particle retains its solid state (no particle

growth). At about 80 % RH the particle undergoes deliquescence (abruptly

changes to liquid state) and retains its liquid state and takes up water (par-

ticle growth) up to RH 92 % (and above). In the reverse part of the cycle

for decreasing RH, the particle shrinks in size but stays in liquid state even

below 80 % RH (metastable, supersaturated) and at around 37 % RH the

particle undergoes efflorescence (abruptly changes back into a solid). Thus,

the ambient relative humidity, supersaturation of liquid droplet and the par-

ticle history determines whether the particle is in a stable, metastable, solid

or liquid state (Zardini, 2007).

This hysteresis behavior of aerosol particles significantly influence the direct

aerosol effect (Krieger et al., 2012). Studies show that inorganic sulphate

aerosol hysteresis results in an uncertainty of 20 % of the aerosol optical

thickness, and 34 % of the radiative effect in sulphate direct climate forcing

(Wang et al., 2008) while the organic compounds influence the hygroscopic-

ity and direct effect potentially causing less cooling by the aerosols (Randles

et al., 2004).

Deliquescence behavior of particles composed of inorganic salts and water sol-

uble organic species such as ammonium sulphate (AS) and sodium chloride

(NaCl) mixed with dicarboxylic acids, polyol or levoglucosan have been inves-

tigated by a variety of experimental techniques (Choi and Chan, 2002; Brooks

et al., 2002; Marcolli and Krieger, 2006; Zardini et al., 2008; Song et al., 2012;

Krieger et al., 2012). These studies suggest that in such mixed systems, the

deliquescence relative humidity (DRH) of the inorganic component may re-

main almost constant or decrease with respect to organic depending on the

mixing ratio and the nature of the organic species. The efflorescence rela-


Figure 1.7: Hysteresis behavior of an aqueous ammonium sulphate particle at am-

bient temperature. Open circles represents measurements when RH is increasing,

whereas the closed circles are points with decreasing RH. Particle mass change is

represented by the ratio m/mo where m is the dry particle mass and mo is the par-

ticle mass at particular RH. The deliquescence relative humidity is about 80 %, the

efforescence point around 37 % RH in case of ammonium sulphate, but sometimes

the efflorescence may also occur at slightly higher or lower values (always below

DRH). Source: Tang and Munkelwitz (1994)

tive humidity may shift to lower relative humidity and even may be totally

suppressed in the presence of certain types of organics. This behavior may

be explained in terms of salting-out or salting-in effects, which are a result

of organic-inorganic interactions. These interactions may also induce liquid-

liquid phase separation during humidity cycles (Marcolli and Krieger, 2006;


Song et al., 2012). Considering the typical organic-inorganic compositions

of tropospheric aerosols, liquid-liquid phase separation should indeed occur

frequently when particles in the atmosphere are exposed to varying relative

humidity. Experimental studies and model predictions suggest that at moder-

ate to high RH, a liquid-liquid phase separation into an organic-rich aqueous

phase and an electrolyte-rich aqueous phase can be expected (Erdakos and

Pankow, 2004; Marcolli and Krieger, 2006; Ciobanu et al., 2009; Smith et al.,

2011; Zuend et al., 2010; Krieger et al., 2012; Song et al., 2012; Reid et al.,

2011; Bertram et al., 2011; Zuend and Seinfeld, 2012).

Water and semivolatile species are distributed between the gas and aerosol

phases are governed by gas-particle thermodynamic equilibrium (Pankow,

2003; Donahue et al., 2006; Zuend et al., 2010; Zuend and Seinfeld, 2012).

The gas-particle partitioning is determined by the activity of the semivolatile

species in the aerosol phase and their pure component subcooled liquid vapour

pressures. Reliable phase state description of an aerosol is essential for the es-

timation of the gas-particle partitioning of water and semivolatile substances

(Zuend et al., 2008, 2010). In addition, the phases present in the aerosol par-

ticles define the reaction medium for heterogeneous and multiphase chemistry

occurring in aerosol particles (Kalberer et al., 2004; Knopf et al., 2005; Anttila

et al., 2006, 2007).

Thermodynamic equilibrium calculations based on activity coefficient models

allow to determine whether the aerosol phase is a liquid, solid or a mixture of

solid and liquid phases. A thermodynamic model can be used for predicting

the activity coefficients of all components in a mixture, thereby predicting

mixing effects including changes of deliquescence relative humidities (Zuend

et al., 2008, 2011). Significant efforts have been made towards developing ac-

tivity coefficient models of mixed organic-inorganic-water systems (Ming and

Russell, 2002; Raatikainen and Laaksonen, 2005; Topping et al., 2005; Tong

et al., 2008; Zuend et al., 2008, 2011; Zuend and Seinfeld, 2012). These models

are generally composed of three different parts, an inorganic term, an organic

term, and an organic-inorganic mixing term. Organic aerosols contain a high

degree of organic functional groups. For complex organic/non-electrolytes

systems the UNIFAC model (Fredenslund et al., 1975) is widely used because

of its simplicity to describe complex, multicomponent systems. UNIFAC uses


a group-contribution approach which has the advantage of reducing the pa-

rameterisation of huge quantity of organic substances to the description of a

restricted number of functional groups contained in that huge variety of com-

pounds. The UNIFAC group-contribution model has also been extended to

include inorganic components by including an extended Debye-Huckel term

and determining semi-empirical UNIFAC parameters for ions (Yan et al., 1999;

Chang and Pankow, 2006; Zuend et al., 2008, 2011).

To reduce the uncertainties related to the aerosol forcing, a better physical

representation of the aerosols, their mixing states and properties in thermo-

dynamic models is required. Also, the detailed monitoring of liquid-liquid

phase separation and crystallization in micrometer-sized droplets allows for

improving the fundamental understanding of these processes.

An important uncertainty in climate models is associated with the under-

standing of upper tropospheric ice cloud formation (Swanson, 2009; Knopf

and Rigg, 2011). Ice particles in the atmosphere form by homogeneous and

heterogeneous nucleation. Homogeneous ice nucleation describes the forma-

tion of ice from a supercooled (or metastable) aqueous particle (at the tem-

peratures below the melting point) in absence of pre-existing substrate. The

heterogeneous ice nucleation process represents nucleation of ice from the pre-

existing substrate (Knopf and Rigg, 2011) at warmer temperatures than the

homogeneous nucleation. Aerosol particles which act as a ice nuclei (IN) can

affect the radiative energy budget by altering the radiative properties and

formation processes of cirrus clouds (Baker and Peter, 2008; Forster et al.,

2007). Cirrus clouds cover about 30 % of Earth’s surface (Wylie et al., 2005)

and have a significant effect on the global radiative budget, resulting in a net

climate warming contribution.

Homogeneous ice freezing and ice melting temperatures of aqueous solutions

depend on the aw and solute concentration, irrespective of the nature of the

solute (Koop et al., 2000; Koop, 2004). The large variation in freezing and

melting temperature of aqueous solution reflects the non-ideal behaviour of

the solutions at moderate to high concentration. Water activity (aw) for

the metastable range is not directly available, in such a case thermodynamic

models such as Pitzer ion-interaction models which mainly account for inor-

ganic/electrolyte solutions (Pitzer, 1991) and the modified UNIFAC (Dort-


mund) (Gmehling et al., 1998, 2002; Jakob et al., 2006) which are widely used

for aqueous organic solutions can be applied to predict aw for the metastable

temperature regime. Laboratory studies show that aqueous solutions at low

temperatures may exhibit significant changes in aw with temperature (Zo-

brist et al., 2003, 2008; Zuberi, 2003; Clegg et al., 1998). Such studies on the

temperature dependence of aqueous solutions are useful for the understand-

ing of organic-water mixing effects on homogeneous ice nucleation. Model

predictions require validation by experimental data, however, due to scarcity

of experimental data at supercooled temperatures for aqueous inorganic and

organic solutions it is hard to obtain reliable predictions of water activity

at freezing temperatures. This therefore motivates additional measurements

in the temperature range below room temperature down to the low tempera-

tures representing upper tropospheric conditions. To obtain better predictions

and constrain the homogeneous freezing and ice nucleation rates of particles

with organics requires corresponding homogeneous ice nucleation experiments.

Furthermore, the role of organics on aw of solutions at the freezing point has

to be estimated for a aw based description of ice nucleation in atmospheric

applications (Knopf and Rigg, 2011). Thus, a combined modeling and ex-

perimental approach is carried out in this thesis with the goal to deepen the

understanding of gas-to-particle conversion, phase transitions in aerosols and

ice nucleation studies.

Chapter 2

Chemical Thermodynamics and

Molecular Interactions

2.1 Thermodynamics of multicomponent sys-

tems

The theory of classical thermodynamics (“thermodynamics” refers to thermo

- heat and dynamics - force) describes processes that involve change in tem-

perature, transformation of energy, and the relationship between heat, work

and energy. It is used to describe macroscopic variables of a defined portion

of matter, such as pressure, temperature, entropy, internal energy and also

the physics that deals with the relationship and conversion between heat and

other forms of energy.

The atmosphere is composed of several chemical components which exist in the

gas phase and in the form of liquid or solid aerosol particles. Thermodynamic

properties of aerosols are used to describe the partitioning of semivolatile

species between the gas and condensed phase. Since water acts as a solvent

for many of the constituents in the aerosol phase (Seinfeld and Pandis, 1998),

the focus in this chapter is on aqueous systems. A thermodynamic system

is a specified portion of space/matter on which the study of energy transfer

or conservation is made. All the space around the system is called the sur-

rounding. A system is separated from its surrounding by a real or virtual

boundary and the exchange of mass, energy or heat between the system and

25

26 Chapter 2. Chemical Thermodynamics and Molecular Interactions

the surrounding takes place across the boundary. A homogeneous system is

one in which the chemical composition and the physical properties are uni-

form all over the system in a macroscopic sense, e.g., the densities measured

in points A and B have the same values. A so-called “open system” is defined

as a system that may exchange both mass and energy with its surrounding,

while a “closed system” is one that allows only exchange of energy through

the system boundary.

The thermodynamics of mixtures of chemical species introduced in this chap-

ter is part of the scope of chemical thermodynamics. Therefore, the focus of

Section 2.1 is on the properties and theoretical relations of a thermodynamic

system subject to a change in the chemical composition.

2.1.1 Homogeneous Open and Closed System

Consider an air parcel as a homogeneous system of volume V at temperature

T containing a number of k independent chemical components. The number

of moles of component i is represented by ni. The internal energy U arises

due to the potential and kinetic energy of the atoms and molecules of the

system. If there is an infinitesimal change in the state of the air parcel (e.g.

if the parcel slightly rises in the atmosphere) but there is no exchange of

mass with the surrounding (closed system), then, according to the first law of

thermodynamics the change in the internal energy of such a closed system is

given by (Prausnitz et al., 1986; Seinfeld and Pandis, 1998)

dU = dQ+ dW (2.1)

where dQ is the heat absorbed (= TdS) and dW is the amount of work

that is done by the system (= −pdV ). Eq. (2.1) is valid only for a single

component in a closed system not generally for a mixture where interactions

among components may happen, such as chemical reactions etc.

dU = TdS − pdV (2.2)

2.1. Thermodynamics of multicomponent systems 27

with pressure p and entropy S. Detailed derivations of this equation can

be found in most textbooks on classical thermodynamics. Since the sys-

tem is assumed to be closed according to Eq. (2.2), if the number of moles

(n1, n2, n3, ...nk) of all the components in the system are constant (conserved

mass, no reactions), the change in the internal energy is a function of S and

V .

If mass exchange is allowed (open system), the number of moles ni of the

individual components i may change and the internal energy as a function of

S, V , and the number of moles of the individual components ni is given as

U = f(S, V, n1, n2, n3, ..nk) (2.3)

dU =

(∂U

∂S

)V,ni

dS +

(∂U

∂V

)S,ni

dV +

k∑i=1

(∂U

∂ni

)S,V,nj 6=i

dni (2.4)

For a closed, non-reactive system dni = 0, and comparing Eq. (2.2) with

Eq. (2.4), which both are valid for a closed system, we obtain

T =

(∂U

∂S

)V,ni

,−p =

(∂U

∂V

)S,ni

(2.5)

Eq. (2.4) can be written as

dU = TdS − pdV +

k∑i=1

(∂U

∂ni

)S,V,nj 6=i

dni (2.6)

Thus, Eq. (2.6) represents the change in the internal energy of an open system.

The partial derivative of the internal energy with respect to a variation in the

number of moles of substance i, while keeping all other variables constant, is

defined as chemical potential µi:

µi =

(∂U

∂ni

)S,V,nj 6=i

(2.7)


The chemical potential contributes to the internal energy of a system and is

of fundamental importance in thermodynamic systems, analogous to pressure

and temperature. A temperature difference between two bodies determines

the tendency of heat transfer as a system progresses in time; likewise, a chem-

ical potential difference can be viewed as the cause for chemical reaction or

for mass transfer from one phase to another. Other extensive thermodynamic

potentials for closed systems can be obtained by using different pairs of the

variables p, V , T and S as independent variables in Eq. (2.2). Three other

pairs retaining the property of fundamental equation can be defined with the

use of partial Legendre transformations (Prausnitz et al., 1986). For example

the Helmholtz energy, A, by interchanging T and S in Eq. (2.2),

A = U − TS (2.8)

dA = −SdT − pdV . (2.9)

In Eq. (2.9) T and V are the pair of independent variables. If T and p are

used as the independent variables, the fundamental thermodynamic relation

is:

G = U − TS − (−pV ) = H − TS, (2.10)

dG = −SdT + V dp, (2.11)

where G is called the Gibbs energy or (Gibbs free energy) and H is the en-

thalpy of a closed system. Similarly, using the definitions of other fundamen-

tal functions (A,H,G) in combination with Eq. (2.6), the four fundamental

equations for an open system are

dU = TdS − pdV +∑i

µidni (2.12)


dH = TdS + V dp+∑i

µidni (2.13)

dA = −SdT − pdV +∑i

µidni (2.14)

dG = −SdT + V dp+∑i

µidni (2.15)

where the sum is over all (k) system components. From the definition of the

chemical potential by Eq. (2.7) and the four fundamental equations Eq. (2.12

to 2.15), the chemical potential can be written as:

µi =

(∂G

∂ni

)T,p,nj 6=i

(2.16)

which is also the partial molar Gibbs energy. For practical atmospheric ap-

plications, S and V cannot be used as independent variables since a criterion

for thermodynamic equilibrium in terms of measurable quantities is required.

In such a case the Gibbs energy is the preferred function, since T and p are

measurable independent state variables of G.

At temperature T , the Gibbs free energy (G) is a function of enthalpy (H) and

entropy (S) as shown in Eq. (2.10) while, the change in the Gibbs free energy

is described by the fundamental equation Eq. (2.15). For a closed system at

constant pressure, the change in the Gibbs free energy with temperature is

given by:(∂G

∂T

)p,ni

= −S. (2.17)

Using Eq. (2.10), the Gibbs-Helmholtz relation can be derived(∂

∂T

(G

T

))p,ni

= − HT 2

(2.18)


At constant pressure, the corresponding change in the enthalpy and entropy

is given by the isobaric heat capacity (Cp):(∂H

∂T

)p

= Cp (2.19)

(∂S

∂T

)p

=CpT

(2.20)

The following sections discuss in more detail the properties obtained from the

Gibbs energy function.

2.1.2 Thermodynamic Equilibrium

A heterogeneous closed system is made up of different phases, considered as

homogeneous, open systems, within an overall closed system (Zuend, 2007).

Thermodynamic equilibrium can be described as the “state” a system tends

to reach when given sufficient time (Zuend, 2007). From Eq. (2.15) for a

system at constant pressure (dp = 0) and temperature (dT = 0) we obtain

dG =∑i

µidni. (2.21)

At constant composition (dni = 0), it follows that dG = 0, i.e., the Gibbs

energy is constant. According to the second law of thermodynamics, the

entropy of a system increases in case of an irreversible process and remains

constant for reversible processes. When the entropy reaches a maximum value

(dS ≥ 0), the system has reached to an equilibrium. At thermodynamic

equilibrium, dG = 0, and given a constant T and p,∑i=1

µidni = 0. (2.22)

Equation. (2.22) is the thermodynamic condition for chemical equilibrium.

For a system with two phases (α, β) in equilibrium at constant p and T , a


change in the composition of species i from phase α to β can be presented by

nαi − dni = nβi + dni (2.23)

applying Eq. (2.22) for this two-phase system:

µαi = µβi . (2.24)

Equation. (2.22) represents the basic formulation for phase equilibrium at

constant p and T conditions. Thermodynamic equilibrium with respect to

different processes can be expressed by excluding the special effects such as

interfacial forces, electric, magnetic and gravitational fields (Zuend, 2007). In

case of a multicomponent system with the component number denoted by i

and the number of phases denoted by variable j:

Tαi = T βi = . . . = T ji : thermal equilibrium

pαi = pβi = . . . = pji : mechanical equilibrium (2.25)

µαi = µβi = . . . = µji : chemical equilibrium

where i = 1,2,...,k goes over all system components. For a heterogeneous

closed system in an equilibrium state, each phase state is characterized by its

temperature, pressure and chemical potential of each individual component

present. Since there are k components, a total of k + 2 variables are used

to characterize the phase. However, not all are independent variables. The

Gibbs-Duhem equation shows how these variables are related. The fundamen-

tal equation in terms of U Eq. (2.12) can be used to characterize a particular

phase state. Integrating Eq. (2.12) from a state of zero mass to finite mass

(ni = 0 to i) at constant p, T , gives

U = TS − pV +∑i

µini (2.26)

In the above equation U is a function of T , p, composition and the size

of the system. Also since U is a state function, the results shown in the


above equation are independent of the path of integration. Differentiation of

Eq. (2.26) gives a general expression for dU

dU = TdS + SdT − pdV − V dp+∑i

µidni +∑i

nidµi (2.27)

comparing Eq. (2.27) with Eq. (2.12) we obtain

−SdT + V dp−∑i

nidµi = 0 (2.28)

This is known as the Gibbs-Duhem relation, which shows that when T and p

of a system change there is a corresponding change in the chemical potential

of the various component species of the system. Thus for a system with

k component species, there will be k + 1 independent variables (degrees of

freedom) of the k + 2 variables per phase.

2.1.3 Chemical Potential of Ideal Gas

For a pure substance i the chemical potential is related to temperature and

pressure by the differential equation Eq. (2.28):

dµi = − SnidT +

V

nidp. (2.29)

Using molar entropy si = S/ni and molar volume vi = V/ni in Eq. (2.29)

yields:

dµi = −sidT + vidp. (2.30)

Integrating and solving for µi for a certain temperature and pressure, we have

µi(T, p) = µi(T′, p′)−

T∫T ′

sidT +

p∫p′

vidp (2.31)


where superscript (T′, p′) refers to a reference state pressure and temperature.

According to the ideal gas equation (vi = RT/p) for a pure, ideal gas at

constant temperature T :

µi(T, p) = µi(T, p′) +RT ln

(p

p′

)(2.32)

where R is the universal gas constant. According to Eq. (2.32) at constant

temperature, the change in chemical potential of an ideal gas is a logarithmic

function of pressure.

Similarly, the approach for a pure, ideal gas, could be applied to define the

chemical potential of species i in an ideal gas mixture with total pressure p,

and is given by:

µi(T, p) = µi (T ) +RT ln

(p

p

)+RT ln yi (2.33)

where µi (T ) is the standard chemical potential of i under standard pressure

(p = 105 Pa). The mole fraction in the gas phase mixture is:

yi =ni∑k

nk(2.34)

where the sum goes over all species in the mixture. Introducing the partial

pressure of species pi = pyi in Eq. (2.33):

µi(T, p) = µi (T ) +RT ln

(pip

)(2.35)

is the chemical potential of species in the ideal gas mixture.

2.1.4 Ideal Solutions

A solution is defined as an ideal solution if the chemical potential of every

component is a linear function of the logarithm of its aqueous mole fraction

xi. The chemical potential for an ideal solution is given by:


µi(T, p) = µ∗i (T, p) +RT lnxi (2.36)

where µ∗i (T, p) is the standard chemical potential of pure species i (xi = 1)

at the same temperature and pressure as the solution under discussion. µ∗i is

a function of both Tand p but does not depend on the chemical composition

of the solution. In contrast to the ideal gas where there are no intermolecular

interactions, in case of liquids the molecular interactions can be substantial

and generally cannot be ignored. Any pure liquid is by definition an ideal

solution i.e., the interactions between the molecules of different species are

equal to the interactions between those of the same species hence practically

there is nothing like a ideal liquid. For example, a mixture of H2O and D2O,

where H stands for protons and D for deuterium, is a nearly perfect ideal

mixture (Zuend, 2007). A multicomponent solution, is ideal only if Eq. (2.36)

is satisfied by every component of the solution (which is typically not the

case).

To describe the distribution of a species between the different phases of a

system let us assume an ideal solution containing species j in thermodynamic

equilibrium with its gas phase:

µ(G)j = µ

(L)j (2.37)

using Eq. (2.33) and Eq. (2.36):

µj (T ) +RT ln

(pjp

)= µ∗

j (T, p) +RT lnxj (2.38)

For pure species j (i.e., an ideal solution; xj = 1), the pressure over the liquid

is the saturation vapour pressure psatj

µ∗j (T, p

satj ) = µ

j (T ) +RT ln

(psatj

p

)(2.39)

using Eq. (2.39) in Eq. (2.38)

ln

(pjp

)=µj (T ) +RT ln

(psatj

p

)− µ

j (T )

RT+ lnxj (2.40)


ln

(pjp

)= ln

(psatj

pxj

)(2.41)

pj = psatj xj (2.42)

is known as the Raoult’s law (for ideal solutions) which states that the vapour

pressure pj of species j over a solution is equal to the product of the pure

component vapour pressure psatj and its mole fraction xj in the solution. For

pure j, the reference state saturation vapour pressure psatj is often written as

pj (not to be confused with the standard total pressure p0).

2.1.5 Non-ideal Solutions

As shown in Fig 2.1(a,b) consider a solution with components A and B. In case

the solution is ideal, then the partial pressures of A and B will vary linearly

with the liquid-phase mole fraction of A, xA. The equilibrium pressure over

the solution is pA when xA = 1 and xB = 0 and according to Eq. (2.42) the

vapour pressure of A in equilibrium with the solution is:

pA = pAxA (2.43)

While in case of a solution highly dilute in B, when xA → 1,

pB = kBxB (2.44)

where kB is known as Henry’s constant and is calculated from the slope of

the pB curve as xA → 1. Henry’s law is valid for dilute solution of B (low

concentration of B). In the non-ideal case (the general case), the relation

between pA, pB , and xA is non-linear except at the concentration limits, when

xA → 0 or 1. Non-ideal solutions approach ideality when the concentrations

of all components but one approaches zero. Interactions between molecules


+

+

Figure 2.1: Vapour pressure over a non-ideal liquid mixture of components A and B

at vapour-liquid equilibrium (VLE).(a) Positive deviations from ideality, (b) negative

deviations from ideality. pA and pB are vapour pressures and pA and p

B are the

saturation vapour pressures of the pure components A and B in gas phase. Ptot

represents the total pressure and is the sum of partial pressure pA and pB.

of substances A and B may cause positive or negative deviation from ideality

and lead to non-ideal mixtures. Practically, almost all solutions are non-ideal.

Ideal solutions were generalized by using the fugacity function f , (Lewis, 1907;

Lewis and Randall, 1961). For an isothermal change of any component in any

system, whether solid, liquid, or gas, pure or mixed, ideal or not (Prausnitz

et al., 1986)

µi − µi = RT ln

fifoi

(2.45)

Note, while either µoi and foi is arbitrary, both may not be chosen indepen-

dently, i.e., if one is chosen arbitrarily, the other one is fixed (Prausnitz et al.,

1986; Zuend, 2007). Fugacity is equal to the pressure for pure, ideal gas,

while for a component i in ideal gas mixture, it is equal to its partial pressure


yip (Prausnitz et al., 1986). Since all systems, pure or mixed, at very low

pressures approach ideal gas behavior, the definition of fugacity is completed

by the limit

fiyip→ 1 (2.46)

as p → 0, where yi is the mole fraction of component i. Lewis called the

ratio fifoi

“the activity” designated by the symbol a (Prausnitz et al., 1986).

The activity of a substance gives an indication of how ‘active’ a substance is

relative to its standard state and provides a measure of the difference between

the actual chemical potential of the substance and that at its standard state.

Introducing the activity ai of a substance i, in Eq. (2.36)

µi(T, p) = µ∗i (T, p) +RT ln ai (2.47)

Comparing this equation with Eq. (2.36), deviation from the ideality can be

expressed as

γi =aixi

(2.48)

where the activity coefficient (γi) is defined on mole fraction basis. γi is

a factor used in thermodynamics to account for deviations from the ideal

behavior in a mixture of chemical substances. The chemical potential of a

non-ideal solution can be represented by introducing the activity coefficient

γi of a substance i, in Eq. (2.36)

µi(T, p) = µ∗i (T, p) +RT lnxi +RT ln γi (2.49)

where the chemical potential from ideal contributions µidi , are described by

the first two terms on the right hand side and the last term is the correction

or excess contribution µexi to the chemical potential.

Fig 2.1 shows component vapour pressures over a solution. The ideal curves

for the mixture components A and B are calculated from Raoult’s law. Non-

ideal mixtures show a positive or negative deviation which reflects the effect

of molecular interactions between the solution components A and B. In an


ideal solution γi = 1, the cohesive and the adhesive forces of the mixture

components balance each other. Figure 2.1(a) shows positive deviations (γ >

1) indicating that adhesive forces between like molecules are weaker than the

cohesive forces. The dissimilarities of polarity or internal pressure will lead

both components to escape solution more easily than expected from Raoult’s

law. Figure 2.1 (b) shows negative deviation (γ < 1) indicating that the

adhesive forces between different components are stronger than the average

cohesive forces between the components, as a consequence each component is

retained in the liquid phase by attractive forces which are stronger than in the

pure liquid so that its vapour pressure is lower than expected from Raoult’s

law. It is often the case that one component has an γ > 1 while the other

component has γ < 1 in parts of the composition range.

The Raoult’s law may be adapted to non-ideal solutions by accounting for the

interactions between the molecules of different components of the mixture.

For a more general case, assuming ideal behaviour in the gas phase, but

considering the real behaviour in the liquid mixture, the modified Raoult’s

law is:

pi = pi xiγi (2.50)

Hence, in general, activity coefficients (γ) are used to represent non-ideal mix-

ing and act as a correction for interactions of mixture components in different

phases. The fundamental Gibbs energy can be calculated if a mixture’s molar

composition, activity coefficients and standard chemical potential are known.

Hence, thermodynamic models and measurements of phase equilibria aim at

estimating activity coefficients.

2.1.6 Gibbs excess energy

The Gibbs energy of a system takes into account the non-ideal contribution

caused by intermolecular interactions, and can be separated from the ideal

contributions. Therefore, the Gibbs energy is considered as the sum of “ideal”

and the so called “excess” contribution (non-ideal contribution)(Zuend et al.,


2008). To obtain activity coefficients, an expression which gives excess Gibbs

energy (Gex) as a function of composition, temperature and pressure is re-

quired. For a system, at constant pressure and temperature the Gibbs energy:

G =∑i

µini. (2.51)

µidi and µexi from Eq. (2.49) in Eq. (2.51), can be used to define ideal Gibbs

energy Gid and excess Gibbs energy Gex contributions on mole fraction scale:

Gid =∑i

niµ∗,(x)i +

∑i

niRT lnxi (2.52)

Gex =∑i

niRT ln γ(x)i (2.53)

Applying the definition of chemical potential from Eq. (2.16), for a substance

A, the ideal chemical potential is:

(∂Gid

∂nA

)T,p,ni6=A

= µ∗,(x)A +

∂

∂nA

[∑i

niRT lnxi

](2.54)

(∂Gid

∂nA

)T,p,ni6=A

= µid,(x)A = µ

∗,(x)A +RT lnxA (2.55)

The correspondiong partial derivative of Gex is:

(∂Gex

∂nA

)T,p,ni6=A

= RT ln γ(x)A +RT

∑i

(ni∂ ln γ

(x)i

∂nA

)(2.56)

According to Gibbs-Duhem relation the partial derivative on the right side is

equal to zero. Thus the excess chemical potential is:


(∂Gex

∂nA

)T,p,ni6=A

= µex,(x)A = RT ln γ

(x)A (2.57)

The corresponding equations for the activity coefficients for substance A can

be derived from excess Gibbs equation:

ln γ(x)A =

[∂Gex/(RT )

∂nA

]p,T,ni6=A

(2.58)

For liquids at standard atmospheric pressure, the effect of pressure is neg-

ligible. The effect of temperature is not negligible and the temperature de-

pendence of the activity coefficients is described quantitatively by the Gibbs-

Helmholtz equation (Gmehling, 1995; Gmehling et al., 1998).(∂ ln γA∂(1/T )

)p,xA

=hexAR

(2.59)

The above expression provides a direct relationship between the temperature

dependence of the activity coefficients and molar excess enthalpy (hexA ). The

hexA measured at different temperatures are important for the revision and

extension of thermodynamic group contribution models.

2.2 Solubility

The dissolution process of solid (solute) in a solvent which is typically a liquid

or mixture of liquids takes place first by the fusion of the solid followed by

the mixing with the solvent. For example, when a (somewhat soluble) solid

(solute) is brought in contact with a liquid (solvent), the solid will start to

dissolve, to some extent, into the liquid. At a certain point, as we continue to

add more of the solid to the liquid, the solid will no longer dissolve into the

liquid on the macroscopic level. Thus the solution formed is then said to be

saturated (with respect to the solid) and the concentration of the saturated

solution is known as the solubility of the specific compound in the solvent

used. In equilibrium state, the concentration of the solution depends on the

2.2. Solubility 41

activity of the solid phase and the properties of the solution and temperature.

While a supersaturated solution may reach equilibrium by nucleation of the

solid in the solution (Nordstrom, 2008).

Solubility not only depends on the activity coefficients of the solute in solu-

tion (which is function of the intermolecular interactions between solute and

solvent), but also on the fugacity of the standard state to which that activity

coefficient refers and on the fugacity of the pure solid (Prausnitz et al., 1986).

Assuming that there is no appreciable solubility of the liquid solvent in the

solid phase (S), fugacity for a solute component i in equilibrium with its liquid

phase (L) is:

fLi = fSi (2.60)

using the activity coefficients and standard state fugacity, the expression for

fugacity can be given by (Diedrichs and Gmehling, 2010):

xLi γLi f

0,Li = xSi γ

Si f

0,Si (2.61)

where xi composition in mole fraction while γi is the activity coefficient for

component i in the solid and liquid phases. f0i is the standard state fugacity

to which γi refers. The value of f0i should belong to the same temperature as

that of the solution. Therefore, the solubility of a solute in the liquid phase

can be calculated by:

xLi =xSi γ

Si f

0,Si

γLi f0,Li

(2.62)

The SLE data is usually composed of systems where the pure solid crystallizes,

both the activity coefficients and the mole fraction of the component i in the

solid phase will be equal to unity (xSi , γSi = 1):

xLi =f0,Si

γLi f0,Li

(2.63)

Thus, Eq. (2.63) indicates that solubility not only depends on the activity

coefficients but also on the ratio of fugacities of i.


Solubilities of chemical substances can differ significantly because of differ-

ences in their melting points and heats of fusion (Jakob et al., 1995). Sol-

ubility also shows temperature dependence. The temperature influences the

intermolecular interactions between the solute and the solvent components

which are responsible for the solubility of solutes in a solution. But the main

effect is the increasing importance of entropy with the rise in temperature

since the entropy of the liquid solution is larger than the entropy of the crys-

talline solid phase.

2.2.1 Solid-liquid equilibria

Solid-liquid equilibria (SLE) can be more complex than those involving vapour

and liquids, since it is possible that a liquid phase can be accompanied by dif-

ferent coexisting solid phases which may be mixed crystals or crystals of pure

compounds whose formation is governed by thermodynamics and chemical be-

haviour of the components in the solution. The solid-liquid equilibrium shows

the dependence of solubility on temperature at constant pressure, where the

liquid phase is in equilibrium with a solid phase. Experimental solid-liquid

phase equilibria are therefore a source to derive activity coefficients typically

in a temperature range lower than room temperature. In a system at SLE,

and if the data for its pure components (solute and solvent) is known, it is

possible to calculate the activity coefficients in the liquid phase.

Let us consider chemical species i in solid-liquid equilibrium. At temperature

T and pressure p, the chemical potential of species i in the pure (single sub-

stance) solid phase (S) in equilibrium with the liquid phase (L) is given by:

µSi (T, p) = µLi (T, p) (2.64)

Analogous to Eq. (2.47) or Eq. (2.66) for liquid solutions, the chemical po-

tential of the species i in solid phase (µSi ) is given as:

µSi (T, p) = µ0,S,(x)i (T, p). (2.65)

2.2. Solubility 43

where µ0,S,(x)i is the standard state chemical potential of species i based on

mole fraction scale. The chemical potential of species i is equal to its standard

state chemical potential µ0,S,(x)i because the solid species i is in its standard

state defined as the pure crystalline component at system temperature (T )

and pressure (p).

The chemical potential of species i in liquid phase (µLi ) is given by:

µLi (T, p) = µ0,L,(x)i (T, p) +RT ln(γ

(x)i xi) (2.66)

where µ0,L,(x)i is the liquid phase standard state chemical potential and γ

(x)i

is the activity coefficient for i based on mole fraction scale. The liquid phase

mole fraction of species i is given by xi. Using Eq. (2.48) in Eq. (2.66) we get

µLi (T, p) = µ0,L,(x)i (T, p) +RT ln a

(x)i (2.67)

where a(x)i is the activity of species i based on mole fraction scale. Comparing

equations Eq. (2.64) and Eq. (2.67):

µ0,S,(x)i (T, p) = µ

0,L,(x)i (T, p) +RT ln a

(x)i (2.68)

The change in the molar Gibbs energy change of fusion (or melting) (∆Gf )

for pure component i at temperature T and pressure p is:

∆gf (T, p) = µL,(x)i (T, p)− µS,(x)

i (T, p) (2.69)

Using Eq. (2.68) and Eq. (2.69) the activity of species i in the solid phase is

given by:

ln a(x),SLE(T, p) =µ

0,S,(x)i (T, p)− µ0,L,(x)

i (T, p)

RT= −∆gf (T, p)

RT. (2.70)

From Eq. (2.10), for species i, the change in the Gibbs free energy of fusion at

temperature T is related to the corresponding change in enthalpy and entropy

and is given by:


∆Gf = ∆Hf − T∆Sf . (2.71)

Using Eq. (2.71) in Eq. (2.70)

ln a(x)i = −∆hf

RT+

∆sf

R(2.72)

where for species i, ∆sf (T ) and ∆hf (T ) represents the molar entropy and

enthalpy of fusion, respectively. The enthalpy of fusion at temperature T

equals:

∆Hf (T ) = HL(T )−HS(T ) (2.73)

where HL is the enthalpy of formation of i in the liquid state (L) while HS is

the enthalpy of formation of i in solid state (S) at temperature T and (total)

pressure p.

At constant pressure, integrating Eq. (2.19) from Tt,i to temperature T , where

Tt,i represents the triple point temperature of i :

∆Hf (T ) = ∆Hf (Tt,i) +

T∫Tt,i

CLp dT −T∫

Tt,i

CSp dT (2.74)

where CLp and CSp denote the temperature-dependent heat capacity at con-

stant pressure of i in the liquid and the solid state respectively.

∆Hf (T ) = ∆Hf (Tt,i) +

T∫Tt,i

∆CpdT (2.75)

where ∆Cp is the heat capacity difference at constant pressure between the

liquid and solid state and is given by:

∆Cp = CLp − CSp . (2.76)

2.2. Solubility 45

The entropy of fusion ∆Sf (T ) at constant pressure is obtained by integrating

Eq. (2.20) from temperature Tt,i to T , where Tt,i is the triple point tempera-

ture of species i and given by:

∆Sf (T ) = ∆Sf (Tt,i) +

T∫Tt,i

∆CpT

dT (2.77)

At (Tt,i), the activity (ai) for the component i in the solution at SLE is equal

to unity and the Gibbs free energy of fusion ∆Gf at the Tt,i is equal to zero

and hence Eq. (2.72) can be written as:

∆Sf (Tt,i) =∆Hf (Tt,i)

Tt,i(2.78)

using Eq. (2.71), Eq. (2.75), Eq. (2.77) and Eq. (2.78), the change in Gibbs

free energy is:

∆Gf (T ) = ∆Hft,i

(1− T

Tt,i

)+

T∫Tt,i

∆CpdT − TT∫

Tt,i

∆CpT

dT (2.79)

assuming that ∆Cp is constant over the temperature range T - Tt,i and using

Eq. (2.79) in Eq. (2.70), ai at constant pressure can be given as (Prausnitz

et al., 1986; Nordstrom, 2008):

ln a(x),SLEi = −

∆Hft,i

RT

(1− T

Tt,i

)+

∆CpR

(1− T

Tt,i

)+

∆CpR

lnT

Tt,i(2.80)

Eq. (2.80) can be made more practical by making some simplifications. Gen-

erally at atmospheric pressure levels, for most pure chemical species the triple

point (Tt,i) differs only very little from the melting point Tm,i often available

in the literature. Similarly, the difference in the molar enthalpy of fusion at


Tt,i and Tm,i are negligible. The Eq. (2.80) for species i at Tm is given by

(Jakob et al., 1995; Lohmann et al., 2001):

ln a(x),SLEi = −∆hm,i

RT

(1− T

Tm,i

)(2.81)

where ∆hm,i is the molar enthalpy of melting. Eq. (2.81) is a simplified ver-

sion, it in addition assumes that the ∆Cp terms of Eq. (2.80) approximately

cancel each other out and are generally small compared to changes in molar

enthalpy changes. Eq. (2.81) can be used to perform calculations to which

most of the non-electrolytes belong. A solid-solid phase transition between

different crystalline morphologies (structures) may occur below the melting

point of the pure solid and has to be taken in account when the temperature

under consideration lies below the transition temperature i.e., (T ≤ Ttr,i),

where Ttr,i is the solid-solid phase transition temperature. A solid-solid tran-

sition can be represented as:

ln a(x),SLEi = −∆hm,i

RT

(1− T

Tm,i

)− ∆htr,i

RT

(1− T

Ttr,i

)(2.82)

where ∆htr,i is the molar enthalpy of solid-solid phase transition, Ttr is the

transition temperature, T is the given absolute temperature, R is the univer-

sal gas constant. Since activity coefficients are concentration and temperature

dependent, Eq. (2.81) and Eq. (2.82) need to be solved iteratively for a given

system and target temperature level at SLE conditions. For ideal systems,

Eq. (2.81) suggests that solubility of the solute increases with the increase in

temperature until the melting point is reached. By accurate determination of

the solute activity in the liquid phase it is possible to differentiate the influence

of the solute-solvent interactions on the solubility of pure solid compounds in

different solvents. In thermodynamics modeling, the temperature dependence

of (γi) is quantitatively described by the molar excess enthalpy (hex) as shown

in Eq. (2.59). The excess enthalpy data at higher temperatures are required

as supporting data for higher temperatures while at lower temperatures the

SLE data which accounts information of ∆hm, i and ∆htr, i are useful for fit-

ting the temperature-dependent group interaction in thermodynamic models.

The excess heat capacities (Cexp ) provide quantitative information about the

2.3. Intermolecular Interactions 47

temperature dependence of the excess enthalpies. Thus Eq. (2.82) provides

a simple relation among the solubility (xi), system temperature (T ) and ac-

tivity coefficients (γi) if the melting temperature (Tm, i), enthalpy of fusion

(∆hm, i), enthalpy of transition (∆htr, i),and transition temperature (Ttr, i)

of component i are known.

2.3 Intermolecular Interactions

The basic structural unit of any substance/matter is formed by atoms and

molecules which are bound to each other by attractive forces. These forces are

classified into intramolecular forces and intermolecular forces. An intramolec-

ular force is any force that holds together the atoms making up a molecule

by covalent (polar/non-polar) and ionic bonding. The type and strength of

the bonds influence molecular shape, and the chemical behavior of substances.

On the other hand, non-covalent intermolecular forces exist between molecules

and are largely responsible for the physical state of a substance at a given tem-

perature and pressure. The thermodynamic properties of mixtures depend on

the intermolecular interactions between the different molecules/atoms/ions in

a mixture. However, in case of mixtures it is more complicated since consid-

eration has to be given not only to interactions between molecules belonging

to the same component, but also to interaction between dissimilar molecules

(Prausnitz et al., 1986).

The physical state of matter depends on the intermolecular forces and the ki-

netic energy of the atoms/molecules/ions of the mixture components. Solids

and liquids differ from gases due to the existence of stronger attractive forces

between closely confined of atoms/molecules/ions of lower kinetic energy. In

case of the gaseous state, molecules experience very week attractive forces

and have a relatively high kinetic energy. Intermolecular interactions are elec-

trostatic in nature; even the strongest intermolecular interactions are much

weaker than covalent or ionic bonds (≤ 15% as strong) (Brown et al., 2009).

However, they are strong enough to control the thermophysical properties

such as boiling and melting points, vapour pressure and viscosity of a sub-

stance. Fig 2.2 shows a comparison of a covalent bond (an intramolecular


Figure 2.2: Intramolecular and intermolecular forces in HCl molecules. The in-

tramolecular interactions within a HCl molecule is represented by a solid line while

intermolecular interactions between the two HCl molecules are represented by the

dash/dotted line.

force) and an intermolecular attraction. Since the intermolecular attractions

are weaker than the covalent bonds, they are usually represented by dash/dot

symbols. In case of the HCl molecule the energy required to break the cova-

lent bond to dissociate HCl into H+ and Cl− ions is 431 kJ/mol while at the

normal boiling point (-85C) of HCl only 16 kJ/mol is required to overcome

the intermolecular attraction between HCl molecules in the liquid state and

and its transition into the vapour state. Thus, when a molecular substance

such as HCl changes its physical state from solid to liquid to gas, the molecules

themselves remain intact (Brown et al., 2009).

Many properties of liquids, including their boiling points, reflect the strength

of the intermolecular forces, for example under normal atmospheric pressure (1

atm = 101325 Pa) HCl with weak intermolecular forces has a boiling point of

-85C. Liquid state molecules have to overcome the attractive forces to escape

to the vapour state. The boiling point of liquids increases for substances with

higher intermolecular forces. Similarly the melting point of solids is higher

for substances with higher intermolecular forces. Depending on the molecular

structure, functional groups and polarity, neutral molecules may take part in

four types of intermolecular interactions namely, dipole-dipole interactions,

dipole-induced dipole forces, London dispersion forces and hydrogen bonds.

The first three forms of attractions are collectively called as the van der Waals

forces. In addition, there is also a fifth type, ion-dipole interaction and these

are important in solutions containing ions.


2.3.1 Ion-dipole forces

These forces exists between an ion and the partly charged end of a polar

molecule. Ion-dipole interactions are important forces of attraction in solu-

tions with ions. Polar molecules such as dipoles have a positive end and a

negative end. There are also polar molecules that have more than one dipole,

overall forming quadrupoles/multipoles. The positive ions are attracted to

the negative end of the dipole while the negative ions are attracted to the

positive end of the dipole. The magnitude of attraction increases with either

increase in the charge of the ion or as the magnitude of the dipole moment

increases. These forces are especially important to dissolve ionic substances

in polar solvents for example sodium chloride (NaCl) in water (Brown et al.,

2009). Fig 2.3 shows ion-dipole interaction between the Na+ and Cl− with

H2O. The water molecules orient themselves on the surface of the NaCl crys-

tals such that the positive end of the water dipole are oriented towards the

Figure 2.3: Ion-dipole interaction of Na+ and Cl− with water molecules. δ+ and

δ− are partial positive and negative charges created due to asymmetrical distribution

of electrons in chemical bonds.


Cl− ions while the negative end of the water molecules are oriented towards

the Na+ ions. The ion-dipole attractions between the Na+ and Cl− ions and

the water molecules are strong enough to pull the Na+ and Cl− ions from

their positions in the NaCl crystals. NaCl dissolves in water since the forces

of interaction between the Na+ and Cl− ions and the polar water molecules

are stronger to overcome the interaction forces between the Na+ and Cl− ions

in the NaCl crystals and the interactions between the water molecules.

2.3.2 Dipole-Dipole forces

Overall neutral, polar molecules attract each other when the positive end of

one molecule is near the negative end of the other molecule. Dipole-dipole

interactions are generally weaker than the ion-dipole forces. In liquids, po-

lar molecules move freely with respect to each other and orient in a way

such that both repulsive interactions (dashed red lines in Fig. 2.4) between

like charges and attractive interactions between oppositely charged poles (red

lines in Fig. 2.4) exists, with an overall effect of net attraction. In molecules of

approximately equal size and shape, the magnitude of intermolecular interac-

tions increases with increasing polarity. For substances with similar molecular

weight but different dipole moments the boiling point increases with increas-

ing dipole moment for example propane (CH3CH2CH3) with molar mass of

44 g/mol and dipole moment of 0.1 debyes (D) has a normal boiling point of

231 K while acetaldehyde (CH3CHO) with molar mass 44 g/mol and dipole

moment of 2.7 debyes (D) boils at 294 K (Brown et al., 2009). For the dipole-

dipole forces to be effective, molecules should be close together with optimal

relative orientation.

2.3.3 Dipole-induced dipole interactions

These interactions like the dipole-dipole interactions, also depend on the pres-

ence of polar molecules. In case of dipole-induced dipole interactions the sec-

ond participating molecule is a non-polar molecule, unlike the dipole-dipole

interactions that involve interactions between polar molecules only. In dipole-


Figure 2.4: Dipole-Dipole interactions. Solid red lines: strong interaction forces

between any two opposite charges, dashed red lines: strong repulsive interaction

forces between the like charges.

induced dipole interaction, the partial charges of the polar molecules causes

polarization, or distortion of the electron orbitals of the other molecule (i.e.,

the nonpolar molecule). As a result of this distortion, the second molecule

acquires partial negative and positive charges and thus becomes slightly polar.

The partial charges formed act just like the permanently polar molecules and

interact favorably with their counterparts in the polar molecule that origi-

nally induced them. For example, interaction between a polar HCl molecule

and Ar molecule. The Ar molecule experiences a dipole as its electrons are

attracted to H and or repelled by the Cl. The dipole-induced dipole interac-

tions are weaker than the dipole-dipole interactions but are stronger than the

dispersion forces.

2.3.4 Dispersion forces

Dipole-dipole interactions do not exist between non-polar atoms and molecules

due to the absence of a dipole moment. However, there are attractive forces


caused by momentary dipoles (temporary dipoles) created by the uneven dis-

tribution of electrons within the molecule/atom at any instant. This tem-

porary dipole in one molecule/atom induces an opposite temporary dipole

in the neighbouring molecule/atom and vice versa. These temporary partial

positive and negative charges that develop between molecules/atoms lead to

attractive interactions also known as dispersion forces. For example as seen

in Fig 2.5 the charge distribution in an He atom on average is spherical as

represented by the spherical electron orbitals. In Fig 2.5a electrons in the 1s

orbital of helium repel each other and therefore, tend to stay away from each

other (He atom 1), it does happen that they occasionally wind up on the same

side of the atom (He atom 2). At this instant, then, the He atom is polar,

with an excess of electrons on the left side and a shortage on the right side.

Figure 2.5b shows that the two dipoles arrange their position with the elec-

tric fields which leads to dipole formation. The strength of dispersion forces

depends on the ease with which the distribution of electrons in a molecule

is distorted also known as polarizability. Dispersion forces operate between

all molecules and atoms, whether they are polar or non-polar. Molecular size

and mass are highly correlated, thus overall effect of dispersion forces tends

to be stronger for substances with higher molecular weight.

When comparing the relative strength of intermolecular forces of polar

Figure 2.5: Dispersion forces. (a) Spherically symmetric charge distribution in

He atom 1. (b) The uneven electron distribution produces a momentary dipoles and

allows temporary electrostatic attraction between atoms.


molecules in two substances, in case of two molecules of comparable size

(molecular weight) and shape, the dispersion forces are approximately equal

in both the substances and the relative strength of intermolecular attraction

will be determined by the dipole-dipole interactions, i.e., the substance with

the more polar molecules will have stronger intermolecular forces. In case

of molecules of different sizes (molecular weight), dispersion forces will likely

determine the substance having stronger intermolecular forces (Brown et al.,

2009).

2.3.5 Hydrogen bonds

Hydrogen bonds are a special type of intermolecular attractive forces that

occur when a H atom is attached via a covalent bond to a small, highly elec-

tronegative atom, for example F, O, or N. Because of the electronegativity

differences and relatively small atom sizes, the H atom will have a permanent

partial positive charge and the F, O, or N atom will develop a permanent par-

tial negative charge, i.e., this configuration forms a special form of a strong

dipole. Fig 2.6 shows an example for hydrogen bonding between H2O and

NH3 molecules. Hydrogen bonding contributes to the hydration of organic

compounds containing oxygen or nitrogen atoms and thus accounts for the

much greater aqueous solubility of alcohols than hydrocarbons.

As a general trend, the boiling points of a series of molecular substances in-

crease with the increasing molecular mass, which is due to the combined effects

Figure 2.6: Hydrogen bonding between H2O and NH3 molecules.


of stronger dispersion forces among the atoms of larger molecules. However

H2O is a notable exception whose boiling point (100C) at 101.325 kPa (at-

mospheric pressure) is higher than expected from dispersion forces and typical

dipole-dipole force strength alone, when considering the relatively lower molar

mass (18.0153 g/mol) of H2O. Fig 2.7 shows the hydrogen bonding between

H2O molecules. The bent geometry of the water molecule and the highly

polar nature of the O-H bonds form a molecule with a strong dipole moment.

The two O-H bonds in a H2O molecule allow it to form strong hydrogen

bonds with other water molecules, resulting in a relatively high boiling point.

Hydrogen bonds are generally stronger than other dipole-dipole or dispersion

forces, and play a significant role in chemical processes, including those of

biological and atmospheric importance. Thus both the physical and chemical

properties play an important role in determining properties of solutions. The

purpose of applying thermodynamic methods to phase-equilibrium calcula-

tions is to classify, interpret, correlate, and predict properties of solutions.

The extent to which this purpose can be fulfilled relies on the degree of un-

derstanding of intermolecular forces, which are responsible for the molecular

behaviour on the microscopic scale, defining macroscopic thermodynamic be-

havior. Classical and statistical thermodynamics can define useful functions

Figure 2.7: Hydrogen bonding between water molecules. The red dash-lines are the

hydrogen bonds between the water molecules.


and derive relationships between them, but the specific parameters describing

molecular interaction effects on the macroscopic scale cannot be defined by

thermodynamic theory alone. Determination of these values, requires exper-

iments which may help to derive the microscopic physicochemical properties

of a mixture.

Chapter 3

Improved AIOMFAC model

parameterisation of the

temperature dependence of

activity coefficients for

organic-water mixtures

G. Ganbavale 1, A. Zuend 1,2,3, C. Marcolli 1, T. Peter 1

1 Institute for Atmospheric and Climate Science, ETH Zurich, Zurich,

Switzerland2 Department of Chemical Engineering, California Institute of Technology,

Pasadena, California, USA3 Department of Atmospheric and Oceanic Sciences, McGill University, Mon-

treal, Quebec, Canada

This chapter is a reproduction of a corresponding article, which is in prepa-

ration to be submitted to the journal “Atmospheric Chemistry and Physics”.

The layout of the article as well as the section, figure, and table numberings

57

58 Chapter 3. Improved AIOMFAC temperature dependence

have been adapted to match with the thesis structuring. Cited literature is

referenced in the bibliography of the thesis.

59

This study presents a new, improved parameterisation of the temperature de-

pendence of activity coefficients in the AIOMFAC (Aerosol Inorganic-Organic

Mixtures Functional groups Activity Coefficients) model applicable for aque-

ous as well as water-free organic solutions. For electrolyte-free organic and

organic-water mixtures the AIOMFAC model uses a group-contribution ap-

proach based on UNIFAC (UNIversal quasi-chemical Functional-group Ac-

tivity Coefficients). This group-contribution approach explicitly accounts for

interactions among organic functional groups and between organic functional

groups and water. The previous AIOMFAC version uses a simple param-

eterisation of the temperature dependence of activity coefficients, aimed to

be applicable in the temperature range from ∼ 275 to ∼ 400 K. With the

goal to improve the description of a wide variety of organic compounds found

in atmospheric aerosols, we extend the AIOMFAC parameterisation for the

functional groups carboxyl, hydroxyl, ketone, aldehyde, ether, ester, alkyl,

aromatic carbon-alcohol, and aromatic hydrocarbon to atmospherically rele-

vant low temperatures with the introduction of a new temperature dependence

parameterisation. The improved temperature dependence parameterisation is

derived from macroscopic (classical) thermodynamic theory by describing ef-

fects from changes in molar enthalpy and heat capacity of a multicomponent

system. Thermodynamic equilibrium data of aqueous organic and water-free

organic mixtures from the literature are carefully assessed and complemented

with new measurements to establish a comprehensive database, covering a

wide temperature range (∼ 190 to ∼ 440 K) for many of the functional

group combinations considered. Different experimental data types and their

processing for the estimation of AIOMFAC model parameters are discussed.

The new AIOMFAC parameterisation for the temperature dependence of ac-

tivity coefficients from low to high temperatures shows an overall improvement

of 25 % in comparison to the previous model version. The new parameterisa-

tion of AIOMFAC agrees well with a large number of experimental datasets

and enables the calculation of activity coefficients of a wide variety of different

aqueous/water-free organic solutions down to the low temperatures present

in the upper troposphere.


3.1 Introduction

Atmospheric aerosols are complex mixtures of inorganic and organic compo-

nents. A large variety of organic compounds account for a significant fraction

of the tropospheric aerosol composition. Airborne and ground-based measure-

ments suggest that the aerosols in the free troposphere are composed of 30%

and up to about 80% of carbonaceous material mostly in the form of organics

(Murphy et al., 2006; Jacobson et al., 2000; Hallquist et al., 2009). Aerosol

loading, size distribution, composition, morphology and physical states of par-

ticles affect the Earth’s radiative budget through the direct effects of aerosols

on climate and the indirect effects, in which aerosols act as cloud condensa-

tion (CCN) or ice nuclei (IN), affecting cloud particle number concentrations,

precipitation, cloud albedo, and life time. Organic aerosols are expected to

stay in a liquid, viscous semi-solid, or amorphous solid state, since the very

large number of organic compounds depresses the temperature at which or-

ganic crystal formation takes place (Marcolli et al., 2004; Virtanen et al., 2010;

Koop et al., 2011).

Non-ideal interactions between different organic and inorganic species in the

particle phase influence water uptake and release (hygroscopicity), may in-

duce liquid-liquid phase separation (LLPS) (e.g., Marcolli and Krieger, 2006;

Zuend et al., 2010; Song et al., 2012), influence gas-particle partitioning of

semivolatile compounds (e.g., Zuend et al., 2010; Zuend and Seinfeld, 2012),

and alter efflorescence and deliquescence relative humidities (e.g., Krieger

et al., 2012). Thermodynamic phase equilibrium calculations allow to de-

termine whether the aerosol phase is a liquid (or viscous amorphous phase),

a crystalline solid, or a mixture of solid and liquid phases and to what degree

semivolatile species partition to the condensed phases (Zuend et al., 2010;

Zuend and Seinfeld, 2012). Phase equilibria calculations can be carried out

by using composition dependent activity coefficients which account for the

non-ideality of the liquid/amorphous phase (Gmehling, 1995; Raatikainen and

Laaksonen, 2005; Zuend et al., 2010). The mole fraction based activity co-

efficient, γ(x)s and activity a

(x)s of a compound s are related by a

(x)s = γ

(x)s xs,

where xs is the mole fraction of s in the liquid mixture.

Thermodynamic models for mixtures of organics and water in condensed

3.1. Introduction 61

phases are usually based on the UNIQUAC (UNIversal QUAsi Chemical)

model (Abrams and Prausnitz, 1975) or its group contribution version UNI-

FAC (UNIquac Functional group Activity Coefficients) (Fredenslund et al.,

1975). The original UNIFAC model was developed for vapour-liquid equilib-

ria (VLE) calculations within a temperature range from ∼ 275 to ∼ 400 K.

Using the UNIFAC model outside of its intended temperature range may re-

sult in poor predictions of real phase behaviour (Lohmann et al., 2001). For

very dilute mixtures, UNIFAC thermodynamic model calculations for compo-

nent activity coefficients at infinite dilution are sometimes not in agreement

with the experimental data. This can be understood since most VLE mea-

surements were performed for liquid mole fractions between 0.02 to 0.98 and,

hence, do not provide specific information for the highly dilute regions. Inac-

curate results were obtained for other types of thermodynamic data, e.g., mo-

lar enthalpies of mixing (hE) or solid-liquid equilibrium (SLE) data, following

the Gibbs-Helmholtz relation, this leads to inaccurate description of activity

coefficients as a function of temperature (Gmehling, 2003, 2009; Lohmann

et al., 2001). With the original UNIFAC model, due to data insufficiency,

inaccurate predictions were often obtained for asymmetric systems (systems

containing molecules of different sizes and shapes) (Lohmann et al., 2001;

Gmehling, 2003). Since then, the original UNIFAC model has been improved

and in addition, modified UNIFAC versions such as modified UNIFAC (Dort-

mund) and modified UNIFAC (Lyngby) have been developed (Hansen et al.,

1991; Gmehling et al., 1998, 2002; Jakob et al., 2006; Larsen et al., 1987),

which amended some of the original weaknesses. For mixtures containing mul-

tifunctional components, both UNIFAC and modified UNIFAC (Dortmund)

sometimes show poor results since the functional group interaction parameters

were mainly determined based on experimental data of mixtures of simple,

monofunctional components (Weidlich and Gmehling, 1987; Gmehling et al.,

2012).

One of the important differences between the UNIFAC model by Hansen et al.

(1991), which we call here “standard UNIFAC”, and the modified UNIFAC

(Dortmund), is the use of a more elaborate parameterisation for the tempera-

ture dependence of activity coefficients in the modified UNIFAC model. How-

ever, the modified UNIFAC models sometimes do not provide reliable predic-


tions of activity coefficients at low temperatures relevant in the troposphere.

Calculations of water activity (aw) of atmospherically relevant aqueous or-

ganic solutions have shown that the performance of standard UNIFAC may

be poor when the organic fraction consists of multifunctional molecules typi-

cally carrying several strong polar functional groups with enhanced hydrogen-

bonding potential (Saxena and Hildemann, 1997; Peng et al., 2001). Marcolli

and Peter (2005) have therefore proposed improved sets of interaction pa-

rameters for standard UNIFAC for alcohols and polyols. Peng et al. (2001)

reparameterised the interaction of the water (group) with the carboxyl group

and the hydroxyl group based on measured water activities of aqueous systems

containing dicarboxylic acids and substituted dicarboxylic and tricarboxylic

acids.

For atmospheric applications, an accurate description of aqueous organic mix-

tures at atmospherically relevant temperatures is required. At low tempera-

tures aw is a crucial parameter for homogeneous ice nucleation (Koop et al.,

2000). Extrapolations of aw of different aqueous organic solutions measured

in the temperature range from the ice melting curve to 313 K suggest that

if the temperature dependence of the activity coefficients is neglected, errors

on the order of 10 to 15 % result for aw at the homogeneous freezing tem-

perature (Zobrist et al., 2008). The uncertainty in predicted homogeneous

ice nucleation temperatures is stated as ± 0.025 aw at melting points and ±0.05 aw at ice freezing temperatures (Koop et al., 2000; Koop, 2004). A small

uncertainty in aw of about 0.025 can change the corresponding homogeneous

nucleation rate coefficients by 6 orders of magnitude (or the onset temperature

of homogeneous freezing by up to 8 K) and may significantly affect predictions

of the onset of ice crystal formation in cloud microphysical models (Knopf and

Rigg, 2011). This shows the need for an improved UNIFAC (and AIOMFAC)

parameterisation at low temperatures.

The AIOMFAC model (Aerosol Inorganic-Organic Mixtures Functional

groups Activity Coefficients) by Zuend et al. (2008, 2011) is a thermody-

namic group-contribution model specifically developed to meet the require-

ments of typical tropospheric aerosol compositions. The model enables calcu-

lations of activity coefficients covering inorganic (water, electrolytes), organic,

and organic-inorganic interactions in multicomponent solutions over a wide

3.2. AIOMFAC model 63

concentration range and includes the standard UNIFAC for the description

of organic and organic-water systems. AIOMFAC is based on the group-

contribution model LIFAC by Yan et al. (1999) and, therefore, includes the

standard UNIFAC model, yet also includes the modified parameter sets from

Peng et al. (2001) and those from Marcolli and Peter (2005). In its short-

range interaction part, the AIOMFAC model shares the simple temperature

dependence expressions of the original UNIFAC model and involves only one

main group interaction term involving two adjustable parameters, am,n and

an,m per binary interaction. Throughout this article, we will refer to this

(original) AIOMFAC model as “AIOMFAC-P1”. The aim of this study is to

improve the performance of AIOMFAC at low temperatures for multicompo-

nent organic + water systems. We will refer to the new AIOMFAC version,

with an improved temperature dependence parameterisation with two addi-

tional main group interaction terms, as AIOMFAC-P3, indicating a 3-term

parameterisation in the short-range (mod. UNIFAC) part. The focus is on

organic functional groups that have been identified in tropospheric aerosols,

namely hydroxyl, carboxyl, ketone, ether, ester, aldehyde, alkyl, and aromatic

functionalities.

3.2 AIOMFAC model

The thermodynamic group-contribution model AIOMFAC allows thermody-

namically consistent calculations of activity coefficients at temperatures close

to 298 K and covers multicomponent solutions containing water, inorganic

ions, and organic compounds. For electrolyte-free systems of organic com-

pounds and water, the applicable temperature range is ∼ 275 to ∼ 400 K, as

for the original UNIFAC model. As mentioned above, the concept of AIOM-

FAC is based on the LIFAC model (Yan et al., 1999), which merges a Pitzer-

like approach with a slightly modified version of the original UNIFAC model

to calculate activity coefficients.

The non-ideality of a thermodynamic system is characterized by the excess


Gibbs energy Gex (p, T, nj), which in AIOMFAC is expressed as the sum of a

long range (LR), middle range (MR) and short range (SR) contribution:

Gex (p, T, nj) = GexLR + Gex

MR + GexSR. (3.1)

Here, p is the total pressure, T the absolute temperature, and nj (j = 1, . . . , k)

the molar amounts of the k components in a system. Mole fraction based ac-

tivity coefficients γ(x)j with nj moles in a mixture are derived from expressions

for the different parts of Gex using the relation

ln γ(x)j =

[∂Gex/(RT )

∂nj

]p,T,nj′ 6=j

(3.2)

where R is the universal gas constant. Activity coefficients are calculated

from the three model parts:

ln γ(x)j = ln γ

(x),LRj + ln γ

(x),MRj + ln γ

(x),SRj . (3.3)

Electrolyte solutions which may range from dilute to highly supersaturated

concentrations are, aside from their SR contribution, considered in the Pitzer-

like part, which combines LR and MR interactions. The LR interactions are

described by an extended Debye-Huckel term and represents contributions by

Coulomb electrostatic forces between permanently charged ions, moderated

by the presence of the dielectric solvent medium. The MR part represents the

effects of interactions involving ions and permanent or induced dipoles and

contains most of the adjustable parameters to describe concentrated aqueous

electrolyte solutions and organic-inorganic mixtures. The original AIOMFAC

model by Zuend et al. (2008) has been extended and re-parameterised to

include organic-inorganic interactions of most of the functional groups typi-

cally present in atmospheric organic compounds (carboxyl, hydroxyl, ketone,

aldehyde, ether, ester, alkyl, aromatic carbon-alcohol, and aromatic hydrocar-

bon) (Zuend et al., 2011). In addition, Zuend and Seinfeld (2012) introduced

the functional groups hydroperoxide, peroxyacid, and peroxide, including es-

timated interaction parameters with the inorganic ions of the model. For

further details of the thermodynamic description of the LR and MR interac-

tions within the Pitzer-like part of AIOMFAC we refer to Zuend et al. (2008,


2011). The interactions among non-charged species (organic molecules and

water) are calculated in the SR part of AIOMFAC.

3.2.1 Group-contribution method

A group contribution concept similar to UNIFAC has been adopted for the

AIOMFAC model. According to the group contribution concept, it is assumed

that the system is composed of combinations of functional groups instead of

whole molecules. The advantage of applying the group contribution method

is that a very large number of organic compounds can be defined using the

various combinations of a limited number of functional groups. In accordance

to the UNIFAC model, the functional groups are further classified into the

so called main groups and subgroups for their application in different model

parts (Fredenslund et al., 1975; Marcolli and Peter, 2005; Zuend et al., 2008,

2011). The main groups cover subgroups of the same functionality that only

differ by the number of hydrogen atoms. The subgroup classification of a

variety of organic compounds can be found in Table 3.1.

3.2.2 Short-range contribution

As in the UNIFAC model, in the SR part of AIOMFAC, activity coefficients

of a mixture component j are in general expressed as the sum of contributions

of a combinatorial part (denoted by superscript C), which accounts for the

size and shape of the molecule, and the residual part (denoted by superscript

R), which reflects the residual contribution from intermolecular (inter-group)

interactions (Fredenslund et al., 1975; Marcolli and Peter, 2005; Zuend et al.,

2008).

ln γSR,(x)j = ln γC

j + ln γRj (3.4)

The expression for the combinatorial part of UNIFAC is (Fredenslund et al.,

1975; Zuend et al., 2008):


ln γCj = ln

Φjxj

+z

2qj ln

Θj

Φj+ lj −

Φjxj

∑j′

xj′ lj′ (3.5)

where

Φj =rjxj∑

j′rj′xj′

; Θj =qjxj∑

j′qj′xj′

(3.6)

and

rj =∑t

ν(j)t Rt; qj =

∑t

ν(j)t Qt; (3.7)

lj =z

2(rj − qj)− (rj − 1). (3.8)

In these equations, xj is the mole fraction of component j and ν(j)t denotes

the number of subgroups of type t present in a formula unit of component j.

The relative van der Waals subgroup volume and surface area are given by

Rt and Qt respectively. The lattice coordinate number z is typically assumed

to be a constant set to z = 10 (Fredenslund et al., 1975). Relative subgroup

volume and surface area parameters published by Hansen et al. (1991) are used

for the neutral species. Enthalpic interaction contributions are considered in

the residual part of UNIFAC. The residual part of the activity coefficient of

component j is given by (γRj ):

ln γRj =

∑t

ν(j)t

[ln Γt − ln Γ

(j)t

], (3.9)

where Γt is the group residual activity coefficient in the mixture, while Γ(j)t

represents the one in a reference liquid containing only compound j. ν(j)t


is the number of subgroups of type t in molecule j. The residual activity

coefficient of subgroup t is:

ln Γt = Qt

1− ln

(∑m

ΘmΨm,t

)−∑m

ΘmΨt,m∑n

ΘnΨn,m

, (3.10)

where

Θm =QmXm∑nQnXn

. (3.11)

In these expressions Θm is the relative surface area fraction of subgroupm, Xm

is the mole fraction of m in the mixture. The standard UNIFAC temperature-

dependent interaction between the subgroups m and n is given by Fredenslund

et al. (1975):

ln Ψn,m = −[Un,m − Un,n

RT

](3.12)

where Un,m is a measure of change in the molar Gibbs free energy due to

interaction between subgroups m and n. Eq. 3.12 is typically represented in

the more compact form of Eq. 3.13.

ln Ψn,m = −[an,mT

](3.13)

Due to the formulation of Eq. 3.12, with equivalent differences for the inter-

actions between subgroups m and n (with the difference Um,n − Um,m), the

main group interaction parameters an,m are unsymmetrical i.e., an,m 6= am,n.

Note that all interaction parameters are only resolved on the main group level,

i.e., all subgroups of a certain main group interacting with a subgroup of a

different main group will have the same interaction parameter. Hence, we

refer to the set of an,m as main group interaction parameters. In standard

UNIFAC, the an,m interaction parameters of organic solutions were estimated

using a large database of experimental vapour-liquid equilibrium (VLE) and

liquid-liquid equilibrium (LLE) datasets. This approach leads to satisfying


predictions for vapour-liquid equilibria, but reliable simultaneous description

of VLE, LLE, solid-liquid equilibria (SLE), and molar enthalpies of mixing

(hE) can often not be obtained (Lohmann et al., 2001). In order to overcome

these deficiencies of the standard UNIFAC, modified UNIFAC (Dortmund)

uses three main group interaction parameters in the residual part to improve

predictions of activity coefficients over a wider range of temperatures and dif-

ferent types of phase equilibria (Gmehling et al., 1993; Lohmann et al., 2001;

Jakob et al., 2006):

ln Ψn,m = −[an,m + bn,mT + cn,mT

2

T

]. (3.14)

In modified UNIFAC (Dortmund) the relative van der Waals volume (Rt)

and surface (Qt) coefficients for the structural groups are not calculated from

molecular parameters as in the standard UNIFAC approach; rather, they are

fit together with the interaction parameters (an,m, bn,m, cn,m) to experimen-

tal data.

The AIOMFAC model is aimed for a wide range of applications, including the

calculation of solid-liquid equilibria and other thermodynamic phase equilib-

ria. The temperature dependence of these equilibria is related to the molecular

interaction of the components in the liquid phase. Hence, the temperature

dependence of chemical reaction equilibria and phase equilibra are described

by the same thermodynamic functions and we can express them with param-

eterisations for the temperature dependence of reaction equilibria. According

to Clarke and Glew (1966), if the equilibrium constant Kp of a chemical re-

action or exchange process is a function of temperature, the changes in the

standard thermodynamic functions, i.e., change in molar Gibbs free energy

∆G, change in molar enthalpy ∆H and change in molar heat capacity ∆Cp

are directly related to Kp (by definition) and are well-behaved functions of

T . The relationship for the equilibrium constant Kp and temperature T ,

when excluding higher order derivatives of molar heat capacity change with

temperature, are given by (Clarke and Glew, 1966):

R lnKp = −∆G

T

T+∆H

T

[1

T− 1

T

]+∆C

pT

[TT− 1 + ln

T

T

], (3.15)


where T is a reference temperature at which the changes in ∆G, ∆H and

∆Cp are determined or known. In order to better describe activity coeffi-

cients at low (and high) temperatures while preserving compatibility with the

already estimated values of the interaction parameters an,m at room temper-

ature, we introduce a similar but slightly modified expression for Ψn,m. We

define the temperature dependent interaction potential in AIOMFAC as

ln Ψn,m = −an,mT

+ bn,m

(1

T− 1

T

)+ cn,m

[(TT− 1

)+ ln

T

T

](3.16)

with the reference temperature T = 298.15 K. The first term is similar as in

standard UNIFAC, but slightly different from the equivalent term in Eq. 3.15,

due to the use of actual temperature T instead of reference temperature T for

consistency with standard UNIFAC/AIOMFAC. This term in Eq. 3.16 there-

fore includes both changes in ∆GT

as well as a part of the changes related

to ∆HT

(note: this is obvious when considering a hypothetical, very high

reference temperature for the second term on the right hand side of Eq. 3.16).

The second term includes the change in enthalpy and in addition acts as a

linear correction term for parameters an,m at temperatures different from the

reference temperature. The third term accounts for the contribution related

to the heat capacity change of a main group interaction, whose importance

increases for temperatures far away from the reference temperature.

We use a database of experimental thermodynamic equilibrium data for or-

ganic and organic-water systems (see (Sect. 3.3) and (Sect. 3.4)), covering

a wide temperature and concentration range, to determine simultanously the

AIOMFAC group interaction parameters bn,m and cn,m for pertaining organic

funcational groups. To preserve compatibility with the AIOMFAC model ver-

sion of (Zuend et al., 2011), and its fitted organic-inorganic interaction pa-

rameters at room temperature, all group-interaction parameters am,n are kept

the same, which implies that the performance of AIOMFAC at 298.15 K will

not be altered by the improved three-parameter temperature-dependence pa-

rameterisation. With a goal to describe a wide variety of organic compunds

at relevent atmospheric temperatures, we focus on the aqueous systems of

oxidized organics at lower temperatures. The temperature dependence for-

mulation given by Eq. (3.16) will at this point only be parametrised for interac-


tions between the UNIFAC main groups alkyl (CHn), specific variants of alkyl

groups in alcohols, such as (CH[alc]n ), (CH

[alc−tail]n ), and (CH

[OH]n ), hydroxyl

(OH), carboxyl (COOH), water (H2O), ketone (CHnCO), aldehyde (CHO),

ether (CHnO), ester (CCOO), alkenyl (C=C), aromatic carbon (ACHn), and

aromatic carbon alcohol (ACOH) (a phenol group). For all other group inter-

actions not considered, bn,m and cn,m are set to zero so that Eq. (3.16) reduces

to Eq. (3.13). The rules for the use of specific alkyl groups, are described be-

low. With this approach, an improved description of activities for organic

systems at low temperatures can be achieved, while maintaining compatibil-

ity with standard UNIFAC, hence, preserving the applicability of AIOMFAC

to a wider range of functional groups.

The UNIFAC functional groups in AIOMFAC include some modifications

with respect to standard UNIFAC to better describe the specific proper-

ties of organic aerosol constituents, which typically are molecules composed

of several (polar) functional groups. Therefore a more detailed descrip-

tion of alcohol/polyol group interaction parameters published by Marcolli

and Peter (2005) was implemented, where the relative positions of the OH

functional group as well as those of neighboring alkyl groups are taken

into account (Zuend et al., 2011). According to this approach, water-alkyl

and water-hydroxyl group interaction parameters for alcohols/polyols are

treated specifically, while keeping the alkyl-hydroxyl interaction parameter

unchanged in order to maintain the performance of AIOMFAC in case of wa-

ter free alkane/alcohol systems compatible with standard UNIFAC. Except

for CH[OH]n groups directly bonded to an OH group, standard UNIFAC CHn

groups are used for alkyl groups in multifunctional molecules that contain

hydroxyl groups combined with different other functional groups. Another

difference with respect to standard UNIFAC is that we use the parameters of

Peng et al. (2001) for the interaction of the COOH group with the OH group

and the H2O group. The use of these modified UNIFAC group interaction

parameters leads to improvements for certain aqueous systems of alcohols, di-

carboxylic and hydroxycarboxylic acids, while being compatible with the use

of standard UNIFAC parameters for other group interactions, as described in

more detail in Zuend et al. (2011).

3.3. Experimental data 71

3.3 Experimental data

Reliable estimation of group interaction parameters and temperature depen-

dence relies on a comprehensive database covering a wide variety of com-

pounds consisting of the targeted functional groups, with consideration of a

large temperature range. In order to establish such a database, an extensive

literature search was carried out. The DETHERM databank (Gesellschaft fur

Chemische Technik und Biotechnologie e.V., www.dechema.de), which offers

the world’s largest collection of thermodynamic mixture data was used to

check the completeness of the literature search and to directly purchase data

for which the original publication was not easily accessible.

Figure. 3.1 provides an overview of the database implemented in this study.

The matrix lists the number of datasets at temperatures substantially dif-

ferent from 298 K available for each main group pair interaction. The green

bars indicate the maximum number of overall datasets including all datatypes

available for each main group interaction pair. For each interaction pair, the

highest temperature (red shaded boxes) and lowest temperature (blue shaded

boxes), for which datapoints are available is indicated. The database overall

consists of 677 datasets covering different data types, for monofunctional and

multifunctional organic molecules in aqueous and water-free mixtures of bi-

nary and ternary systems. Table 3.1 lists the datasets and the data types used

for determining the main group interaction parameters (bn,m and cn,m) in the

SR part of the AIOMFAC model. The table lists the mixture components,

main groups, chemical formula (subgroups), data type, number of data points,

temperature range, assigned initial weighting used in the model parameter fit,

and the data source. Tables reporting new water activity measurements are

provided in the Appendix. Different data types and their processing for use

with the model parameterisation are described in the following.

3.3.1 Solid-liquid equilibrium

Most low temperature data available for the model parameterisation are bi-

nary SLE data with water and an organic component. SLE data can be


1 2 3 7 8 9 10 11 13 65 66 67 68 69

groupno main groups (CHn) (C=C) (ACHn) (H2O) (ACOH) (CHnCO) CHO (CCOO) CHnO (COOH) (CHn[alc]) (CHn[alc-tail]) (CHn[OH]) (OH) [aldehyde] [ether]

1 (CHn) 2 (C=C) set count: 26 T_low [K]: 191 T_high [K]: 2883 (ACHn) set count: 26 T_low [K]: 197 140 T_high [K]: 393 1707 (H2O) set count: 239 27 21 200 T_low [K]: 187 191 214 230 T_high [K]: 447 338 455 2608 (ACOH) set count: 7 23 17 290 T_low [K]: 298 214 214 320 T_high [K]: 383 455 455 3509 (CHnCO) set count: 91 10 46 2 380 T_low [K]: 154 225 198 323 410 T_high [K]: 423 348 406 333 44010 (CHO [aldehyde]) set count: 28 2 8 2 470 T_low [K]: 143 353 278 296 500 T_high [K]: 393 393 367 32911 (CCOO) set count: 69 3 45 2 6 2 lowest T[K] : 143 T_low [K]: 163 298 208 298 263 313 highest T[K] : 484 T_high [K]: 439 353 439 348 348 32313 (CHnO[ether]) set count: 105 9 81 6 2 2 7 T_low [K]: 148 197 187 214 322 288 208 T_high [K]: 423 383 423 383 330 304 40265 (COOH) set count: 140 27 12 111 7 24 3 10 21 T_low [K]: 173 191 214 191 214 173 295 243 194 T_high [K]: 447 338 387 447 348 391 386 366 36066 (CHn[alc]) set count: 12 2 96 1 4 3 5 T_low [K]: 288 185 196 313 288 319 288 T_high [K]: 399 313 442 313 333 399 32367 (CHn[alc-tail]) set count: 63 13 52 3 16 10 8 15 15 15 T_low [K]: 143 160 200 243 154 143 163 148 241 215 T_high [K]: 423 350 384 348 423 353 351 400 389 36768 (CHn[OH]) set count: 157 26 22 245 9 20 10 15 84 46 100 116 T_low [K]: 143 191 160 187 214 154 143 163 148 191 185 143 T_high [K]: 423 288 383 484 383 423 353 402 423 389 442 42369 (OH) set count: 159 26 22 247 9 20 10 15 84 48 100 116 323

T_low [K]: 143 191 160 187 214 154 143 163 148 191 185 143 143 T_high [K]: 423 288 383 484 383 423 353 402 423 389 442 423 484

Tem

pera

ture

scal

e (K

)

Figure 3.1: Database distribution for the water ↔ organic and organic ↔ organic

interaction parameters. The table lists the total number of datasets (set count)

available for each main group interaction at temperatures substantially different from

the chosen reference temperature (T = 298.15 K). The total number of datasets

available for each main group interaction pair are visualized by the green coloured

bars. The percentile-wise colouring is used to visualize the lowest temperature (Tlow,

blue colour) and the highest temperature (Thigh, red colour) (units of K) of the data

points available for each main group interaction pair.

obtained by measuring the melting point depression of solutes as a function

of solution composition. Consequently, at maximum two data points for each

temperature level can be acquired for binary systems, corresponding to the

points on the melting curves of the two components. However, most datasets

collected provide only data for one component forming a solid in equilibrium

with the remaining liquid solution. In many of these cases, hexagonal water

ice is the solid phase. Since the temperature dependence of water activity

(aw) of aqueous solutions in equilibrium with ice is well known, an accurate

determination of the activity coefficients (γ(x)w = aw

xw) of water as a function


of solution composition and temperature using SLE data is possible. At SLE,

the activity of water in a solution with organic mole fraction xorg at ther-

modynamic equilibrium with ice, aSLEw (T, p), is given by (Koop et al., 2000):

aSLEw (T, p) = exp

[µSw(T, p)− µ,L

w (T, p)

RT

], (3.17)

where µSw(T ) and µ,Lw (T ) are the pressure and temperature dependent chem-

ical potentials of ice (superscript S) and pure liquid water( superscript , L),

respectively. At ambient pressures, neglecting the pressure dependence of the

liquids and solids is well justified.

µSw(T )− µ,Lw (T ) = 210368 + 131.438 T

−3.32373× 106 T−1 − 41729.1 ln(T ). (3.18)

The parameterisation in Eq. (3.18) represents the thermodynamically con-

sistent function valid at low (ambient) pressure in the temperature range

150 < T < 273 K (Koop et al., 2000).

The activity of dissolved organic component in equilibrium with its pure crys-

talline solid can be calculated using the following relationship (Prausnitz et al.,

1986; Domanska et al., 2009):

lnxiγSLEi = −∆hm,i

RT

(1− T

Tm,i

)− ∆htr,i

RT

(1− T

Ttr,i

)+

∆cp,m,iR

[(1− T

Tm,i

)+ ln

T

Tm,i

].(3.19)

where ∆hm,i is the molar enthalpy of fusion (melting, subscript m), ∆htr,i is

the molar enthalpy of a certain solid-solid phase transition, ∆cp,m,i is the mo-

lar heat capacity change upon fusion at constant pressure, Ttr is the solid-solid

phase transition temperature and Tm,i the melting temperature of pure com-

ponent i. The second term is only of significance when there is a solid-solid

phase transition (change of polymorphic form) between T and Tm,i. Equa-

tion 3.19 uses the simplification that the melting temperature and the triple

point temperature of an organic compound are relatively close at atmospheric


pressure. For obtaining activity coefficients from experimental data at given

temperatures and mole fractions (xorg, T ), Eq. 3.19 can be solved for the

SLE organic activity and/or activity coefficients. Pure component physico-

chemical properties such as ∆hm,i and ∆cp,m,i are obtained from tabulated

experimental data (Dean, 1999) and (Domalski and Hearing, 1996).

3.3.2 Water activity measurements

Water activity measurements were conducted for aqueous organic solutions

with an Aqualab dew point water activity meter (Model 3TE, Decagon De-

vices, USA), which enables water activity measurements within the tempera-

ture range from 289− 313 K for several concentrations at each different tem-

perature levels. Water activity data for measured binary aqueous organic bulk

solutions are tabulated in the Appendix. Additional measurements of aque-

ous multifunctional organic solutions are provided in Ganbavale et al.(2014).

Measured water activities were then used directly for the AIOMFAC-P3 pa-

rameter determination.

3.3.3 Liquid-liquid equilibria data

The equilibrium state between coexisting liquid phases is known as liquid-

liquid equilibrium (LLE). Liquid-liquid equilibria are useful as a source of data

for systems containing relatively hydrophobic organic compounds and water,

with a miscibility gap that depends on temperature and mixture composition.

In general, multicomponent systems may form more than two phases. For

salt-free aqueous organic systems with two coexisting liquid phases, usually

one phase is an aqueous (water-rich) phase while the other is an organic-rich

phase. Most available experimental LLE data has been measured relatively

close to room temperature and is useful for a better description of the phase

behaviour. However, for the purpose of our new parameterisation of AIOM-

FAC with regard to low temperatures far from room temperature, the LLE

data tend to be less useful than, e.g., SLE data. We use the tie-line data

from LLE measurements, which represents the composition of the two liquid


phases in equilibrium at a certain temperature. Initial mixture composition of

experimental tie-lines are used as input for computation of LLE phase sepa-

ration in order to compare the AIOMFAC model with the experimental data.

A direct calculation and comparison of activities in coexisting phases is pos-

sible at experimental LLE compositions. This data type can be implemented

in the model fit by minimizing the relative differences between the activities

of the components in the two liquid phases. We use the method introduced

by Zuend et al. (2011) for the comparison of calculated relative activity de-

viations between the activities of components j present in the two phases.

An initial mixture composition with mole fraction xinitj of component j on a

unstable/metastable point on a tie-line is generated by:

xinitj =

1

2

(xαj + xβj

)(3.20)

where xαj and xβj are the experimental compositions of the two liquid phases

α and β at equilibrium. This allows a direct comparison of the measured and

calculated phase compositions. According to the reference state definitions of

AIOMFAC, different independent components should have the same activities

in coexisting phases. i.e. a(x),αj = a

(x),βj .

Forward computations of LLE were also performed using the method of

(Zuend and Seinfeld, 2013), particularly for the graphical comparison of mea-

sured and predicted tie-line LLE data. For more details about the LLE com-

putations with AIOMFAC we refer to Zuend et al. (2010) and Zuend and

Seinfeld (2013); the specific method used for fitting LLE data based on rela-

tive activity deviations is described in more detail in Zuend et al. (2011).

3.3.4 Vapour-liquid equilibria

VLE data represent the temperature and pressure conditions where a liquid

(mixture) and its vapour(s) (gas phase) are in equilibrium with each other.

The VLE data is usually obtained by performing measurements either at

isobaric or isothermal conditions. VLE data considered in the model include

binary water + organic systems and binary data for water-free organic (1) +


organic (2) systems. Since isobaric measurements are usually conducted at 1

atm (= 101.325 kPa) pressure by measuring the boiling point temperature,

they typically provide data at relatively high temperatures. In order to be

used in the model parameterisation, the composition of the liquid in terms

of mole fraction xj of each component j, the composition of the gas phase in

terms of mole fraction yj and the total pressure p of the gas phase have to

be stated or need to be derived from the data source. VLE data provide the

composition dependence of activity coefficients. Assuming that the gas phase

can be treated as an ideal gas mixture, activity coefficients of the components

in the solution can be calculated by modified Raoult’s law:

γ(x)j =

pjp0jxj

, pj = yjp (3.21)

where pj is the partial pressure of component j, and poj (T ) is the pure liq-

uid component saturation vapour pressure calculated at the measurement

temperatures using the Antoine equation with coefficients from the Landolt-

Bornstein database (Dykyj et al., 2000), from Yaws et al. (2005) or, in some

cases, the p0j (T ) are directly available from the reference of the experimental

VLE data. Except for monocarboxylic acids such as formic, acetic, and pro-

pionic acid, which exhibit significant gas phase association (dimers, trimers),

assuming an ideal gas mixture for the total pressure and temperature ranges

of the data is acceptable. Other exceptions include certain diols and triols,

e.g., glycerol, which show moderate non-ideality in the gas phase, requiring

fugacity corrections. For mono-alcohols, fugacity corrections of the gas phase

did not lead to substantial changes in activity coefficients, due to the form of

(Eq. 3.21) (where the ratio of partial pressure and saturation vapour pressure,

both similarly affected by association effects, cancel most of the non-ideality),

and were typically ignored. To account for the gas phase dimerisation of car-

boxylic acids we obtain the monomer partial pressures using the dimerisation

equilibrium coefficients from Tsonopoulos and Prausnitz (1970). The proce-

dure for calculating experimental activity coefficients using this dimerization

correction is described in more detail in Zuend et al. (2011).

3.4. Objective function and model parameter estimation 77

3.4 Objective function and model parameter

estimation

Organic-organic and organic-water main group interactions are parametrised

in the SR part of AIOMFAC. The model parameter determination procedure

involves simultaneous fitting of the various group interaction parameters to

available thermodynamic phase equilibria data (see the database overview in

Fig. 3.1). In order to ensure intercomparability of different thermodynamic

quantities and with due consideration of the various aspects of the uncertainty

in measurements and the group-contribution concept of the model, we use the

following general objective function (Fobj), subject to minimization (Zuend

et al., 2011):

Fobj =∑d

∑u

wd,u

Qcalcd,u −Qref

d,u∣∣∣Qrefd,u

∣∣∣ + Qtold,u

2

. (3.22)

Here, wd,u is the weighting value of a data point and the sums cover all data

points u in all datasets d considered. Qrefd,u is a reference quantity, directly

determined from experiments (e.g., measured water activity value at a cer-

tain T and xw) or derived from measurements by means of thermodynamic

relations, e.g., SLE water activity on the ice melting curve at a specific tem-

perature. Qcalcd,u represents the corresponding quantity calculated with the

model at the given conditions. Qtold,u is a tolerance quantity (> 0) which rep-

resents the measurement uncertainty or model sensitivity and has the same

units as Qrefd,u. During the iterative fitting of the model parameters, we use the

AIOMFAC model (with the current parameter set at that iteration step) to

calculate the model activity sensitivity with respect to an assumed represen-

tative uncertainty in absolute mixture composition, a mole fraction tolerance

set to: xtol = 0.01. We refer to Zuend et al. (2011) (their Section 3.3) for

a detailed description of how the model sensitivity is calculated. We use the

AIOMFAC model to calculate the effect of a tiny change in composition on

the activity coefficients of the different mixture components by means of a

total molar derivative. Technically, this is done by scaling and summation of


the partial derivatives of the activity coefficients at a given solution compo-

sition by means of finite differences in molar composition (Eq. 10 of Zuend

et al. (2011).

3.4.1 Dataset weighting and temperature range

Both experimental uncertainties and model deficiencies need to be considered

while determining the main group interaction parameters. The measured

experimental quantities have some level of random and systematic errors,

which may also depend on mixture composition, rendering some data points

more reliable than others. This is considered during the parameter estimation

procedure by giving appropriate weighting to the datasets and by data point-

specific tolerance quantities computed in parallel from the model sensitivities

as the model fit progresses. With the aim of reducing a disproportionate

influence of datasets with a large number of data points, as well as preventing

an immoderate high weighting of datasets with a small number of data points,

Zuend et al. (2011) propose a simplified procedure of assigning individual

weighting to datasets on the basis of data type and number of data points Ndin a dataset:

wd,u =

winitd if Nd ≤ η,

winitd × η

Ndif Nd > η,

(3.23)

where winitd is an initial weighting of dataset d on the basis of its temperature

range, data type, and, in certain cases, additional expert judgement of its reli-

ability. η is a characteristic number of data points per dataset. The weighting

of individual data points that are part of large datasets can be reduced by

multiplication with η/Nd. In this work, we keep η = 10 as in Zuend et al.

(2011). Initial weightings assigned to datasets for the model fit are given in

Table 3.1.

With the goal of fitting the AIOMFAC model parameters for a better descrip-

tion of activities at (low/high) temperatures far from room temperature, a set

of rules was applied to assign initial weightings based on data type and the

temperature range covered. Low temperature aw data were assigned an ini-

3.4. Objective function and model parameter estimation 79

tial weighting winitd =5.0 while the SLE organic activity (SLE(org)) datasets

(i.e., SLE data where an organic compound forms the solid in equilibrium

with the liquid solution) are given an initial weighting of winitd =0.2 because

of the lower reliability of deriving solute activities using Eq. 3.19 compared

to calculating water activities with Eq. 3.17. Relying on the water activity

parameterisation of homogeneous freezing temperatures in aqueous solutions

(Koop et al., 2000), freezing point depressions were also used as data source

for parameter fitting. The aw from DSC measurements at homogeneous freez-

ing temperatures (Thom) are assigned winitd =1.0 (considering some uncertain-

ties associated with the Thom determination from DSC measurements). The

weighting of all types of datasets close to room temperature (289 - 307 K)

are set to zero to keep AIOMFAC unchanged around room temperature and

guarantee consistency with functional groups which were not included in the

new three-parameter temperature-dependence parameterisation. The LLE

and VLE datasets are assigned an initial weighting of winitd =1.0. However,

datasets showing large scatter or inconsistencies with other comparable data

(direct comparison of measurements or comparable via the thermodynamic

relations underpinning AIOMFAC) were given lower or even zero weightings.

To obtain parameters representing the best simultaneous description of all

phase equilibria, thermodynamically inconsistent data have been excluded

from the parameter fitting process (but only after test runs and a careful data

quality review).

For determining the set of main group interaction parameters, i.e., the set

of bm,n and cm,n values, where m,n represent all combinations of different

main groups, we use a set of selective criteria by considering the temperature

range of available experimental data. These criteria are separately applied to

each group interaction pair as follows: the bm,n values are determined only

if: ∆Tlow (= |Tlow − T|) or ∆Thigh = (|Thigh − T|) > 40 K and ∆T =

(|Tlow − Thigh|) > 40 K, where T = 298.15 K is the reference temperature.

Similarly, the cm,n parameters for the main groups are determined only if

∆Tlow > 80 K or ∆Thigh > 80 K and if ∆T > 80 K. In addition, we set limits

on the expected values of the fitted parameters. The three terms on the right

hand side of Eq. 3.16 contain parameters of different thermodynamical mean-

ing (see Eq. 3.15) and different magnitude. The terms containing am,n and


bm,n are associated with changes of molar enthalpy over a certain tempera-

ture difference, while cm,n is related to changes in the molar heat capacity at

constant pressure (hence, accounting for the change of the change of enthalpy

with temperature). These thermodynamic quantities tend to be of different

magnitude (molar heat capacity changes are roughly two to three orders of

magnitude smaller in value). Hence, the expected values and set limits on the

parameters bm,n and cm,n are quite different for these reasons. Symmetric pa-

rameter bounds for permissible values of bm,n are set to max [am,n, 200], while

the numerical limits on cm,n are set to ±max [4× 10−3 × |max [am,n, 200]|].With the implementation of these parameter bounds and based on the re-

duced set of experimental data fulfilling the selection criteria, 150 new inter-

action short-range parameters were determined simultaneously for 14 func-

tional main groups. Due to the high dimensionality, and nonlinear coupling

of the fit parameters, the minimization problem is a challenging task for any

global optimization method. For the parameter optimization, it is sufficient

to find a ‘good’ local minimum, rather than the global minimum. As a part

of data quality control and to avoid that a few datasets dominate the pa-

rameter optimization due to potential numerical issues or other reasons, such

as inconsistent datasets and outliers, a large number of trial parameter opti-

mization runs were carried out. To solve the parameter optimization problem,

we use the formulation by Zuend et al. (2011) which uses a combination of

algorithums to solve the parameter optimization problem. First, the Best-of-

Random Differential Evolution (BoRDE) algorithm Lin et al. (2011) is used

to explore the parameter space and to locate a minimum of Fobj subject to the

polarity series constraints. Second, the global trust region method BOBYQA

of Powell (2009) is applied to further refine the solution. Finally, the Downhill-

Simplex algorithm by Nelder and Mead (1965) is used to fully converge to the

minimum. More details are given in (Zuend et al., 2011).

During trial runs, the contributions of the individual datasets to the objective

function value (Eq. 3.22) were used to identify potential inconsistencies among

datasets, errors in data calculations and conversion or the implementation in

the model. This allowed us to establish a high level of data quality, correct

mistakes (e.g. typing errors) and compare thermodynamic data from different

types of experiments and references for general consistency. Table 3.2 provides

3.5. Results and Discussion 81

the final values of the determined organic main group interaction parameters.

For comparison and completeness, the values of am,n parameters, which were

preserved in the new UNIFAC parametrisation are listed as well. All main

group interaction parameters bm,n and cm,n, for which the database does not

satisfy our criteria concerning temperature range and data availability are set

to zero.

3.5 Results and Discussion

The new temperature dependence parameterisation is applied to aqueous

organic and water-free organic solutions covering a wide concentration and

temperature range. In this section, we compare and discuss the model per-

formance of the new AIOMFAC-P3 version, with AIOMFAC-P1 (original

AIOMFAC version) for a selection of aqueous organic mixtures and water-

free organic mixtures. The new AIOMFAC-P3 parameterisation for the tem-

perature dependence of activity coefficients shows an overall improvement of

25 % in terms of Fobj in comparison to AIOMFAC-P1. As stated earlier,

AIOMFAC-P1 uses the temperature-dependence expression of standard UNI-

FAC and represents the AIOMFAC performance using only am,n interaction

paramters. The AIOMFAC-P3 model version uses all the three parameters

i.e., am,n, bm,n and cm,n, where applicable, with our new expression for the

temperature dependence of group interactions.

It should be noted that the models were not just fitted to these datasets; rather

the figures show a few examples, and the AIOMFAC-P3 model is, of course,

based on the simultaneous optimisationt of all fit parameters to the complete

database. For each individual system, a specific fit of either AIOMFAC-P1 or

-P3 could better represent those datasets shown, but that is not the goal of a

versatile group-contribution model.


3.5.1 Aqueous organic mixtures

Figure. 3.2 shows the comparison of aqueous 1,2-ethanediol solutions using

the AIOMFAC-P1 and AIOMFAC-P3 models. Panels (a, b, c) represent the

AIOMFAC-P1 performance while panels (d-f) represent the corresponding

AIOMFAC-P3 results. The low-temperature SLE data (panels a and d) are

relatively well represented by both AIOMFAC-P1 and AIOMFAC-P3. The

high-temperature VLE data are much better represented by AIOMFAC-P3 in

comparison to AIOMFAC-P1. Panels (c) and (f) show predicted water activ-

ities covering the full concentration space from pure water to pure organic for

12 different temperature levels between 150 K and 480 K. Over all concen-

trations, aqueous 1,2-ethanediol indicates a small temperature dependence.

In comparison to the AIOMFAC-P1, the resulting temperature dependence

from low to high xorg is relatively small in the AIOMFAC-P3 case.

Figure. 3.3 compares the model performance of AIOMFAC-P1 and

AIOMFAC-P3 for SLE and VLE experimental data of aqueous acetic acid

systems. The SLE data is well represented by both AIOMFAC-P1 and

AIOMFAC-P3 (Figure. 3.3 a, d). At higher temperatures, covered by VLE

data, the AIOMFAC-P3 prediction is clearly in better agreement with the

experimental data than the AIOMFAC-P1 calculation. However, both model

parameterisations tend to underestimate the activity coefficients of water and

acetic acid, particularly at high and low mole fractions of water. Overall

for this system, the extended description of the temperature dependence of

activity coefficients in AIOMFAC-P3 allows a relatively good representation

of observations at low and high temperatures, while AIOMFAC-P1 shows

quite large deviations at higher temperatures. The temperature dependence

predictions for the temperature range 150 - 480 K are given in panel (c, f).

AIOMFAC-P1 predicts less pronounced temperature dependence at higher

temperatures, 360 - 480 K, while AIOMFAC-P3 predicts an overall wider

temperature dependence of water activity over the whole temperature range.

This steeper slope of changes in water activity with temperature seems to be

necessary to reproduce both VLE and SLE data for this system and other

systems containing compounds with common functional groups.

Figure. 3.4 shows measured SLE data for the malonic acid + water system


3F3 3F2 3F4 3F6 3F8 1F3

mole fraction xXH2OP

3F3

3F2

3F4

3F6

3F8

1F3

activity

aXxP

H2O X1P A 1I2MEthanediol X2P

Temperature range: 225 MM 267 K

EXP: 1I2MethanediolAwater γw

EXP: 1I2MethanediolAwater γorg

AIOMFAC γw

AIOMFAC γorg


153 483KTemperature range: MM

3F3 3F2 3F4 3F6 3F8 1F3

xorgX2P

3F3

3F2

3F4

3F6

3F8

1F3

activity

aw

AIOMFACMP1

3F3 3F2 3F4 3F6 3F8 1F3


3F3

3F2

3F4

3F6

3F8

1F3

activity

aXxP

EXP: 1I2MethanediolAwater

AIOMFAC water activity aw

AIOMFAC organic activity aorg

ideal aw

AIOMFACMP3


Temperature range: 225 MM 267 K

3F3 3F2 3F4 3F6 3F8 1F3


3F3

3F2

3F4

3F6

3F8

1F3

activitycoeffFγXxP

EXP: 1I2MethanediolAwater γw

EXP: 1I2MethanediolAwater γorg

AIOMFAC γw

AIOMFAC γorg


153 483KTemperature range: MM

223

243

263

TXKP

223

243

263

TXKP

363383433423443

TXKP

363383433423443

TXKP

a b c

d e f

Figure 3.2: Measurements for 1,2-ethanediol + water solutions, corresponding cal-

culations of AIOMFAC-P1 in (panels a-c) and AIOMFAC-P3 (panels d-f). The

coloured curves in panels (c, f) represents the temperature dependence of water ac-

tivities predicted for the range from 150 - 480 K. Panels (a, d): Low temperature

experimental SLE data (crosses) are compared with the predictions for water ac-

tivity at the same compositions and temperatures (blue circles). Predictions of the

corresponding organic activities are shown as well (green triangles). The dashed line

represents the hypothetical water activity of an ideal mixture. The error bars rep-

resent the model sensitivity to a composition variation by xtol = 0.01. The middle

panels (b and e) show the model predictions of the activity coefficients compared to

VLE data covering temperatures significantly higher than room temperature. The

temperature of the individual data points are given in the boxes below the main pan-

els. Experimental data: Ott et al. (1972) and Gmehling and Onken (2003a).

84 Chapter 3. Improved AIOMFAC temperature dependenceAIOMFACuP1

AIOMFACuP3

a b c

d e f

K9K K92 K94 K96 K98 19K

mole fraction x_H2Od

K9K

K92

K94

K96

K98

19K

activity

a_xd

K9K K92 K94 K96 K98 19K


K9K

K95

19K

195

29K

295

activitycoeff9γ_xd

K9K K92 K94 K96 K98 19K

xorg_2d

K9K

K92

K94

K96

K98

19K

activity

aw

EXP: Acetic_acidTwater



ideal aw

EXP: acetic_acidTwater γw

EXP: acetic_acidTwater γorg

AIOMFAC γw

AIOMFAC γorg

K9K K92 K94 K96 K98 19K


K9K

K92

K94

K96

K98

19K

activity

a_xd

K9K K92 K94 K96 K98 19K


K9K

K95

19K

195

29K

295

activitycoeff9γ_xd

K9K K92 K94 K96 K98 19K

xorg_2d

K9K

K92

K94

K96

K98

19K

activity

aw

H2O _1d T Acetic_acid _2d

Temperature range: 249 uu 272 K








15K 48KKTemperature range: uu


15K 48KKTemperature range: uu

37K

38K

39K

T_Kd

37K

38K

39K

T_Kd

EXP: acetic_acidTwater γw

EXP: acetic_acidTwater γorg

AIOMFAC γw

AIOMFAC γorg

EXP: Acetic_acidTwater



ideal aw

Figure 3.3: Measurements for acetic acid + water solutions, corresponding calcu-

lations of AIOMFAC-P1 in and AIOMFAC-P3. The coloured curves in panels (c,

f) represents the temperature dependence of water activities predicted for the range

from 150 - 480 K. Panels (a, d): Low temperature experimental SLE data (crosses)

are compared with the predictions for water activity at the same compositions and

temperatures (blue circles). Predictions of the corresponding organic activities are

shown as well (green triangles). The dashed line represents the hypothetical water

activity of an ideal mixture. The error bars represent the model sensitivity to a

composition variation by xtol = 0.01. The middle panels (b and e) show the model

predictions of the activity coefficients compared to VLE data covering temperatures

significantly higher than room temperature. The temperature of the individual data

points are given in the boxes below the main panels. Experimental data: Faucon

(1910) and Narayana et al. (1985).


and its comparison with the predictions from AIOMFAC-P1 and AIOMFAC-

P3. In the panels a and c water is the component in equilibrium with ice

(hence the data describes the ice melting curve at different T and mixture

composition), while panels b and e show analogous data for the malonic acid

melting curve. The temperature ranges are slightly different, with the highest

temperature in the plots referring to the melting temperature of the pure com-

ponent or the SLE at the highest concentration of the organic, respectively.

The predicted aw shows slight deviations from the experimental data towards

lower water activities for both AIOMFAC-P1 and AIOMFAC-P3 (panels a, d).

The predicted aorg (in a range closer to and above room temperature) is well

represented in both AIOMFAC-P1 and AIOMFAC-P3 (b, e). No VLE data

is available for aqueous malonic acid at higher temperatures and hence could

not be compared. AIOMFAC-P3 predicts a larger temperature dependence

in comparison to AIOMFAC-P1 which shows a relatively small temperature

dependence of water activity at higher temperatures (panels c, f).

Figure. 3.5 shows an example of a binary system consisting of water and

2-butanone with a miscibility gap present over a large temperature and com-

position range. Both model parameterisations show deviations from the SLE

data (panels a, d). However, the AIOMFAC-P3 parameterisation clearly re-

duces the deviations from the experimental data in comparison to AIOMFAC-

P1, the latter showing deviations up to > 0.3 in aw at low water contents.

On the other hand, at the higher temperatures covered by VLE data, both

AIOMFAC-P1 (Fig. 3.5 b) and AIOMFAC-P3 (Fig. 3.5 e) are in good agree-

ment with the experimental data. A miscibility gap is also predicted by both

AIOMFAC parameterisations, although the width and temperature range of

the predicted phase separations do differ between the model results. Accord-

ing to the AIOMFAC-P3 prediction, a phase separation would occur in the

temperature range 150 to ≈ 390 K in the composition space bounded by one

of the local maxima of aw a horizontal line intersecting with the aw curve at

another xorg value. A miscibility gap is also found in the experiments and

is the reason why in panels (a, b, d, e) there are no data points in the mole

fraction range 0.35 < x(H2O) < 0.85.

Figure. 3.6 shows the model-measurement comparison for aqueous 2-

butoxyethanol. AIOMFAC-P1 and AIOMFAC-P3 show similar performance.


Both models are not in good agreement with the experimental data. Contrary

to the experimental data, both AIOMFAC-P1 and AIOMFAC-P3 predict aw> 1, implying a liquid-liquid phase separation over a wide range of tempera-

tures, explaining the reason for deviations in predicted water activity shown

in Fig. 3.6 (a, d) (see also local maxima in aw curves of panels c, d). Note that

the model predictions in these figures do not include phase separation compu-

tations on purpose, since the experimental data are for a homogenous single

phase and so are the model calculations here. Also, at higher temperatures

the activity coefficients of both water and 2-butoxyethanol show deviation

from experimental data (panels b, e). AIOMFAC-P3 (panel f) shows a larger

temperature dependence over the entire temperature range in comparison to

AIOMFAC-P1 (panel c).

3.5.2 Binary organic mixtures

Figure. 3.7 shows the water-free mixture of cyclohexanol + adipic acid. The

AIOMFAC-P3 prediction is in better agreement with the experimental data

than AIOMFAC-P1, which shows a positive deviation at lower mole fractions

of component 1 (cyclohexanol). In this binary system, the AIOMFAC-P3

parameterisation leads to a relatively large temperature dependence of the

activity of cyclohexanol, aorg(1), (panel d). In addition, with that parame-

terisation a phase separation occurs at lower xorg(2) values for temperatures

below ∼ 180 K. However, no phase separation is expected at higher temper-

atures, more relevant in the troposphere. AIOMFAC-P1 on the other hand

shows a much smaller temperature dependence and does not predict a phase

separation.

Measurements for water-free binary organic mixtures of ethanol + acetone

are shown in Fig. 3.8. The AIOMFAC-P3 predictions of the activities of ace-

tone are in a very good agreement with the experimental SLE derived data

(panel d), while AIOMFAC-P1 shows larger deviations from the experimental

data at these low temperatures (panel a). At high temperatures, the VLE

data for both AIOMFAC-P1 and AIOMFAC-P3 show similar results (Panel

b, d), with slightly larger deviations of γ(x)org2 (activity coefficient of acetone)

in AIOMFAC-P1. At temperatures higher than 300 K both AIOMFAC-P1


and AIOMFAC-P3 show a much smaller temperature dependence than for

the range below room temperature.

Figure. 3.9 shows a similar example, for ethanol + 3-heptanone mixtures. The

prediction from AIOMFAC-P3 is in relatively good agreement with the ex-

perimental SLE data, showing less deviations in 3-heptanone activities than

the results from the AIOMFAC-P1 calculations. Achieving better agreement

with the new (AIOMFAC-P3) parameterisation requires a larger temperature

dependence of the organic activities, particularly towards lower temperatures.

Figure. 3.10 shows the binary ethanol + diethyl ether system, where experi-

mental data are available for a temperature range of more than 200 K: from

149 K up to 378 K. Of course, additional data from other systems of our

database are also affecting the main group interaction parameters that are

necessary to describe this system with AIOMFAC-P3. Both models describe

the diethyl ether activity derived from SLE at low temperatures quite well

(panels a and d). AIOMFAC-P3 shows slight overprediction of the diethyl

ether activity, while AIOMFAC-P1 tends to underpredict the experimental

data. In contrast, at higher temperatures (∼ 350 to 380 K) covered by exper-

imental VLE data (panels b and e), the predicted γ(x)org2 both by AIOMFAC-

P1 and AIOMFAC-P3 are not in good agreement with the VLE experimental

data. The main reason for the observed deviations is due to inaccurately

predicted activity coefficients at infinite dilution (i.e., when one of the com-

pounds is present only as a tiny mole fraction in the solution) of the two

organic compounds at these temperatures. At infinite dilution conditions the

activity coefficients are dominated by subgroup properties in the UNIFAC

/ AIOMFAC model, so that the activity coefficient values are largely unaf-

fected by the new main group interaction parameterisation of AIOMFAC-P3

in comparison to AIOMFAC-P1. As is visible from Fig. 3.10 (c,f), particularly

at x(diethyl ether) > 0.4, the temperature dependence of ethanol activities

predicted by AIOMFAC-P3 is larger than the original one in AIOMFAC-P1.

The example of this system shows that it is not always possible to achieve

good model predictions for the full temperature range with the new treatment

of temperature dependence in AIOMFAC. For further improvements, other

model parts, such as the lattice constant (z), which is not really a constant,


would need to be considered for the introduction of additional, physically

meaningful temperature dependent parameterisations.

3.5.3 Scope and limitations of the new parameterisation

The thermodynamic model AIOMFAC has been developed based on modified

versions of UNIFAC and LIFAC (Yan et al., 1999), with the aim to establish

a versatile activity coefficient model for atmospheric applications. The new

parameterisation of the model aims at improving AIOMFAC predictions par-

ticularly at lower temperatures of atmospheric relevance. Deviations between

the experimental data and model predictions from the new AIOMFAC-P3 ver-

sion are associated with either the inaccuracy of the measurements, the lack of

data to better cover and parameterise the model for a wide composition and

temperature range, or limitations of the AIOMFAC expressions and the un-

derlying group contribution method. Own measurements were performed for

selected aqueous organic systems at low temperature and temperature around

room temperature, which were used together with experimental data from the

literature database for parameterising the model over a wider temperature

range. The complexity of organics in terms of their physical and chemical

properties such as size, shape and combinations of groups in multifunctional

molecules are important factors that influence the quality of AIOMFAC pre-

dictions.

Most of the SLE data at low temperature are limited to simple organic

molecules, which thus make up the majority of the model parameterisation

database. Due to this, the accuracy of AIOMFAC predictions is expected to

decrease with increasing complexity of multifunctional organic compounds.

However, the new AIOMFAC parameterisation provides a tool to predict ac-

tivity coefficients with better overall accuracy than the previous version and

offers the versatility of a group-contribution method for the prediction of ac-

tivity coefficients in complex mixtures containing many tens to thousands of

individual components.


AIOMFACXP1

AIOMFACXP3

a b c

d e f

0w0 0w2 0w4 0w6 0w8 1w0

mole fraction xgH2O:

0w0

0w2

0w4

0w6

0w8

1w0

activity

agx:

H2O g1: K Malonic_acid g2:

Temperature range: 262 XX 273 K

EXP: malonic_acidKwater



ideal aw





0w0 0w2 0w4 0w6 0w8 1w0


0w0

0w2

0w4

0w6

0w8

1w0

activity

agx:

EXP: malonic_acidKwater



ideal aw

0w0 0w2 0w4 0w6 0w8 1w0


0w0

0w2

0w4

0w6

0w8

1w0

activity

agx:

EXP: Malonic_acidKwater



ideal aw



0w0 0w2 0w4 0w6 0w8 1w0


0w0

0w2

0w4

0w6

0w8

1w0

activity

agx:

EXP: Malonic_acidKwater



ideal aw

0w0 0w2 0w4 0w6 0w8 1w0

xorgg2:

0w0

0w2

0w4

0w6

0w8

1w0

activity

aw

0w0 0w2 0w4 0w6 0w8 1w0

xorgg2:

0w0

0w2

0w4

0w6

0w8

1w0

activity

aw


150 480KTemperature range: XX


150 480KTemperature range: XX

250260270280

TgK:

250260270280

TgK:

280300320340

TgK:

280300320340

TgK:

Figure 3.4: Measurements for malonic acid + water solutions, corresponding cal-

culations of AIOMFAC-P1 in (panels a-c) and AIOMFAC-P3 (panels d-f). Panels

(c, f) show the temperature dependence of water activities predicted for the range

from 150 - 480 K. Panels(a, d): Low temperature experimental SLE data (crosses)



shown as well (green triangles) while panels b and e show analogous data for the

malonic acid melting curve. The error bars represent the model sensitivity to a com-

position variation by xtol =0.01. The dashed line represents the hypothetical water

activity of an ideal mixture. The temperature of the individual data points are given

in the boxes below the main panels. Experimental data: Braban et al. (2003) and

Apelblat and Manzurola (1987).

90 Chapter 3. Improved AIOMFAC temperature dependenceAIOMFACXP5

AIOMFACXP3

a b c

d e f

dbd db2 db4 db6 db8 5bd

mole fraction x:H2O9

dbd

db2

db4

db6

db8

5bd

activity

a:x9

H2O :59 K 2XButanone :29


EXP: 2XbutanoneKwater



ideal aw





d

5

5d

55

2d

activitycoeffbγ:x9

EXP: waterK2Xbutanone γw

EXP: waterK2Xbutanone γorg

AIOMFAC γw

AIOMFAC γorg


xorg:29

dbd

db2

db4

db6

db8

5bd

activity

aw


xorg:29

dbd

db2

db4

db6

db8

5bd

activity

aw

55d 48dK


Temperature range: XX

55d 48dK


Temperature range: XX





d

5

5d

55

2d

activitycoeffbγ:x9

EXP: waterK2Xbutanone γw

EXP: waterK2Xbutanone γorg

AIOMFAC γw

AIOMFAC γorg



2dd22d24d26d28d

T:K9

2dd22d24d26d28d

T:K9



dbd

db2

db4

db6

db8

5bd

activity

a:x9

EXP: 2XbutanoneKwater



ideal aw

35d

32d

33d

T:K9

35d

32d

33d

T:K9

Figure 3.5: Measurements for 2-butanone + water solutions, corresponding calcu-

lations of AIOMFAC-P1 in (panels a-c) and AIOMFAC-P3 (panels d-f). Panels

(c, f) show the temperature dependence of water activities predicted for the range




shown as well (green triangles). The error bars represent the model sensitivity to

a composition variation by xtol =0.01. The dashed line represents the hypothetical

water activity of an ideal mixture. The middle panels (b and e) show the model



points are given in the boxes below the main panels. Experimental data: Lohmann

et al. (1997) and Gmehling et al. (1981).


AIOMFACEPd

AIOMFACEP3

a b c

d e f

wXw wX9 wX4 wX6 wX8 dXw


wXw

wX9

wX4

wX6

wX8

dXw

activity

agx:

H9O gd: 7 9EButoxyethanol g9:

Temperature range: 959 EE 973 K


Temperature range: 959 EE 973 K

EXP: 9Ebutoxyethanol7water



ideal aw



wXw

wX9

wX4

wX6

wX8

dXw

activity

agx:

EXP: 9Ebutoxyethanol7water



ideal aw


Temperature: 383 K


Temperature: 383 K



wXw

wX5

dXw

dX5

9Xw

9X5

3Xw

activitycoeffXγgx:

EXP: 9Ebutoxyethanol7water γw

EXP: 9Ebutoxyethanol7water γorg

AIOMFAC γw

AIOMFAC γorg



wXw

wX5

dXw

dX5

9Xw

9X5

3Xw

activitycoeffXγgx:

EXP: 9Ebutoxyethanol7water γw

EXP: 9Ebutoxyethanol7water γorg

AIOMFAC γw

AIOMFAC γorg


d5w 48wKTemperature range: EE


xorgg9:

wXw

wX9

wX4

wX6

wX8

dXw

activity

aw


xorgg9:

wXw

wX9

wX4

wX6

wX8

dXw

activity

aw


d5w 48wKTemperature range: EE

94w95w96w97w98w

TgK:

94w95w96w97w98w

TgK:

38w

39w

TgK:

38w

39wTgK:

Figure 3.6: Measurements for 2-butoxyethanol + water solutions, corresponding

calculations of AIOMFAC-P1 in (panels a-c) and AIOMFAC-P3 (panels d-f). Pan-

els (c, f) show the temperature dependence of water activities predicted for the range




shown as well (green triangles). The error bars represent the model sensitivity to

a composition variation by xtol =0.01. The dashed line represents the hypothetical

water activity of an ideal mixture. The middle panels (b and e) show the model



points are given in the boxes below the main panels. Experimental data: Koga et al.

(1994) and Schneider and Wilhelm (1959).


AIOMFAC5P7

AIOMFAC5P3

a b

d

=K= =K2 =K4 =K6 =K8 7K=

mole fraction x+7h

=K=

=K2

=K4

=K6

=K8

7K=

activity

a+xh

org

EXP: Adipic_aciducyclohexanol

AIOMFAC organic activity aorg7AIOMFAC organic activity aorg2ideal aorg

Cyclohexanol +7h u Adipic_acid +2h

Temperature range: 299 55 352 K

=K= =K2 =K4 =K6 =K8 7K=

xorg+2h

=K=

=K2

=K4

=K6

=K8

7K=

activity

aorg+7h

=K= =K2 =K4 =K6 =K8 7K=

mole fraction x+7h

=K=

=K2

=K4

=K6

=K8

7K=

activity

a+xh

org




75= 48=Temperature range: 55 K


75= 48=Temperature range: 55 K

=K= =K2 =K4 =K6 =K8 7K=

xorg+2h

=K=

=K2

=K4

=K6

=K8

7K=

activity

aorg+7h

EXP: Adipic_aciducyclohexanol

AIOMFAC organic activity aorg7AIOMFAC organic activity aorg2ideal aorg

aorg+7h9 T = 75= K

aorg+7h9 T = 78= K

aorg+7h9 T = 27= K

aorg+7h9 T = 24= K

aorg+7h9 T = 27= K

aorg+7h9 T = 3== K

aorg+7h9 T = 33= K

aorg+7h9 T = 36= K

aorg+7h9 T = 39= K

aorg+7h9 T = 42= K

aorg+7h9 T = 45= K

aorg+7h9 T = 48= K

aorg+7h9 T = 75= K

aorg+7h9 T = 78= K

aorg+7h9 T = 27= K

aorg+7h9 T = 24= K

aorg+7h9 T = 27= K

aorg+7h9 T = 3== K

aorg+7h9 T = 33= K

aorg+7h9 T = 36= K

aorg+7h9 T = 39= K

aorg+7h9 T = 42= K

aorg+7h9 T = 45= K

aorg+7h9 T = 48= K

c

3==32=34=36=

T+Kh

3==32=34=36=

T+Kh

Figure 3.7: Measurements for cyclohexanol + adipic acid solutions, corresponding

calculations of AIOMFAC-P1 in panels (a, b) and AIOMFAC-P3 (c, d). Panels

(b, d) represent the temperature dependence predictions from AIOMFAC-P1 and

AIOMFAC-P3 for temperature range of 150 - 480 K. Panel (a, c): SLE of adipic

acid shown vs. mole fraction of cyclohexanol (component 1). The error bars rep-

resent the model sensitivity to a composition variation by xtol = 0.01. The dashed

line is the ideal solution curve for component 1. The temperature of the individual

data points are given in the boxes below the main panels. Experimental data: Lihua

et al. (2007).


AIOMFAC5P1

AIOMFAC5P3

a b c

d e f

9,9 9,2 9,4 9,6 9,8 1,9

mole fraction x:17

9,9

9,2

9,4

9,6

9,8

1,9

activity

a:x7

org

Ethanol :17 X Acetone :27


EXP: ethanolXacetone

AIOMFAC organic activity aorg1


ideal aorg



9,9 9,2 9,4 9,6 9,8 1,9

mole fraction x:17

9,9

9,5

1,9

1,5

2,9

activitycoeff,γ(x7

EXP: ethanolXacetone γorg1:x7


AIOMFAC γorg1:x7

AIOMFAC γorg2:x7


159 489KTemperature range: 55

9,9 9,2 9,4 9,6 9,8 1,9

xorg:27

9,9

9,2

9,4

9,6

9,8

1,9

activity

aorg:17

aorg:17d T = 159 K

aorg:17d T = 189 K

aorg:17d T = 219 K

aorg:17d T = 249 K

aorg:17d T = 279 K

aorg:17d T = 399 K

aorg:17d T = 339 K

aorg:17d T = 369 K

aorg:17d T = 399 K

aorg:17d T = 429 K

aorg:17d T = 459 K

aorg:17d T = 489 K


159 489KTemperature range: 55

9,9 9,2 9,4 9,6 9,8 1,9

xorg:27

9,9

9,2

9,4

9,6

9,8

1,9

activity

aorg:17

aorg:17d T = 159 K

aorg:17d T = 189 K

aorg:17d T = 219 K

aorg:17d T = 249 K

aorg:17d T = 279 K

aorg:17d T = 399 K

aorg:17d T = 339 K

aorg:17d T = 369 K

aorg:17d T = 399 K

aorg:17d T = 429 K

aorg:17d T = 459 K

aorg:17d T = 489 K

9,9 9,2 9,4 9,6 9,8 1,9

mole fraction x:17

9,9

9,2

9,4

9,6

9,8

1,9

1,2

1,4

1,6

1,8

activitycoeff,γ:x7





AIOMFAC γorg1:x7

AIOMFAC γorg2:x7



EXP: ethanolXacetone



ideal aorg

169

179

189

T:K7

169

179

189

T:K7

Figure 3.8: Measurements for ethanol + acetone solutions, corresponding calcula-

tions of AIOMFAC-P1 in panels (a-c) and AIOMFAC-P3 (d-e). Panels (c, f) show

the temperature dependence as predicted by AIOMFAC-P1 and AIOMFAC-P3 for

the temperature range of 150 - 480 K. Panels (a, d): Low temperature experimental

SLE data (crosses), shown as mole fraction of ethanol, x(1), versus activity (a(x)org2)

of acetone. The error bars represent the model sensitivity to a composition variation

by xtol = 0.01. The dashed line is the ideal solution curve for component 1. The

middle panels (b and e) show the model predictions of the activity coefficients com-

pared to VLE data covering temperatures significantly higher than room temperature.

The temperature of the individual data points are given in the boxes below the main

panels. The temperature of the individual data points are given in the boxes below

the main panels. Experimental data: Sapgir (1929) and Amer et al. (1956).


AIOMFACxPd

AIOMFACxP3

a b

d

aorg:d.i T = d5X K

aorg:d.i T = d8X K

aorg:d.i T = 2dX K

aorg:d.i T = 24X K

aorg:d.i T = 27X K

aorg:d.i T = 3XX K

aorg:d.i T = 33X K

aorg:d.i T = 36X K

aorg:d.i T = 39X K

aorg:d.i T = 42X K

aorg:d.i T = 45X K

aorg:d.i T = 48X K

c

Ethanol :d. c 3xHeptanone :2.

Temperature range: 2X4 xx 236 K

XvX Xv2 Xv4 Xv6 Xv8 dvX

mole fraction x:d.

XvX

Xv2

Xv4

Xv6

Xv8

dvX

activity

a:x.

org

EXP: 3xheptanonecethanol

AIOMFAC organic activity aorgdAIOMFAC organic activity aorg2ideal aorg


d5X 48XTemperature range: xx K


xorg:2.

XvX

Xv2

Xv4

Xv6

Xv8

dvX

activity

aorg:d.


mole fraction x:d.

XvX

Xv2

Xv4

Xv6

Xv8

dvX

activity

a:x.

org

EXP: 3xheptanonecethanol

AIOMFAC organic activity aorgdAIOMFAC organic activity aorg2ideal aorg


Temperature range: 2X4 xx 236 K


xorg:2.

XvX

Xv2

Xv4

Xv6

Xv8

dvXactivity

aorg:d.


d5X 48XTemperature range: xx K

aorg:d.i T = d5X K

aorg:d.i T = d8X K

aorg:d.i T = 2dX K

aorg:d.i T = 24X K

aorg:d.i T = 27X K

aorg:d.i T = 3XX K

aorg:d.i T = 33X K

aorg:d.i T = 36X K

aorg:d.i T = 39X K

aorg:d.i T = 42X K

aorg:d.i T = 45X K

aorg:d.i T = 48X K

2XX2dX22X23X24X

T:K.

2XX2dX22X23X24X

T:K.

Figure 3.9: Measurements for ethanol + 3-heptanone solutions, corresponding cal-

culations of AIOMFAC-P1 in panels (a, b) and AIOMFAC-P3 (c, d). Panels (b, d)

shows the temperature dependence predictions from AIOMFAC-P1 and AIOMFAC-

P3 for temperature range of 150 - 480 K. The SLE data in panel (a, c) show the

composition (mole fraction of ethanol) against activity of 3-heptanone. The error

bars represent the model sensitivity to a composition variation by xtol = 0.01. The

dashed line is the ideal solution curve for component 1. Experimental data: Fiege

et al. (1996).


AIOMFACxPX

AIOMFACxP3

a b c

d e f

aorgmXp. T = X5f K

aorgmXp. T = X8f K

aorgmXp. T = dXf K

aorgmXp. T = d4f K

aorgmXp. T = d7f K

aorgmXp. T = 3ff K

aorgmXp. T = 33f K

aorgmXp. T = 36f K

aorgmXp. T = 39f K

aorgmXp. T = 4df K

aorgmXp. T = 45f K

aorgmXp. T = 48f K

Ethanol mXp : Diethyl_ether mdp

X5f 48fKTemperature range: xx

fcf fcd fc4 fc6 fc8 Xcf

xorgmdp

fcf

fcd

fc4

fc6

fc8

Xcf

activity

aorgmXp


X5f 48fKTemperature range: xx


xorgmdp

fcf

fcd

fc4

fc6

fc8

Xcf

activity

aorgmXp

aorgmXp. T = X5f K

aorgmXp. T = X8f K

aorgmXp. T = dXf K

aorgmXp. T = d4f K

aorgmXp. T = d7f K

aorgmXp. T = 3ff K

aorgmXp. T = 33f K

aorgmXp. T = 36f K

aorgmXp. T = 39f K

aorgmXp. T = 4df K

aorgmXp. T = 45f K

aorgmXp. T = 48f K


Temperature range: 34d xx 378 K


Temperature range: 34d xx 378 K


mole fraction xmXp

f

X

d

3

4

5

6

7

8

9

activitycoeffcγmxp

EXP: diethyl_ether:ethanol γorgXmxp

EXP: diethyl_ether:ethanol γorgdmxp

AIOMFAC γorgXmxp

AIOMFAC γorgdmxp


mole fraction xmXp

f

X

d

3

4

5

6

7

8

9

activitycoeffcγmxp

EXP: diethyl_ether:ethanol γorgXmxp

EXP: diethyl_ether:ethanol γorgdmxp

AIOMFAC γorgXmxp

AIOMFAC γorgdmxp


mole fraction xmXp

fcf

fcd

fc4

fc6

fc8

Xcf

activity

amxp

org

EXP: diethyl_ether:ethanol

AIOMFAC organic activity aorgXAIOMFAC organic activity aorgdideal aorg


mole fraction xmXp

fcf

fcd

fc4

fc6

fc8

Xcf

activity

amxp

org

EXP: diethyl_ether:ethanol

AIOMFAC organic activity aorgXAIOMFAC organic activity aorgdideal aorg

X4f

X5f

X6f

TmKp


Temperature range: X49 xx X56 K


Temperature range: X49 xx X56 K

X4f

X5f

X6f

TmKp

34f35f36f37f38f

TmKp

34f35f36f37f38f

TmKp

Figure 3.10: Measurements for ethanol + diethyl ether solutions, corresponding

calculations of AIOMFAC-P1 and AIOMFAC-P3. Panels (c, f) show the tem-

perature dependence of the ethanol activity, as predicted by AIOMFAC-P1 and

AIOMFAC-P3 for the temperature range 150 - 480 K. Panel (a,d): Experimen-

tal SLE data (crosses) compared with model predictions (triangles) for the activity

of diethyl ether in the very low temperature range 149 to 156 K. The dashed line

is the ideal solution curve for component 1. The middle panels (b and e) show the

model predictions of the activity coefficients compared to VLE data covering temper-

atures significantly higher than room temperature. The temperature of the individual

data points are given in the boxes below the main panels. Experimental data:Lalande

(1934) and Moeller et al. (1951).


3.6 Conclusions

An improved temperature dependence parameterisation of aqueous organic

and binary organic water-free mixtures is presented for the thermodynamic

group contribution model AIOMFAC. A comprehensive database of experi-

mental thermodynamic equilibria data is established by collecting and care-

fully validating different data types covering a wide temperature and concen-

tration range. In addition, new measurements that have been performed for

selected aqueous organic systems, at room temperature and below, were also

included in the database. The database is used to determine new AIOM-

FAC group interaction parameters for organic main groups of atmospheric

relevance: carboxyl, hydroxyl, ketone, aldehyde, ether, ester, alkyl, aromatic

carbon-alcohol, and aromatic hydrocarbons. The parameter fitting proce-

dure involved the simultaneous determination of 150 interaction parameters

for the 14 main groups. Thus, the new temperature dependence parame-

terisation allows to calculate activity coefficients and their temperature de-

pendence for a wide variety of organic and water-free mixtures. In general,

the new AIOMFAC parameterisation achieves good agreement with a large

number of experimental datasets. In the case of some organic systems, lack

of experimental data to constrain the activity coefficients is a major limi-

tation. Further improvements of the AIOMFAC model description of these

systems and by that, the interactions of the functional groups involved, will

require additional measurements over a wide temperature and concentration

range.The improved AIOMFAC model can be used to better account for the

temperature dependence of activity coefficients relevant in predictions related

to atmospheric ice nucleation and gas-particle partitioning in multicomponent

systems.

Acknowledgements

This work was supported by the Swiss National Foundation, project 200020-

125151 and by the CCES projects IMBALANCE and OPTIWARES funded

by the ETH Domain.

3.6. Conclusions 97

Table 3.1: Database used for the parameterisation of organic main group ↔ wa-

ter and organic ↔ organic main group interactions of AIOMFAC-P3. Listed

are components, main groups, temperature range, number of data points (Nd), ini-

tial weighting (winitd ) and references of “water + organic ” and “organic + organic ”

datasets

Organic compounds Org. main groupsChemical formula (sub-

groups)T ( K) Data type Nd winit

d Reference

— water + alcohol/polyol —

ethanol CHn, CH[OH]n , OH (CH

[alc−tail]3 )(CH

[OH]2 )(OH) 220− 269 SLE 7 5.00 Ross (1954)


[alc−tail]3 )(CH

[OH]2 )(OH) 265− 273 SLE 31 5.00 Knight (1962)


[alc−tail]3 )(CH

[OH]2 )(OH) 211− 273 SLE 62 1.00 Pickering (1893)


[alc−tail]3 )(CH

[OH]2 )(OH) 307− 318 VLE 11 1.00

Gmehling and Onken

(1977)


[alc−tail]3 )(CH

[OH]2 )(OH) 341− 353 VLE 11 1.00

Gmehling and Onken

(1977)


[alc−tail]3 )(CH

[OH]2 )(OH) 350− 363 VLE 11 1.00

Gmehling and Onken

(1977)


[alc−tail]3 )(CH

[OH]2 )(OH) 351− 372 VLE 34 1.00

Gmehling and Onken

(1977)

1-propanol CHn, CH[OH]n , OH

(CH[alc−tail]3 )(CH

[alc−tail]2 )

(CH[OH]2 )(OH) 263− 270 SLE 3 5.00 Ross (1954)



[alc−tail]2 )

(CH[OH]2 )(OH) 264− 270 SLE 7 5.00 Chapoy et al. (2008)



[alc−tail]2 )

(CH[OH]2 )(OH) 264− 270 SLE 12 5.00 Pickering (1893)



[alc−tail]2 )

(CH[OH]2 )(OH) 264− 270 SLE 14 5.00 Pickering (1893)



[alc−tail]2 )

(CH[OH]2 )(OH) 330− 339 VLE 8 1.00

Gmehling and Onken

(1977)



[alc−tail]2 )

(CH[OH]2 )(OH) 345− 355 VLE 8 1.00

Gmehling and Onken

(1977)



[alc−tail]2 )

(CH[OH]2 )(OH) 355− 365 VLE 8 1.00

Gmehling and Onken

(1977)



[alc−tail]2 )

(CH[OH]2 )(OH) 361− 372 VLE 8 1.00

Gmehling and Onken

(1977)

2-propanol CHn, CH[OH]n , OH (CH

[alc]3 )2(CH[OH])(OH) 266− 273 SLE 29 5.00 Knight (1962)


[alc]3 )2(CH[OH])(OH) 271− 273 SLE 9 5.00 Okamoto et al. (1978)


[alc]3 )2(CH[OH])(OH) 272− 273 SLE 17 5.00

Webb and Lindsley

(1934)


[alc]3 )2(CH[OH])(OH) 353− 372 VLE 24 1.00

Gmehling and Onken

(1977)


[alc]3 )2(CH[OH])(OH) 341− 357 VLE 19 1.00

Gmehling and Onken

(2003a)


[alc]3 )2(CH[OH])(OH) 325− 340 VLE 19 1.00

Gmehling and Onken

(2003a)


[alc]3 )2(CH[OH])(OH) 328 VLE 8 1.00

Gmehling and Onken

(1977)


[alc]3 )2(CH[OH])(OH) 318 VLE 8 1.00

Gmehling and Onken

(1977)


[alc]3 )2(CH[OH])(OH) 308 VLE 8 1.00

Gmehling and Onken

(1977)


Table 3.1: Continued.


groups)T (K) Data type Nd winit

d Reference

1-butanol CHn, CH[OH]n , OH


[alc−tail]2 )2

(CH[OH]2 )(OH) 271− 273 SLE 22 5.00 Knight (1962)



[alc−tail]2 )2

(CH[OH]2 )(OH) 200− 273 SLE 10 5.00 Lohmann et al. (1997)



[alc−tail]2 )2

(CH[OH]2 )(OH) 366− 384 VLE 12 1.00

Gmehling and Onken

(1977)



[alc−tail]2 )2

(CH[OH]2 )(OH) 365− 382 VLE 8 1.00

Gmehling and Onken

(2003a)



[alc−tail]2 )2

(CH[OH]2 )(OH) 323 VLE 4 1.00 Gmehling et al. (1988)



[alc−tail]2 )2




[alc−tail]2 )2



(CH[alc]3 )(CH

[alc−tail]3 )

(CH[alc−tail]2 )(CH[OH])(OH) 215− 259 SLE 10 5.00 Lohmann et al. (1997)


(CH[alc]3 )(CH

[alc−tail]3 )

(CH[alc−tail]2 )(CH[OH])(OH) 240− 273 SLE 2 5.00 Lohmann et al. (1997)


(CH[alc]3 )(CH

[alc−tail]3 )

(CH[alc−tail]2 )(CH[OH])(OH) 268− 273 SLE 28 2.00 Knight (1962)


(CH[alc]3 )(CH

[alc−tail]3 )

(CH[alc−tail]2 )(CH[OH])(OH) 330− 335 VLE 11 1.00

Gmehling and Onken

(2003a)


(CH[alc]3 )(CH

[alc−tail]3 )


Gmehling and Onken

(2003a)


(CH[alc]3 )(CH

[alc−tail]3 )


Gmehling and Onken

(2003a)


(CH[alc]3 )(CH

[alc−tail]3 )


Gmehling and Onken

(2003a)


(CH[alc]3 )(CH

[alc−tail]3 )


Gmehling and Onken

(2003a)

isobutanol CHn, CH[OH]n , OH

(CH[alc−tail]3 )2(CH[alc−tail])

(CH[OH]2 )(OH) 225− 273 SLE 10 5.00 Lohmann et al. (1997)

tert-butanol CHn, CH[OH]n , OH (CH

[alc]3 )3(C[OH])(OH) 266− 273 SLE 34 5.00 Knight (1962)


[alc]3 )3(C[OH])(OH) 353− 364 VLE 15 1.00 Gmehling et al. (1981)


[alc]3 )3(C[OH])(OH) 331− 339 VLE 15 1.00

Gmehling and Onken

(1977)


[alc]3 )3(C[OH])(OH) 308− 317 VLE 17 1.00

Gmehling and Onken

(1977)


[alc]3 )3(C[OH])(OH) 353 VLE 9 1.00

Gmehling and Onken

(2003a)


[alc]3 )3(C[OH])(OH) 343 VLE 9 1.00

Gmehling and Onken

(2003a)


[alc]3 )3(C[OH])(OH) 303 VLE 16 1.00

Gmehling and Onken

(2003a)

glycerol CH[OH]n , OH (CH

[OH]2 )2(CH[OH])(OH)3 189− 231 SLE 10 1.00 Kanno et al. (2004)

3.6. Conclusions 99




d Reference


[OH]2 )2(CH[OH])(OH)3 225− 271 SLE 7 5.00 Ross (1954)


[OH]2 )2(CH[OH])(OH)3 232− 271 SLE 10 5.00 Olsen et al. (1930)


[OH]2 )2(CH[OH])(OH)3 258− 272 SLE 5 5.00 Lerici et al. (2006)


[OH]2 )2(CH[OH])(OH)3 255− 291 SLE(org) d 11 0.20

Pushin and Glagoleva

(1922)


[OH]2 )2(CH[OH])(OH)3 289 aw(bulk) 15 0.0 this work






[OH]2 )2(CH[OH])(OH)3 347− 421 VLE 8 0.001 Soujanya et al. (2010)









1,2-ethanediol CH[OH]n , OH (CH

[OH]2 )2(OH)2 220− 270 SLE 6 5.00 Dykyj et al. (1956)


[OH]2 )2(OH)2 271− 273 SLE 10 5.00 Okamoto et al. (1978)


[OH]2 )2(OH)2 225− 267 SLE 7 5.00 Ott et al. (1972)


[OH]2 )2(OH)2 224− 273 SLE 7 2.00 Ott et al. (1972)


[OH]2 )2(OH)2 223− 270 SLE 6 5.00 Clendenning (1946)


[OH]2 )2(OH)2 230− 260 SLE(org) d 11 0.20 Ott et al. (1972)


[OH]2 )2(OH)2 298.15 aw(bulk) 14 0.0 Marcolli and Peter (2005)


[OH]2 )2(OH)2 323 VLE 19 1.00 Gmehling et al. (1988)






[OH]2 )2(OH)2 343 VLE 15 0.20

Gmehling and Onken

(2003a)




[OH]2 )2(OH)2 359− 437 VLE 9 1.00

Gmehling and Onken

(2003a)

1,2 propanediol CHn, CH[OH]n , OH

(CH[alc]3 )(CH

[OH]2 )(CH[OH])

(OH)2207− 270 SLE 7 5.00 Ross (1954)


(CH[alc]3 )(CH

[OH]2 )(CH[OH])

(OH)2216− 271 SLE 12 5.00 Boese et al. (1953)


(CH[alc]3 )(CH

[OH]2 )(CH[OH])

(OH)2298 aw(bulk) 13 0.0 Marcolli and Peter (2005)


(CH[alc]3 )(CH

[OH]2 )(CH[OH])

(OH)2288 VLE 5 1.00 Gmehling et al. (1988)


(CH[alc]3 )(CH

[OH]2 )(CH[OH])



(CH[alc]3 )(CH

[OH]2 )(CH[OH])






d Reference


(CH[alc]3 )(CH

[OH]2 )(CH[OH])



(CH[alc]3 )(CH

[OH]2 )(CH[OH])



(CH[alc]3 )(CH

[OH]2 )(CH[OH])



(CH[alc]3 )(CH

[OH]2 )(CH[OH])

(OH)2353 VLE 8 1.00

Gmehling and Onken

(2003a)


(CH[alc]3 )(CH

[OH]2 )(CH[OH])

(OH)2373 VLE 6 0.10

Gmehling and Onken

(2003a)


(CH[alc]3 )(CH

[OH]2 )(CH[OH])

(OH)2383 VLE 11 1.00

Gmehling and Onken

(2003a)


(CH[alc]3 )(CH

[OH]2 )(CH[OH])

(OH)2395 VLE 9 1.00

Gmehling and Onken

(2003a)

1,3 propanediol CHn, CH[OH]n , OH (CH

[alc]2 )(CH

[OH]2 )2(OH)2 200− 231 SLE 4 1.00 Ganbavale et al


[alc]2 )(CH

[OH]2 )2(OH)2 256− 270 SLE 5 5.00 Ganbavale et al


[alc]2 )(CH

[OH]2 )2(OH)2 249− 270 SLE 5 5.00 Ross (1954)


[alc]2 )(CH

[OH]2 )2(OH)2 298 aw(bulk) 13 0.00 Marcolli and Peter (2005)


[alc]2 )(CH

[OH]2 )2(OH)2 343− 442 VLE 19 0.1 Sanz et al. (2001)


[alc]2 )(CH

[OH]2 )2(OH)2 341− 428 VLE 12 1.00 Mun and Lee (1999)


[alc]2 )(CH

[OH]2 )2(OH)2 353− 441 VLE 18 0.20 Mun and Lee (1999)

1,4-butanediol CHn, CH[OH]n , OH (CH

[alc]2 )2(CH

[OH]2 )2(OH)2 196− 230 SLE 4 1.00 Zobrist et al. (2008)


[alc]2 )2(CH

[OH]2 )2(OH)2 258− 270 SLE 4 5.00 Zobrist et al. (2008)


[alc]2 )2(CH

[OH]2 )2(OH)2 258 SLE 2 1.00 Ganbavale et al a


[alc]2 )2(CH



[alc]2 )2(CH



[alc]2 )2(CH



[alc]2 )2(CH



[alc]2 )2(CH



[alc]2 )2(CH



[alc]2 )2(CH



[alc]2 )2(CH



[alc]2 )2(CH



[alc]2 )2(CH



[alc]2 )2(CH



[alc]2 )2(CH



[alc]2 )2(CH



[alc]2 )2(CH

[OH]2 )2(OH)2 298 aw(bulk) 16 0.00 Marcolli and Peter (2005)

3.6. Conclusions 101




d Reference


[alc]2 )2(CH

[OH]2 )2(OH)2 289 aw(bulk) 9 0.00 Ganbavale et al a


[alc]2 )2(CH



[alc]2 )2(CH



[alc]2 )2(CH

[OH]2 )2(OH)2 333 VLE 10 1.00

Gmehling and Onken

(2003b)


[alc]2 )2(CH

[OH]2 )2(OH)2 338 VLE 10 1.00

Gmehling and Onken

(2003b)


[alc]2 )2(CH

[OH]2 )2(OH)2 343 VLE 10 1.00

Gmehling and Onken

(2003b)


[alc]2 )2(CH

[OH]2 )2(OH)2 348 VLE 10 1.00

Gmehling and Onken

(2003b)


[alc]2 )2(CH

[OH]2 )2(OH)2 353 VLE 9 1.00

Gmehling and Onken

(2003b)


[alc]2 )2(CH

[OH]2 )2(OH)2 358 VLE 10 1.00

Gmehling and Onken

(2003b)


[alc]2 )2(CH

[OH]2 )2(OH)2 363 VLE 10 1.00

Gmehling and Onken

(2003b)


[alc]2 )2(CH

[OH]2 )2(OH)2 368 VLE 10 1.00

Gmehling and Onken

(2003b)


[alc]2 )2(CH

[OH]2 )2(OH)2 367− 409 VLE 13 1.00 Gmehling et al. (1988)


[alc]3 )2(CH[OH])2(OH)2 232− 270 SLE 6 5.00 Clendenning (1946)


[alc]3 )2(CH[OH])2(OH)2 298 aw(bulk) 13 0.00 Marcolli and Peter (2005)


[alc]3 )2(CH[OH])2(OH)2 375− 420 VLE 8 1.00 Gmehling et al. (1988)













1,5-pentanediol CHn, CH[OH]n , OH

(CH[alc−tail]2 )3(CH

[OH]2 )2

(OH)2200− 232 SLE 5 1.00 Ganbavale et al a.



[OH]2 )2

(OH)2260− 272 SLE 5 5.00 Ganbavale et al a.



[OH]2 )2

(OH)2298 aw(bulk) 14 0.00 Marcolli and Peter (2005)

1,2-hexanediol CHn, CH[OH]n , OH


[alc−tail]2 )3

(CH[OH]2 )(CH[OH])(OH)2

223− 232 SLE 4 1.00 Ganbavale et al a.



[alc−tail]2 )3


272− 271 SLE 4 5.00 Ganbavale et al a.



[alc−tail]2 )3


298 aw(bulk) 12 0.00 Marcolli and Peter (2005)


(CH[alc]3 )2(CH

[alc]2 )2(CH[OH])2

(OH)2204− 230 SLE 3 1.00 Zobrist et al. (2008)


(CH[alc]3 )2(CH

[alc]2 )2(CH[OH])2






d Reference


(CH[alc]3 )2(CH

[alc]2 )2(CH[OH])2

(OH)2289 aw(bulk) 9 0.00 this work


(CH[alc]3 )2(CH

[alc]2 )2(CH[OH])2



(CH[alc]3 )2(CH

[alc]2 )2(CH[OH])2


1,2,6-hexanetriol CHn, CH[OH]n , OH

(CH[alc]2 )3(CH

[OH]2 )2(CH[OH])



(CH[alc]2 )3(CH

[OH]2 )2(CH[OH])

(OH)3251− 272 SLE 6 5.00 Ross (1954)


(CH[alc]2 )3(CH

[OH]2 )2(CH[OH])



(CH[alc]2 )3(CH

[OH]2 )2(CH[OH])



(CH[alc]2 )3(CH

[OH]2 )2(CH[OH])



(CH[alc]2 )3(CH

[OH]2 )2(CH[OH])


1,2,7,8-octantetrol CHn, CH[OH]n , OH

(CH[alc]2 )4(CH

[OH]2 )2(CH[OH])2



(CH[alc]2 )4(CH

[OH]2 )2(CH[OH])2



(CH[alc]2 )4(CH

[OH]2 )2(CH[OH])2



(CH[alc]2 )4(CH

[OH]2 )2(CH[OH])2



(CH[alc]2 )4(CH

[OH]2 )2(CH[OH])2


2,2,6,6-tetrakis

(hydroxymethyl)cyclohexanol CHn, CH[OH]n , OH

(CH[alc]2 )2(CH[alc])2(CH

[OH]2 )4

(CH[OH])(C[alc])(OH)5208− 232 SLE 4 1.00 Zobrist et al. (2008)

2,2,6,6-tetrakis



[OH]2 )4

(CH[OH])(C[alc])(OH)5265− 272 SLE 5 5.00 Zobrist et al. (2008)

2,2,6,6-tetrakis



[OH]2 )4

(CH[OH])(C[alc])(OH)5289 aw(bulk) 8 0.00 this work

2,2,6,6-tetrakis



[OH]2 )4


2,2,6,6-tetrakis



[OH]2 )4


sorbitol CH[OH]n , OH (CH

[OH]2 )2(CH[OH])4(OH)6 208− 233 SLE 5 1.00 Ganbavale et al a.





d Reference


[OH]2 )2(CH[OH])4(OH)6 256− 272 SLE 6 5.00 Ganbavale et al a.


[OH]2 )2(CH[OH])4(OH)6 298 aw(bulk) 7 0.00 Ganbavale et al a.


[OH]2 )2(CH[OH])4(OH)6 298 aw(bulk) 8 0.00

Bower and Robinson

(1963)


[OH]2 )2(CH[OH])4(OH)6 298 aw(bulk) 8 0.00 Peng et al. (2001)

— water + carboxylic/dicarboxylic acid sys-

tems —

acetic acid CHn, COOH (CH3)(COOH) 249− 272 SLE 12 5.00 Faucon (1910)

acetic acid CHn, COOH (CH3)(COOH) 250− 289 SLE 26 5.00 Pickering (1893)

acetic acid CHn, COOH (CH3)(COOH) 249− 290 SLE(org) d 11 0.20 Faucon (1910)

acetic acid CHn, COOH (CH3)(COOH) 251− 273 SLE(org) d 20 0.20 Pickering (1893)

acetic acid CHn, COOH (CH3)(COOH) 298 VLE 8 0.00 Campbell et al. (1963)

acetic acid CHn, COOH (CH3)(COOH) 374− 389 VLE 10 0.20Sebastiani and

Lacquaniti (1967)

acetic acid CHn, COOH (CH3)(COOH) 373− 390 VLE 16 0.20 Ito and Yoshida (1963)



acetic acid CHn, COOH (CH3)(COOH) 343 VLE 11 0.20Arich and Tagliavini

(1958)


(1958)


(1958)

acetic acid CHn, COOH (CH3)(COOH) 322− 329 VLE 8 0.20 Keyes (1933)



acetic acid CHn, COOH (CH3)(COOH) 373− 386 VLE 9 0.20 Narayana et al. (1985)

propanoic acid CHn, COOH (CH3)(CH2)(COOH) 245− 273 SLE 19 5.00 Faucon (1910)

propanoic acid CHn, COOH (CH3)(CH2)(COOH) 244− 254 SLE(org) d 8 0.20 Faucon (1910)

propanoic acid CHn, COOH (CH3)(CH2)(COOH) 373− 405 VLE 8 0.20 Ito and Yoshida (1963)



propanoic acid CHn, COOH (CH3)(CH2)(COOH) 372− 401 VLE 18 0.20Dakshinamurty et al.

(1961)

propanoic acid CHn, COOH (CH3)(CH2)(COOH) 325− 354 VLE 24 0.2Gmehling and Onken

(1977)


(1977)


(1977)


(1977)


(2003a)




groups)T ( K) Data type Nd winit

d Reference

butanoic acid CHn, COOH (CH3)(CH2)2(COOH) 261− 273 SLE 19 5.00 Faucon (1910)

butanoic acid CHn, COOH (CH3)(CH2)2(COOH) 261− 269 SLE(org) d 8 0.20 Faucon (1910)

butanoic acid CHn, COOH (CH3)(CH2)2(COOH) 303 VLE 7 0.00Wright and Akhtar

(1970)

butanoic acid CHn, COOH (CH3)(CH2)2(COOH) 373− 394 VLE 8 1.00Gmehling and Onken

(1977)

oxalic acid COOH (COOH)2 272− 273 SLE 4 5.00 Braban et al. (2003)

oxalic acid COOH (COOH)2 277− 308 SLE(org) d 11 0.00 Braban et al. (2003)

oxalic acid COOH (COOH)2 278− 338 SLE(org) d 13 0.00Apelblat and Manzurola

(1987)

oxalic acid COOH (COOH)2 298 aw 14 0.00 Peng et al. (2001)

oxalic acid COOH (COOH)2 284− 352 SLE(org) d 8 0.00 Omar and Ulrich (2006)

malic acidCHn, CH

[OH]n , COOH,

OH

(CH2)(CH[OH])(COOH)2

(OH) 278− 338 SLE(org) d 13 0.2Apelblat and Manzurola

(1987)

malic acidCHn, CH

[OH]n , COOH,

OH


(OH) 262− 273 SLE 16 2.00 Beyer et al. (2008)

malic acidCHn, CH

[OH]n , COOH,

OH


(OH) 298 aw 6 0.00Maffia and Meirelles

(2001)

malonic acid CHn, COOH (CH2)(COOH)2 262− 273 SLE 22 5.00 Braban et al. (2003)

malonic acid CHn, COOH (CH2)(COOH)2 278− 338 SLE(org) d 13 0.2Apelblat and Manzurola

(1987)

malonic acid CHn, COOH (CH2)(COOH)2 298 aw 6 0.00 Peng et al. (2001)

malonic acid CHn, COOH (CH2)(COOH)2 298 aw 6 0.00Maffia and Meirelles

(2001)

malonic acid CHn, COOH (CH2)(COOH)2 298 aw 7 0.00 Peng et al. (2001)

succinic acid CHn, COOH (CH2)2(COOH)2 273 SLE 9 5.00 Beyer et al. (2008)

succinic acid CHn, COOH (CH2)2(COOH)2 296− 447 SLE(org) d 10 0.20 Lin et al. (2007)

succinic acid CHn, COOH (CH2)2(COOH)2 278− 338 SLE(org) d 13 0.20Apelblat and Manzurola

(1987)

succinic acid CHn, COOH (CH2)2(COOH)2 298 aw 5 0.00Maffia and Meirelles

(2001)

succinic acid CHn, COOH (CH2)2(COOH)2 298 aw 9 0.00 Peng et al. (2001)

glutaric acid CHn, COOH (CH2)3(COOH)2 271− 273 SLE 5 5.00 Beyer et al. (2008)

glutaric acid CHn, COOH (CH2)3(COOH)2 279− 336 SLE(org) d 24 0.10Apelblat and Manzurola

(1989)

glutaric acid CHn, COOH (CH2)3(COOH)2 277− 298 SLE(org) d 23 0.10 Beyer et al. (2008)

glutaric acid CHn, COOH (CH2)3(COOH)2 298 aw 34 0.00 Peng et al. (2001)

glutaric acid CHn, COOH (CH2)3(COOH)2 291 aw 57 0.00 Zardini et al. (2008)

citric acidCHn, CH

[OH]n , COOH,

OH

(CH2)2(C[OH])(COOH)3

(OH) 278− 338 SLE(org) d 13 0.0Apelblat and Manzurola

(1987)

citric acidCHn, CH

[OH]n , COOH,

OH

(CH2)2(C[OH])(COOH)3

(OH) 291 aw 90 0.00 Zardini et al. (2008)

citric acidCHn, CH

[OH]n , COOH,

OH

(CH2)2(C[OH])(COOH)3(OH)298 aw 25 0.00 Peng et al. (2001)





d Reference

adipic acid CHn, COOH (CH2)4(COOH)2 278− 338 SLE(org) d 13 0.20Apelblat and Manzurola

(1987)

pimelic acid CHn, COOH (CH2)5(COOH)2 279− 342 SLE(org) d 21 0.20Apelblat and Manzurola

(1989)

M5 b CHn, CH[OH]n , COOH,

OH, C=C

c191− 230 SLE 6 1.00 Zobrist et al. (2008)


OH, C=C

c255− 271 SLE 6 5.00 Zobrist et al. (2008)


OH, C=C

c233 SLE 3 0.20 Ganbavale et al a


OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C






d Reference

— water + ketone systems —


OH, C=C



OH, C=C



OH, C=C



OH, C=C



OH, C=C


acetone CHn, CHnCO (CH3)(CH3CO) 221− 273 SLE 17 5.00 Jakob (1994)

acetone CHn, CHnCO (CH3)(CH3CO) 298 VLE 13 0.00 Gmehling et al. (1988)





acetone CHn, CHnCO (CH3)(CH3CO) 373 VLE 20 1.00Griswold and Wong

(1952)

acetone CHn, CHnCO (CH3)(CH3CO) 295− 321 VLE 10 1.00Othmer and Benenati

(1945)


(1945)


(1945)


(1945)

acetone CHn, CHnCO (CH3)(CH3CO) 330− 361 VLE 13 1.00 Othmer et al. (1952)

acetone CHn, CHnCO (CH3)(CH3CO) 371− 396 VLE 12 1.00 Othmer et al. (1952)

2-butanone CHn, CHnCO (CH3)(CH2)(CH3CO) 198− 273 SLE 19 5.00 Lohmann et al. (1997)

2-butanone CHn, CHnCO (CH3)(CH2)(CH3CO) 293 VLE 5 0.00 Gmehling et al. (1988)



2-butanone CHn, CHnCO (CH3)(CH2)(CH3CO) 323 VLE 15 1.00 Gaube et al. (1996)

2-butanone CHn, CHnCO (CH3)(CH2)(CH3CO) 333 VLE 20 1.00 Zou and Prausnitz (1987)

2-butanone CHn, CHnCO (CH3)(CH2)(CH3CO) 343 VLE 22 1.00 Zou and Prausnitz (1987)

2-butanone CHn, CHnCO (CH3)(CH2)(CH3CO) 313− 326 VLE 8 1.00 Gmehling et al. (1981)




2-butanone CHn, CHnCO (CH3)(CH2)(CH3CO) 385− 406 VLE 19 1.00 Othmer et al. (1952)

2-pentanone CHn, CHnCO (CH3)(CH2)2(CH3CO) 273− 363 solubil. 20 1.00 Stephenson (1992)

3-pentanone CHn, CHnCO (CH3)2(CH2)(CH2CO) 273− 353 solubil. 18 1.00 Stephenson (1992)





d Reference

3-pentanone CHn, CHnCO (CH3)2(CH2)(CH2CO) 323 VLE 12 1.00Gmehling and Onken

(2003b)


(2003b)


(2003b)


(2003b)


(2003b)

— water + ether systems —

diethyl ether CHn, CHnO (CH3)2(CH2)(CH2O) 269− 272 SLE(org) d 7 5.00 Lalande (1934)

diethyl ether CHn, CHnO (CH3)2(CH2)(CH2O) 269− 303 solubli. 14 0.20 Hill (1923)

diethyl ether CHn, CHnO (CH3)2(CH2)(CH2O) 307− 367 VLE. 10 0.05 Borisova et al. (1983)

2-methoxyethanolCHn, CH

[OH]n , CHnO,

OH(CH2)(CH

[OH]2 )(CH3O)(OH) 343 VLE 16 0.50

Chiavone-Filho et al.

(1993)


[OH]n , CHnO,

OH(CH2)(CH

[OH]2 )(CH3O)(OH) 363 VLE 16 0.50

Chiavone-Filho et al.

(1993)


[OH]n , CHnO,

OH

(CH2)(CH[OH]2 ) (CH3O)(OH)

373− 394 VLE 12 0.50Gmehling and Onken

(2003a)

2-ethoxyethanolCHn, CH

[OH]n , CHnO,

OH

(CH3)(CH2)(CH[OH]2 )(CH2O)

(OH) 343 VLE 20 0.50Chiavone-Filho et al.

(1993)


[OH]n , CHnO,

OH



(1993)


[OH]n , CHnO,

OH


(OH) 373− 407 VLE 34 0.50Hirata and Hoshino

(1982)


[OH]n , CHnO,

OH


(OH) 372− 406 VLE 17 0.50Gmehling and Onken

(2003b)

2-butoxyethanolCHn, CH

[OH]n , CHnO,

OH

(CH3)(CH2)3(CH[OH]2 )(CH2O)

(OH) 252− 273 SLE 23 0.50 Koga et al. (1994)


[OH]n , CHnO,

OH


(OH) 261− 273 SLE 23 5.00 Koga et al. (1994)


[OH]n , CHnO,

OH


(OH) 298 VLE 8 0.00Scatchard and Wilson

(1964)


[OH]n , CHnO,

OH


(OH)318 VLE 8 0.05

Scatchard and Wilson

(1964)


[OH]n , CHnO,

OH



(1964)


[OH]n , CHnO,

OH



(1964)


[OH]n , CHnO,

OH



(1993)


[OH]n , CHnO,

OH


(OH) 363 VLE 22 0.50Escobedo-Alvarado and

Sandler (1999)


[OH]n , CHnO,

OH



(1993)


[OH]n , CHnO,

OH


(OH) 371 VLE 20 0.50Escobedo-Alvarado and

Sandler (1999)





d Reference


[OH]n , CHnO,

OH


(OH) 383 VLE 21 0.50Schneider and Wilhelm

(1959)


[OH]n , CHnO,

OH


(OH) 372− 423 VLE 8 0.50 Newman et al. (1949)

2-isopropoxyethanolCHn, CH

[OH]n , CHnO,

OH

(CH3)2(CH)(CH[OH]2 )(CH2O)

(OH) 358 SLE 16 0.50Chiavone-Filho et al.

(1993)

2-isopropoxyethanolCHn, CH

[OH]n , CHnO,

OH

(CH3)2(CH)(CH[OH]2 )(CH2O)

(OH) 368 SLE 16 0.50Chiavone-Filho et al.

(1993)

1-methoxy-2-propanolCHn, CH

[OH]n , CHnO,

OH

(CH3)(CH2)(CH[OH])(CH3O)


(1993)

1-methoxy-2-propanolCHn, CH

[OH]n , CHnO,

OH



(1993)

— water + ester systems —

methyl acetate CHn, CCOO (CH3)(CH3COO) 233− 273 SLE 7 5.00 Ahlers (1998)

methyl acetate CHn, CCOO (CH3)(CH3COO) 298 VLE 5 0.00Gmehling and Onken

(1977)


(1977)


(1977)

methyl acetate CHn, CCOO (CH3)(CH3COO) 323 VLE 30 1.00 Loehe et al. (1983)


(1977)


(1977)


(1977)

methyl acetate CHn, CCOO (CH3)(CH3COO) 330− 369 VLE 12 1.00 Alvarez et al. (2011)

ethyl acetate CHn, CCOO (CH3)(CH2)(CH3COO) 273− 344 solubil. 16 1.00Stephenson and Stuart

(1986)

ethyl acetate CHn, CCOO (CH3)(CH2)(CH3COO) 323 VLE 9 1.00 Gmehling et al. (1988)




ethyl acetate CHn, CCOO (CH3)(CH2)(CH3COO) 345− 367 VLE 9 1.00 Gmehling et al. (1988)

ethyl acetate CHn, CCOO (CH3)(CH2)(CH3COO) 344− 349 VLE 11 0.20 Gmehling et al. (1988)

1-propyl acetate CHn, CCOO (CH3)(CH2)2(CH3COO) 273− 353 solubil. 18 1.00Stephenson and Stuart

(1986)

1-propyl acetate CHn, CCOO (CH3)(CH2)2(CH3COO) 324− 338 VLE 7 1.00 Gmehling et al. (1988)




1-propyl acetate CHn, CCOO (CH3)(CH2)2(CH3COO) 338 VLE 7 1.00 Gmehling et al. (1988)

1-propyl acetate CHn, CCOO (CH3)(CH2)2(CH3COO) 353 VLE 7 1.00 Gmehling et al. (1988)





d Reference

1-butyl acetate CHn, CCOO (CH3)(CH2)3(CH3COO) 273− 364 solubil. 20 0.50Stephenson and Stuart

(1986)

1-butyl acetate CHn, CCOO (CH3)(CH2)3(CH3COO) 364− 397 VLE 31 0.20 Cho et al. (1983)

isobutyl acetate CHn, CCOO (CH3)2(CH2)(CH)(CH3COO) 273− 353 solubil. 18 0.50Stephenson and Stuart

(1986)

2-butyl acetate CHn, CCOO (CH3)2(CH2)(CH)(CH3COO) 273− 364 solubil. 20 0.50Stephenson and Stuart

(1986)

1-pentyl acetate CHn, CCOO (CH3)(CH2)4(CH3COO) 273− 353 solubil. 16 0.20Stephenson and Stuart

(1986)

1-hexyl acetate CHn, CCOO (CH3)(CH2)5(CH3COO) 273− 363 solubil. 20 0.20Stephenson and Stuart

(1986)

1-hexyl acetate CHn, CCOO (CH3)(CH2)5(CH3COO) 371− 349 VLE 6 0.001 Bomshtein et al. (1983)

tert-butyl acetate CHn, CCOO (CH3)3(C)(CH3COO) 273− 354 solubil. 18 0.50Stephenson and Stuart

(1986)

— water + multifunctional aromatic compounds systems —

benzene ACHn(ACH)6 293− 353 solubil. 8 1.00 Udovenko (1963)

benzene ACHn(ACH)6 274− 339 solubil. 10 1.00 Alexander (1959)

benzene ACHn(ACH)6 273− 229 solubil. 8 1.00 May et al. (1983)

benzene ACHn(ACH)6 342− 371 VLE 20 0.01

Gmehling and Onken

(2003b)

phenol ACHn, ACOH (ACH)5 (ACOH) 284− 314 SLE(org) d 23 0.20Paterno and Ampola

(1897)

phenol ACHn, ACOH (ACH)5 (ACOH) 293− 308 SLE(org) d 16 0.00 Jaoui et al. (2002)

phenol ACHn, ACOH (ACH)5 (ACOH) 318 VLE 22 1.00 Gmehling et al. (1981)

phenol ACHn, ACOH (ACH)5 (ACOH) 340− 400 VLE 21 1.0 Kliment et al. (1964)

phenol ACHn, ACOH (ACH)5 (ACOH) 373− 442 VLE 15 1.0 Schreinemakers (1900)

phenol ACHn, ACOH (ACH)5 (ACOH) 373− 455 VLE 14 1.0Gmehling and Onken

(2003b)

phenol ACHn, ACOH (ACH)5 (ACOH) 373− 444 VLE 11 1.00 Tochigi et al. (1997)

— water + aldehyde systems —

acetaldehyde CHn, CHO (CH3)(CHO) 283 VLE 5 1.00 dAvila and Silva (1970)





acetaldehyde CHn, CHO (CH3)(CHO) 306− 367 VLE 5 1.00 Coles and Popper (1950)

propionaldehyde CHn, CHO (CH3)(CH2)(CHO) 288− 313 solubil. 12 1.00 Ferino et al. (1983)

propionaldehyde CHn, CHO (CH3)(CH2)(CHO) 321− 342 VLE 6 1.00 Mozzhukhin et al. (1967)

butyraldehyde CHn, CHO (CH3)(CH2)2(CHO) 278− 313 solubil. 16 1.00 Ferino et al. (1983)

butyraldehyde CHn, CHO (CH3)(CH2)2(CHO) 323 VLE 13 0.20 Tapper et al. (1985)

butyraldehyde CHn, CHO (CH3)(CH2)2(CHO) 338 VLE 12 0.20 Tapper et al. (1985)





d Reference

— water + multifunctional systems —

glucoseCH

[OH]n , OH,

CHO[ether]

(CH[OH]2 )(CH[OH])4

(CHO[ether])(OH)5217− 233 SLE 9 1.00

Miyata and Kanno

(2005)

glucoseCH

[OH]n , OH,

CHO[ether]

(CH[OH]2 )(CH[OH])4

(CHO[ether])(OH)5204− 231 SLE 5 1.00 Zobrist et al. (2008)

glucoseCH

[OH]n , OH,

CHO[ether]

(CH[OH]2 )(CH[OH])4

(CHO[ether])(OH)5243− 273 SLE 8 5.00 Young (1957)

glucoseCH

[OH]n , OH,

CHO[ether]

(CH[OH]2 )(CH[OH])4

(CHO[ether])(OH)5260− 273 SLE 5 5.00 Zobrist et al. (2008)

glucoseCH

[OH]n , OH,

CHO[ether]

(CH[OH]2 )(CH[OH])4

(CHO[ether])(OH)5298 aw(bulk) 20 0.00 Ruegg and Blanc (1981)

glucoseCH

[OH]n , OH,

CHO[ether]

(CH[OH]2 )(CH[OH])4

(CHO[ether])(OH)5298 aw(bulk) 26 0.00

Bonner and Breazeale

(1965)

glucoseCH

[OH]n , OH,

CHO[ether]

(CH[OH]2 )(CH[OH])4

(CHO[ether])(OH)5298 aw(bulk) 8 0.00 Peng et al. (2001)

sucroseCH

[OH]n , OH,

CHO[ether]

(C)(CH[OH]2 )3(CH[OH])5

(CHO[ether])3(OH)8211− 235 SLE 16 1.00 Kanno et al. (2007)

sucroseCH

[OH]n , OH,

CHO[ether]

(C)(CH[OH]2 )3(CH[OH])5

(CHO[ether])3(OH)8217− 232 SLE 6 1.00 Ganbavale et al a

sucroseCH

[OH]n , OH,

CHO[ether]

(C)(CH[OH]2 )3(CH[OH])5

(CHO[ether])3(OH)8237− 273 SLE 10 5.00 Ablett et al. (1992)

sucroseCH

[OH]n , OH,

CHO[ether]

(C)(CH[OH]2 )3(CH[OH])5

(CHO[ether])3(OH)8247− 273 SLE 9 5.00

Williams and Carnahan

(1990)

sucroseCH

[OH]n , OH,

CHO[ether]

(C)(CH[OH]2 )3(CH[OH])5

(CHO[ether])3(OH)8259− 271 SLE 9 5.00 Blond et al. (1997)

sucroseCH

[OH]n , OH,

CHO[ether]

(C)(CH[OH]2 )3(CH[OH])5

(CHO[ether])3(OH)8261− 272 SLE 8 5.00 Zobrist et al. (2008)

sucroseCH

[OH]n , OH,

CHO[ether]

(C)(CH[OH]2 )3(CH[OH])5

(CHO[ether])3(OH)8262− 273 SLE 16 5.00 Kanno et al. (2007)

sucroseCH

[OH]n , OH,

CHO[ether]

(C)(CH[OH]2 )3(CH[OH])5

(CHO[ether])3(OH)8264− 272 SLE 5 5.00 Sei and Gonda (2006)

sucroseCH

[OH]n , OH,

CHO[ether]

(C)(CH[OH]2 )3(CH[OH])5

(CHO[ether])3(OH)8269− 273 SLE 6 5.00 Lerici et al. (2006)

sucroseCH

[OH]n , OH,

CHO[ether]

(C)(CH[OH]2 )3(CH[OH])5

(CHO[ether])3(OH)8289 aw(bulk) 8 0.00 this work

sucroseCH

[OH]n , OH,

CHO[ether]

(C)(CH[OH]2 )3(CH[OH])5


sucroseCH

[OH]n , OH,

CHO[ether]

(C)(CH[OH]2 )3(CH[OH])5


raffinoseCHn, CH

[OH]n ,

OH,CHO[ether]

(C)(CH)(CH[OH]2 )3(CH[OH])8

(CH2O)(CHO[ether])4(OH)11 214− 233 SLE 4 1.00 Zobrist et al. (2008)

raffinoseCHn, CH

[OH]n ,

OH,CHO[ether]


(CH2O)(CHO[ether])4(OH)11 266− 273 SLE 4 5.00 Zobrist et al. (2008)





d Reference

raffinoseCHn, CH

[OH]n , OH,

CHO[ether]


(CH2O)(CHO[ether])4(OH)11 289 aw(bulk) 4 0.00 this work

raffinoseCHn, CH

[OH]n , OH,

CHO[ether]



raffinoseCHn, CH

[OH]n , OH,

CHO[ether]



levoglucosanCHn, CH

[OH]n , OH,

CHO[ether]

(CH)(CH[OH])3(CH2O)

(CHO[ether]) (OH)3192− 233 SLE 8 1.00 Zobrist et al. (2008)

levoglucosanCHn, CH

[OH]n , OH,

CHO[ether]

(CH)(CH[OH])3(CH2O)

(CHO[ether]) (OH)3187− 230 SLE 6 1.00 Lienhard et al. (2012)

levoglucosanCHn, CH

[OH]n , OH,

CHO[ether]

(CH)(CH[OH])3(CH2O)

(CHO[ether]) (OH)3255− 272 SLE 7 5.00 Zobrist et al. (2008)

levoglucosanCHn, CH

[OH]n , OH,

CHO[ether]

(CH)(CH[OH])3(CH2O)

(CHO[ether]) (OH)3254− 272 SLE 7 5.00 Lienhard et al. (2012)

levoglucosanCHn, CH

[OH]n ,

OH,CHO[ether]

(CH)(CH[OH])3(CH2O)

(CHO[ether]) (OH)3291 aw(bulk) 8 0.00 Lienhard et al. (2012)

levoglucosanCHn, CH

[OH]n , OH,

CHO[ether]

(CH)(CH[OH])3(CH2O)

(CHO[ether]) (OH)3296 aw(bulk) 6 0.00 Chan et al. (2005)

levoglucosanCHn, CH

[OH]n , OH,

CHO[ether]

(CH)(CH[OH])3(CH2O)

(CHO[ether]) (OH)3298 aw(bulk) 7 0.00 Lienhard et al. (2012)

glycolic acid CH[OH]n ,OH, COOH (CH

[OH]2 )(OH)(COOH) 206− 230 SLE 4 1.00 Ganbavale et al a

glycolic acid CH[OH]n ,OH, COOH (CH

[OH]2 )(OH)(COOH) 259− 271 SLE 4 5.00 Ganbavale et al a

glycolic acid CH[OH]n , OH, COOH (CH

[OH]2 )(OH)(COOH) 298 aw(bulk) 8 0.00 Ganbavale et al a

pyruvic acid COOH, CHnCO (CH3CO)(COOH) 211− 232 SLE 3 1.00 Ganbavale et al a

pyruvic acid COOH, CHnCO (CH3CO)(COOH) 254− 271 SLE 4 5.00 Ganbavale et al a

pyruvic acid COOH, CHnCO (CH3CO)(COOH) 298 aw(bulk) 9 0.00 Ganbavale et al a

2-methoxyacetic acid CHn, COOH, CHnO (CH2)(CH3O)(COOH) 194− 232 SLE 5 1.00 Ganbavale et al a

2-methoxyacetic acid CHn, COOH, CHnO (CH2)(CH3O)(COOH) 251− 271 SLE 3 2.00 Ganbavale et al a

2-methoxyacetic acid CHn, COOH, CHnO (CH2)(CH3O)(COOH) 266 SLE 2 1.00 Ganbavale et al a













d Reference





2-methoxyacetic acid CHn, COOH, CHnO (CH2)(CH3O)(COOH) 298 aw(bulk) 9 0.00 Ganbavale et al a

2-ethoxyethyl acetate CHn, CHnO, CCOO(CH3)(CH2)(CH2O)2

(CH3COO) 208− 233 SLE 3 1.00 Ganbavale et al a


(CH3COO) 271− 272 SLE 3 2.00 Ganbavale et al a


(CH3COO) 276− 368 solubli. 12 1.00Carvoli and Delogu

(1986)

resorcinol ACHn, ACOH (ACH)4(ACOH)2 223− 232 SLE 4 1.00 Ganbavale et al a

resorcinol ACHn, ACOH (ACH)4(ACOH)2 267− 272 SLE 4 2.00 Ganbavale et al a

resorcinol ACHn, ACOH (ACH)4(ACOH)2 298 aw(bulk) 7 0.00 Ganbavale et al a

2-hydroxybenzoic acid ACHn, ACOH, COOH (ACH)4(AC)(ACOH)(COOH) 298− 348 SLE(org) d 11 0.20Shalmashi and Eliassi

(2008)

2-hydroxybenzoic acid ACHn, ACOH, COOH (ACH)4(AC)(ACOH)(COOH) 283− 339 SLE(org) d 13 0.20Apelblat and Manzurola

(1989)

2-(2-ethoxyethoxy)ethanol CHn, CHnO, OH (CH3)(CH2)2(CH2O)2 (OH) 211− 233 SLE 3 0.10 Ganbavale et al a

2-(2-ethoxyethoxy)ethanol CHn, CHnO, OH (CH3)(CH2)2(CH2O)2 (OH) 260− 272 SLE 4 0.10 Ganbavale et al a

2-(2-ethoxyethoxy)ethanol CHn, CHnO, OH (CH3)(CH2)2(CH2O)2 (OH) 265 SLE 5 0.10 Ganbavale et al a














vanillylmandelic acidACHn, CH

[OH]n , COOH,

CHnO, OH

(ACH)3(AC)2(ACOH)(CH3O)

(CH[OH])(OH)(COOH) 214− 232 SLE 4 1.00 Zobrist et al. (2008)


[OH]n , COOH,

CHnO, OH


(CH[OH])(OH)(COOH) 267− 272 SLE 4 5.00 Zobrist et al. (2008)


[OH]n , COOH,

CHnO, OH


(CH[OH])(OH)(COOH) 289 aw(bulk) 6 0.00 this work





d Reference


[OH]n , COOH,

CHnO, OH




[OH]n , COOH,

CHnO, OH



— water + alcohol+ alcohol systems —

1-butanol, 1-propanolCHn, CH

[OH]n ,

OH,COOH


[alc−tail]2 )2

(CH[OH]2 )(OH),


[alc−tail]2 )

(CH[OH]2 )(OH)

298 LLE 20 0.00Gomis-Yagues et al.

(1998)


[OH]n , OH,

COOH


[alc−tail]2 )2

(CH[OH]2 )(OH),


[alc−tail]2 )

(CH[OH]2 )(OH)


(1998)


[OH]n , OH,

COOH


[alc−tail]2 )2

(CH[OH]2 )(OH),


[alc−tail]2 )

(CH[OH]2 )(OH)


(1998)


[OH]n , OH,

COOH


[alc−tail]2 )2

(CH[OH]2 )(OH),


[alc−tail]2 )

(CH[OH]2 )(OH)


(1998)

1-pentanol, ethanolCHn, CH

[OH]n , OH,

COOH


[alc−tail]2 )3

(CH[OH]2 )(OH),


[OH]2 )(OH)

298 LLE 12 0.00Fernandez-Torres et al.

(1999)


[OH]n , OH,

COOH


[alc−tail]2 )3

(CH[OH]2 )(OH),


[OH]2 )(OH)


(1999)


[OH]n , OH,

COOH


[alc−tail]2 )3

(CH[OH]2 )(OH),


[OH]2 )(OH)


(1999)


[OH]n , OH,

COOH


[alc−tail]2 )3

(CH[OH]2 )(OH),


[OH]2 )(OH)


(1999)

— water + alcohol+ acid systems —

1-butanol, acetic acidCHn, CH

[OH]n , OH,

COOH


[alc−tail]2 )2

(CH[OH]2 )(OH),

(CH3)(COOH)298 LLE 10 0.00 Ruiz Bevia et al. (1984)


[OH]n , OH,

COOH


[alc−tail]2 )2

(CH[OH]2 )(OH),

(CH3)(COOH)303 LLE 12 0.00

Esquıvel and

Bernardo-Gil (1990)


[OH]n , OH,

COOH


[alc−tail]2 )2

(CH[OH]2 )(OH),

(CH3)(COOH)323 LLE 14 1.00

Esquıvel and

Bernardo-Gil (1990)


[OH]n , OH,

COOH

(CH[alc]3 )(CH

[alc−tail]3 )

(CH[alc−tail]2 )(CH[OH])(OH),

(CH3)(COOH)303 LLE 10 0.00

Esquıvel and

Bernardo-Gil (1990)





d Reference


[OH]n , OH,

COOH

(CH[alc]3 )(CH

[alc−tail]3 )


(CH3)(COOH)323 LLE 14 1.00

Esquıvel and

Bernardo-Gil (1990)

1-butanol, propanoic acidCHn, CH

[OH]n , OH,

COOH


[alc−tail]2 )2

(CH[OH]2 )(OH),

(CH3)(CH2)(COOH)298 LLE 8 0.00 Kim and Park (2005)

1-butanol, propanoic acidCHn, CH

[OH]n , OH,

COOH


[alc−tail]2 )2

(CH[OH]2 )(OH),

(CH3)(CH2)(COOH)303 LLE 14 0.00 Solimo et al. (1997)

2-butanol, citric acidCHn, CH

[OH]n , OH,

COOH

(CH[alc]3 )(CH

[alc−tail]3 )


(CH2)2(C[OH])(COOH)3(OH) 298 LLE 8 0.00 Lintomen et al. (2001)

2-pentanol, acetic acidCHn, CH

[OH]n , OH,

COOH

(CH[alc]3 )(CH

[alc−tail]3 )

(CH[alc−tail]2 )2(CH[OH])(OH),

(CH3)(COOH)288 LLE 20 0.20

Al-Muhtaseb and Fahim

(1996)


[OH]n , OH,

COOH

(CH[alc]3 )(CH

[alc−tail]3 )


(CH3)(COOH)298 LLE 20 0.00


(1996)


[OH]n , OH,

COOH

(CH[alc]3 )(CH

[alc−tail]3 )


(CH3)(COOH)303 LLE 8 0.00


(1996)


[OH]n , OH,

COOH

(CH[alc]3 )(CH

[alc−tail]3 )


(CH3)(COOH)308 LLE 10 1.00


(1996)


[OH]n , OH,

COOH

(CH[alc]3 )(CH

[alc−tail]3 )

(CH[alc−tail]2 )2 (CH[OH])(OH),

(CH3)(COOH)318 LLE 8 1.00


(1996)


[OH]n , OH,

COOH

(CH[alc]3 )(CH

[alc−tail]3 )


(CH3)(COOH)323 LLE 14 1.00


(1996)

1-hexanol, acetic acidCHn, CH

[OH]n , OH,

COOH


[alc−tail]2 )4

(CH[OH]2 )(OH),

(CH3)(COOH)293 LLE 8 0.00 Senol (2004)

— water + alcohol+ ketone systems —

tert-butanol,

4-methyl-2-pentanone

CHn, CH[OH]n , OH,

CHnCO

(CH[alc]3 )3(C[OH])(OH),

(CH3)2(CH2)(CH)(CH3CO) 288 LLE 14 0.10 Fang et al. (2008)

tert-butanol,


CHn, CH[OH]n , OH,

CHnCO



tert-butanol,


CHn, CH[OH]n , OH,

CHnCO



tert-butanol,


CHn, CH[OH]n , OH,

CHnCO



1-pentanol, acetoneCHn, CH

[OH]n , OH,

CHnCO


[alc−tail]2 )3

(CH[OH]2 )(OH),

(CH3)(CH3CO)298 LLE 16 0.00 Tiryaki et al. (1994)





d Reference


[OH]n , OH,

CHnCO


[alc−tail]2 )3

(CH[OH]2 )(OH),



[OH]n , OH,

CHnCO


[alc−tail]2 )3

(CH[OH]2 )(OH),


2-octanol, acetoneCHn, CH

[OH]n , OH,

CHnCO


[alc]3 )




[OH]n , OH,

CHnCO


[alc]3 )




[OH]n , OH,

CHnCO


[alc]3 )



— water + alcohol+ ether systems —

ethanol,

2-ethoxy-2-methyl-propane

CHn, CH[OH]n , OH,

CHnO


[OH]2 )(OH),

(CH3)4(C)(CH2O) 288 LLE 14 0.20 Fandary et al. (1999)

ethanol,


CHn, CH[OH]n , OH,

CHnO


[OH]2 )(OH),


ethanol,


CHn, CH[OH]n , OH,

CHnO


[OH]2 )(OH),


ethanol,


CHn, CH[OH]n , OH,

CHnO


[OH]2 )(OH),


— water + alcohol+ ester systems —

ethanol, ethyl acetateCHn, CH

[OH]n , OH,

CCOO


[OH]2 ) (OH),

(CH3) (CH2)(CH3COO) 313 LLE 10 1.00 Mertl (1972)

ethanol, ethyl acetateCHn,CH

[OH]n , OH,

CCOO


[OH]2 )(OH),

(CH3)(CH2) (CH3COO) 328 LLE 10 1.00 Mertl (1972)

ethanol, ethyl acetateCHn,CH

[OH]n , OH,

CCOO


[OH]2 )(OH),

(CH3)(CH2) (CH3COO) 343 LLE 10 1.00 Mertl (1972)

— water + alcohol+ aromatic systems —

1-butanol, phenolCHn,CH

[OH]n , OH,

ACHn, ACOH


[alc−tail]2 )2

(CH[OH]2 )(OH), (ACH)5

(ACOH)298 LLE 12 0.00

De Oliveira and Aznar

(2010)

2-butanol, phenolCHn,CH

[OH]n , OH,

ACHn, ACOH

(CH[alc]3 )(CH

[alc−tail]3 )


(ACH)5(ACOH)298 LLE 12 0.00


(2010)

2-butanol, phenolCHn, CH

[OH]n , OH,

ACHn, ACOH

(CH[alc]3 )(CH

[alc−tail]3 )


(ACH)5(ACOH)313 LLE 12 1.00


(2010)

— water + alcohol+ aldehyde systems —

ethanol, butyraldehyde CHn, CH[OH]n , OH, CHO


[OH]2 )(OH),

(CH3) (CH2)2(CHO) 298 LLE 10 0.00 Letcher et al. (1996)





d Reference

2-propanol, butyraldehyde CHn, CH[OH]n , OH, CHO

(CH[alc]3 )2(CH[OH]) (OH),

(CH3)(CH2)2(CHO) 298 LLE 10 0.00 Letcher et al. (1996)

2-butanol, butyraldehyde CHn, CH[OH]n , OH, CHO

(CH[alc]3 )(CH

[alc−tail]3 )


(CH3)(CH2)2 (CHO)298 LLE 10 0.00 Letcher et al. (1996)

— water + acid+ ketone systems —

acetic acid, 2-butanone CHn, COOH, CHnCO(CH3)(COOH),

(CH3)(CH2)(CH3CO) 298 LLE 8 0.00 Correa et al. (1987)





propanoic acid, 2-butanone CHn, COOH, CHnCO(CH3)(CH2)(COOH),

(CH3)(CH2)(CH3CO) 298 LLE 8 0.00 Arce et al. (1995)





propanoic acid,

2-pentanoneCHn, COOH, CHnCO

(CH3)(CH2)(COOH),

(CH3)(CH2)2(CH3CO) 298 LLE 12 0.00 Arce et al. (1995)

propanoic acid,


(CH3)(CH2)(COOH),


propanoic acid,


(CH3)(CH2)(COOH),


propanoic acid,


(CH3)(CH2)(COOH),


— water + acid+ ether systems —

acetic acid, 2-methoxy-2-

methylpropane CHn, COOH, CHnO(CH3)(COOH),

(CH3)3(C)(CH3O) 293 LLE 18 0.00 Miao et al. (2007)










— water + acid+ ester systems —

acetic acid, ethyl acetate CHn, COOH, CCOO(CH3)(COOH),

(CH3)(CH2)(CH3COO) 283 LLE 12 1.00 Colombo et al. (1999)







d Reference



acetic acid, 1-butyl acetate CHn, COOH, CCOO(CH3)(COOH),

(CH3)(CH2)3(CH3COO) 304 LLE 18 0.00 Wang et al. (2007)





acetic acid, isobutyl acetate CHn, COOH, CCOO

(CH3)(COOH),

(CH3)2(CH2)(CH)(CH3COO) 304 LLE 16 0.00 Wang et al. (2007)


(CH3)(COOH),



(CH3)(COOH),


— water + acid+ aromatic systems —

acetic acid, benzene CHn, COOH, ACHn(CH3)(COOH), (ACH)6 298 LLE 10 0.00 Backes et al. (1990)

— water + ketone+ ether systems —

2-butanone,

2-butoxyethanol

CHn, CH[OH]n , CHnO,

OH

(CH3)(CH2)(CH3CO),

(CH3)(CH2)3(CH[OH]2 )(CH2O)(OH)298 LLE 10 0.00 Newman et al. (1949)

— water + ketone+ ester systems —

acetone, ethyl acetate CHn, CHnCO, CCOO(CH3)(CH3CO),

(CH3)(CH2)(CH3COO) 283 LLE 10 1.00 Choi et al. (1986)

— water + ketone+ aromatic systems —

acetone, phenolCHn, CHnCO, ACHn,

ACOH

(CH3)(CH3CO),

(ACH)5(ACOH) 323 LLE 24 1.00Mafra and Krahenbuhl

(2006)

acetone, phenolCHn, CHnCO, ACHn,

ACOH

(CH3)(CH3CO),

(ACH)5(ACOH) 333 LLE 22 1.00Mafra and Krahenbuhl

(2006)

— water + ether+ aromatic systems —

2-methoxy-2-

methylpropane,

benzene

CHn, CHnO, ACHn(CH3)3(C)(CH3O), (ACH)6 298 LLE 30 0.00 Stephenson (1992)

— water + ether+ aldehyde systems —

diethyl ether, acetaldehyde CHn, CHnO, CHO(CH3)2(CH2)CH2O,

(CH3)(CHO) 288 LLE 10 0.20 Suska (1979)

— water + ester+ aromatic systems —

ethyl acetate, phenolCHn, CCOO, ACHn,

ACOH

(CH3)(CH2)(CH3COO),

(ACH)5(ACOH) 298 LLE 18 0.00Alvarez Gonzalez et al.

(1986)

1-butyl acetate, phenolCHn, CCOO, ACHn,

ACOH

(CH3)(CH2)3(CH3COO),

(ACH)5(ACOH) 298 LLE 32 0.00 Takahashi et al. (1988)





d Reference

1-butyl acetate, phenolCHn, CCOO, ACHn,

ACOH

(CH3)(CH2)3(CH3COO),

(ACH)5(ACOH) 313 LLE 32 0.50 Takahashi et al. (1988)

— Water-free systems —

ethanol, acetic acidCHn, CH

[OH]n , OH,

COOH


[OH]2 )(OH),

(CH3)(COOH) 244− 284 SLE(org) d 13 0.20 Carta and Dernini (1983)


[OH]n , OH,

COOH


[OH]2 )(OH),

(CH3)(COOH) 241− 289 SLE(org) d 22 0.20 Pickering (1893)


[OH]n , OH,

COOH


[OH]2 )(OH),

(CH3)(COOH) 354− 389 VLE 12 0.10 Reichl et al. (1998)


[OH]n , OH,

COOH


[OH]2 )(OH),

(CH3)(COOH) 351− 386 VLE 16 0.10 Hirata et al. (1975)


[OH]n , OH,

COOH


[OH]2 )(OH),

(CH3)(COOH) 323 VLE 16 0.10 Miyamoto et al. (2001)

1-propanol, acetic acidCHn, CH

[OH]n , OH,

COOH


[alc−tail]2 )

(CH[OH]2 )(OH),

(CH3)(COOH)254− 287 SLE(org) d 13 0.20 Pickering (1893)

1-propanol, acetic acidCHn, CH

[OH]n , OH,

COOH


[alc−tail]2 )

(CH[OH]2 )(OH),

(CH3)(COOH)370− 387 VLE 14 1.00 Rius et al. (1959)

cyclohexanol, adipic acidCHn, CH

[OH]n , OH,

COOH


(CH2)4(COOH)2299− 352 SLE(org) d 12 0.10 Lihua et al. (2007)

ethanol, acetoneCHn, CH

[OH]n , OH,

CHnCO


[OH]2 )(OH),

(CH3)(CH3CO) 154− 173 SLE 6 0.20 Sapgir (1929)


[OH]n , OH,

CHnCO


[OH]2 )(OH),

(CH3)(CH3CO) 344 VLE 9 1.00 Lee and Hu (1995)


[OH]n , OH,

CHnCO


[OH]2 )(OH),



[OH]n , OH,

CHnCO


[OH]2 )(OH),



[OH]n , OH,

CHnCO


[OH]2 )(OH),

(CH3)(CH3CO) 373 VLE 9 1.00 Campbell et al. (1987)


[OH]n , OH,

CHnCO


[OH]2 )(OH),



[OH]n , OH,

CHnCO


[OH]2 )(OH),



[OH]n , OH,

CHnCO


[OH]2 )(OH),

(CH3)(CH3CO) 330− 350 VLE 9 1.00 Amer et al. (1956)

ethanol, 2-butanoneCHn, CH

[OH]n , OH,

CHnCO


[OH]2 )(OH),

(CH3)(CH2)(CH3CO) 298 VLE 12 0.00 Ohta et al. (1981)


[OH]n , OH,

CHnCO


[OH]2 )(OH),

(CH3)(CH2)(CH3CO) 308− 314 VLE 19 1.00 Martınez et al. (2008)


[OH]n , OH,

CHnCO


[OH]2 )(OH),

(CH3)(CH2)(CH3CO) 348− 351 VLE 19 1.00 Martınez et al. (2008)


[OH]n , OH,

CHnCO


[OH]2 )(OH),

(CH3)(CH2)(CH3CO) 347− 352 VLE 19 1.00 Wen and Tu (2007)





d Reference

ethanol, 2-heptanoneCHn, CH

[OH]n , OH,

CHnCO


[OH]2 )(OH),

(CH3)(CH2)4(CH3CO) 208− 238 SLE(org) d 20 0.20 Fiege et al. (1996)


[OH]n , OH,

CHnCO


[OH]2 )(OH),

(CH3)2(CH2)3(CH2CO) 204− 236 SLE(org) d 20 0.20 Fiege et al. (1996)


[OH]n , OH,

CHnCO


[OH]2 )(OH),

(CH3)2(CH2)3(CH3CO) 205− 240 SLE(org) d 20 0.20 Fiege et al. (1996)

1-hexanol, 2-octanoneCHn, CH

[OH]n , OH,

CHnCO


[alc−tail]2 )4

(CH[OH]2 )(OH),

(CH3)(CH2)5(CH3CO)227− 253 SLE(org) d 20 0.20

Abbas and Gmehling

(2008)

ethanol, diethyl etherCHn, CH

[OH]n , OH,

CHnO


[OH]2 )(OH),

(CH3)2(CH2)CH2O 151− 157 SLE(org) d 4 0.20 Sapgir (1929)


[OH]n , OH,

CHnO


[OH]2 )(OH),

(CH3)2(CH2)CH2O 149− 157 SLE(org) d 13 0.20 Lalande (1934)


[OH]n , OH,

CHnO


[OH]2 )(OH),

(CH3)2(CH2)CH2O 154− 159 SLE(org) d 2 0.20 Sapgir (1929)


[OH]n , OH,

CHnO


[OH]2 )(OH),

(CH3)2(CH2)CH2O 148− 159 SLE(org) d 9 0.20 Lalande (1934)


[OH]n , OH,

CHnO


[OH]2 )(OH),

(CH3)2(CH2)CH2O 342− 378 VLE 10 0.20 Moeller et al. (1951)


[OH]n , OH,

CHnO


[OH]2 )(OH),



[OH]n , OH,

CHnO


[OH]2 )(OH),


ethanol, 2-methoxy-2-

methylpropane

CHn, CH[OH]n , OH,

CHnO


[OH]2 )(OH),

(CH3)3(C)(CH3O) 324− 347 VLE 22 1.00 Al-Rub et al. (2002)


methylpropane

CHn, CH[OH]n , OH,

CHnO


[OH]2 )(OH),

(CH3)3(C)(CH3O) 326− 349 VLE 30 1.00 Al-Rub et al. (2002)


methylpropane

CHn, CH[OH]n , OH,

CHnO


[OH]2 )(OH),

(CH3)3(C)(CH3O) 328− 351 VLE 30 1.00 Park et al. (2002)

ethanol,

2-ethoxy-2-methylpropane

CHn, CH[OH]n , OH,

CHnO


[OH]2 )(OH),

(CH3)4(C)(CH2O) 298 VLE 56 0.00 Rarey et al. (1999)

ethanol,


CHn, CH[OH]n , OH,

CHnO


[OH]2 )(OH),


ethanol,


CHn, CH[OH]n , OH,

CHnO


[OH]2 )(OH),

(CH3)4(C)(CH2O) 333 VLE 21 1.00 Oh and Park (1998)

ethanol,


CHn, CH[OH]n , OH,

CHnO


[OH]2 )(OH),


ethanol,


CHn, CH[OH]n , OH,

CHnO


[OH]2 )(OH),



[OH]n , OH,

CCOO


[OH]2 )(OH),

(CH3)(CH2)(CH3COO) 158− 190 SLE(org) d 7 0.20 Sapgir (1929)


[OH]n , OH,

CCOO


[OH]2 )(OH),

(CH3)(CH2)(CH3COO) 313 VLE 14 1.00 Mertl (1972)


[OH]n , OH,

CCOO


[OH]2 )(OH),






d Reference


[OH]n , OH,

CCOO


[OH]2 )(OH),



[OH]n , OH,

CCOO


[OH]2 )(OH),

(CH3)(CH2)(CH3COO) 345− 351 VLE 24 1.00 Calvar et al. (2005)

2-propanol, 1-butyl acetateCHn, CH

[OH]n , OH,

CCOO

(CH[alc]3 )2(CH[OH])(OH),

(CH3)(CH2)3(CH3COO) 355− 399 VLE 27 0.20 Gonzalez (1996)

tert-Butanol, tert-butyl

acetateCHn, OH, CCOO


(CH3)3(C)(CH3COO) 356− 369 VLE 21 1.00 Monton et al. (2005)

tert-Butanol, tert-butyl

acetateCHn, OH, CCOO


(CH3)3(C)(CH3COO) 319− 324 VLE 20 1.00 Monton et al. (2005)

ethanol, benzeneCHn, CH

[OH]n , OH,

ACHn


[OH]2 )(OH),

(ACH)6160− 279 SLE(org) d 22 0.20 Viala (1914)


[OH]n , OH,

ACHn


[OH]2 )(OH),

(ACH)6207− 279 SLE(org) d 10 0.20 Tarasenkov (1930)


[OH]n , OH,

ACHn


[OH]2 )(OH),

(ACH)6202− 277 SLE(org) d 44 0.20 Pickering (1893)


[OH]n , OH,

ACHn


[OH]2 )(OH),

(ACH)6328 VLE 17 1.00 Fu et al. (1995)


[OH]n , OH,

ACHn


[OH]2 )(OH),

(ACH)6333 VLE 17 1.00 Fu et al. (1995)


[OH]n , OH,

ACHn


[OH]2 )(OH),

(ACH)6341− 350 VLE 17 1.00 Cabezas et al. (1985)

2-propanol, benzeneCHn, CH

[OH]n , OH,

ACHn

(CH[alc]3 )2(CH[OH])(OH),

(ACH)6185− 279 SLE(org) d 23 0.20 Perrakis (1925)

1-butanol, benzeneCHn, CH

[OH]n , OH,

ACHn


[alc−tail]2 )2

(CH[OH]2 )(OH), (ACH)6

192− 279 SLE(org) d 19 0.20 Perrakis (1925)

cyclohexanol, benzeneCHn, CH

[OH]n , OH,

ACHn


(ACH)6241− 265 SLE(org) d 11 0.20 Lohmann et al. (1997)


[OH]n , OH,

ACHn




[OH]n , OH,

ACHn



ethanol, 2-hydroxybenzoic

acid

CHn, CH[OH]n , OH,

ACHn, ACOH, COOH


[OH]2 )(OH),

(ACH)4(AC)(ACOH)(COOH) 298− 348 SLE(org) d 11 0.10Shalmashi and Eliassi

(2008)

ethanol, phenolCHn, CH

[OH]n , OH,

ACHn, ACOH


[OH]2 )(OH),

(ACH)5(ACOH) 243− 313 SLE(org) d 9 0.20 Perrakis (1925)

ethanol, acetaldehyde CHn, CH[OH]n , OH, CHO


[OH]2 )(OH),

(CH3)(CHO) 146− 158 SLE(org) d 3 0.20 de Leeuw (1911)

ethanol, acetaldehyde CHn, OH, CHO(CH

[alc−tail]3 )(CH

[OH]2 )(OH),

(CH3)(CHO) 283 VLE 5 0.01 dAvila and Silva (1970)



[OH]2 )(OH),




[OH]2 )(OH),






d Reference



[OH]2 )(OH),




[OH]2 )(OH),

(CH3)(CHO) 302− 350 VLE 5 0.01 Suska (1979)



[OH]2 )(OH),

(CH3)(CH2)2(CHO) 323 VLE 9 1.0 Gmehling et al. (1988)



[OH]2 )(OH),




[OH]2 )(OH),




[OH]2 )(OH),




[OH]2 )(OH),

(CH3)(CH2)2(CHO) 346− 350 VLE 15 1.0 Gmehling et al. (1988)

acetic acid, acetone CHn, COOH, CHnCO(CH3)(COOH),

(CH3)(CH3CO) 173 SLE(org) d 1 0.20 Chesnokov (1969)


(CH3)(CH3CO) 244− 284 SLE(org) d 8 0.20 Carta and Dernini (1983)

acetic acid, acetone CHn, COOH,CHnCO(CH3)(COOH),

(CH3)(CH3CO) 245− 283 SLE(org) d 5 0.20 Carta and Dernini (1983)


(CH3)(CH3CO) 332− 383 VLE 10 1.00 Othmer (1943)


(CH3)(CH3CO) 308 VLE 12 0.10Waradzin and Surovy

(1975)



(1975)



(1975)


(CH3)(CH2)(CH3CO) 242− 290 SLE(org) d 12 0.20 Dallos et al. (1986)


(CH3)(CH2)(CH3CO) 353− 391 VLE 40 1.00 Fu et al. (1986)


(CH3)(CH2)(CH3CO) 353− 388 VLE 22 0.00 Xie et al. (2009)


(CH3)(CH2)(CH3CO) 303 VLE 12 0.00 Dallos et al. (1986)

acetic acid, 2-butanone CHn, COOH,CHnCO(CH3)(COOH),




butanoic acid, acetone CHn, COOH, CHnCO(CH3)(CH2)2(COOH),

(CH3)(CH3CO) 240− 268 SLE(org) d 12 0.20Proust and Fernandez

(1986)

butanoic acid, 2-butanone CHn, COOH, CHnCO(CH3)(CH2)2(COOH),

(CH3)(CH2)(CH3CO) 240− 268 SLE(org) d 12 0.20Proust and Fernandez

(1986)





d Reference


(CH3)(CH2)(CH3CO) 343 VLE 9 1.00Rasmussen and

Fredenslund (1977)


(CH3)(CH2)(CH3CO) 353 VLE 10 1.00Rasmussen and

Fredenslund (1977)

acetic acid, diethyl ether CHn, COOH, CHnO(CH3)(COOH),

(CH3)2(CH2)CH2O 207− 289 SLE(org) d 40 0.20 Pickering (1893)


(CH3)2(CH2)CH2O 293− 343 VLE 7 1.00Meehan and Murphy

(1965)



(1965)



(1965)


(CH3)(CH2)(CH3COO) 323 VLE 9 1.00 Miyamoto et al. (2001)

hexadecanoic acid

(palmitic acid), ethyl

acetate

CHn, COOH, CCOO(CH3)(CH2)14(COOH),

(CH3)(CH2)(CH3COO) 243− 273 SLE(org) d 4 0.20 Kolb (1959)

octadecanoic acid (stearic

acid), ethyl acetateCHn, COOH, CCOO

(CH3)(CH2)16(COOH),

(CH3)(CH2)(CH3COO) 253− 283 SLE(org) d 4 0.20 Kolb (1959)

acetic acid, benzene CHn, COOH, ACHn(CH3)(COOH), (ACH)6 264− 289 SLE(org) d 20 0.20 Roloff (1895)

acetic acid, benzene CHn, COOH, ACHn(CH3)(COOH), (ACH)6 274− 289 SLE(org) d 8 0.20 Roloff (1895)

acetic acid, benzene CHn, COOH, ACHn(CH3)(COOH), (ACH)6 313 VLE 9 0.20 Miyamoto et al. (2000)

acetic acid, benzene CHn, COOH, ACHn(CH3)(COOH), (ACH)6 353− 387 VLE 15 1.00 Haughton (1967)

acetic acid, benzene CHn, COOH, ACHn(CH3)(COOH), (ACH)6 296− 322 VLE 12 1.00 Carta et al. (1979)

acetic acid, acetaldehyde CHn, COOH, CHO(CH3)(COOH), (CH3)(CHO)

295− 386 VLE 33 1.00Shanghai-Inst. and

Zhejiang (1978)

acetic acid, butyraldehyde CHn, COOH, CHO(CH3)(COOH),

(CH3)(CH2)2(CHO) 323 VLE 9 1.00 Miyamoto et al. (2001)

propanoic acid,

butyraldehydeCHn, COOH, CHO

(CH3)(CH2)(COOH),

(CH3)(CH2)2(CHO) 323 VLE 9 1.00 Miyamoto et al. (2001)

acetone, 2-methoxy-2-

methylpropaneCHn,CHnCO, CHnO

(CH3)(CH3CO),

(CH3)3(C)(CH3O) 322− 326 VLE 19 1.00 Mejıa et al. (2008)

2-butanone,

2-ethoxyethanol

CHn, CH[OH]n , CHnCO,

CHnO, OH

(CH3)(CH2)(CH3CO),

(CH3)(CH2)(CH[OH]2 )(CH2O)(OH) 330 VLE 9 1.00

Naumann and Wagner

(1986)


(CH3)(CH2)(CH3COO) 330− 348 VLE 16 1.00Subrahmanyam and

Murty (1964)


(CH3)(CH2)(CH3COO) 328− 348 VLE 16 1.00 Gilburd et al. (1979)


(CH3)(CH2)(CH3COO) 313− 330 VLE 12 1.00 Gilburd et al. (1981)

acetone, octadecanoic acid

methyl ester (methyl

stearate)

CHn, CHnCO, CCOO(CH3)(CH3CO),

(CH3)(CH2)16(CH3COO) 265− 311 SLE(org) d 6 0.10 Bailey et al. (1970)





d Reference

acetone, octadecanoic acid

ethyl ester (ethyl stearate)CHn, CHnCO, CCOO

(CH3)(CH3CO),

(CH3)2(CH2)16(CH3COO) 263− 303 SLE(org) d 5 0.20 Bailey et al. (1970)

acetone, benzene CHn, CHnCO, ACHn(CH3)(CH3CO), (ACH)6 318 VLE 11 1.00 Brown and Smith (1957)

acetone, benzene CHn, CHnCO, ACHn(CH3)(CH3CO), (ACH)6 330− 348 VLE 21 1.00 Kurihara et al. (1998)

2-heptanone, benzene CHn, CHnCO, ACHn

(CH3)(CH2)4(CH3CO),

(ACH)6228− 279 SLE(org) d 13 0.20 Fiege et al. (1996)


(CH3)(CH2)4(CH3CO),



(CH3)2(CH2)3(CH2CO),











acetone, acetaldehyde CHn, CHnCO, CHO(CH3)(CH3CO), (CH3)

(CHO) 296− 326 VLE 8 0.20 Tikhonova et al. (1970)

acetone, propionaldehyde CHn, CHnCO, CHO(CH3)(CH3CO),

(CH3)(CH2)(CHO) 322− 329 VLE 13 1.00 Danciu (1970)

2-methoxyethanol, methyl

acetate

CHn, CH[OH]n , OH,

CHnO, CCOO

(CH2)(CH[OH]2 )(CH3O)(OH),

(CH3) (CH3COO) 298 VLE 9 0.00 Martin et al. (1994)

2-methoxyethanol, ethyl

acetate

CHn, CH[OH]n , OH,

CHnO, CCOO


(CH3)(CH2)(CH3COO) 343 VLE 13 0.20 Chandak et al. (1977)


acetate

CHn, CH[OH]n , OH,

CHnO, CCOO


(CH3)(CH2)(CH3COO) 353 VLE 12 0.20 Chandak et al. (1977)


acetate

CHn, CH[OH]n , OH,

CHnO, CCOO


(CH3)(CH2)(CH3COO) 351− 395 VLE 14 0.20 Chandak et al. (1977)

2-ethoxyethanol, methyl

acetate

CHn, CH[OH]n , OH,

CHnO, CCOO


(OH), (CH3)(CH3COO) 298 VLE 9 0.00 Martin et al. (1994)

2-ethoxyethanol, ethyl

acetate

CHn, CH[OH]n , OH,

CHnO, CCOO

(CH3)


(CH3)(CH2)(CH3COO)351− 402 VLE 17 0.20

Thorat and Nageshwar

(1988)

diethyl ether, benzene CHn, CHnO, ACHn(CH3)2(CH2)CH2O, (ACH)6 197− 278 SLE(org) d 37 0.20 Pickering (1893)

2-butoxyethanol, benzeneCHn, CHnO, CH

[OH]n ,

OH, ACHn


(OH), (ACH)6217− 279 SLE(org) d 18 0.20 Negadi et al. (2006)

1-methoxy-2-propanol,

benzene

CHn, CH[OH]n , OH,

CHnO, ACHn


(OH), (ACH)6220− 279 SLE(org) d 18 0.20 Negadi et al. (2006)

2-ethoxyethanol, phenolCHn, CH

[OH]n , OH,

CHnO, ACHn


(OH), (ACH)5(ACOH) 363 VLE 17 0.10 Chylinski et al. (2001)


[OH]n , OH,

CHnO, ACHn







d Reference


[OH]n , OH,

CHnO, ACHn



diethyl ether, acetaldehyde CHn, CHnO, ACHn

(CH3)2(CH2)(CH2O),

(CH3)(CHO) 293− 304 VLE 10 1.00 Suska (1979)

ethyl acetate, benzene CHn,CCOO, ACHn

(CH3)(CH2)(CH3COO),

(ACH)6350− 353 VLE 19 1.00

Carr and Kropholler

(1962)

ethyl acetate,

2-hydroxybenzoic acid

CHn, CCOO, ACHn,

ACOH, COOH

(CH3)(CH2)(CH3COO),

(ACH)4(AC)(ACOH)(COOH) 298− 348 SLE 11 1.00Shalmashi and Eliassi

(2008)

methyl acetate,

butyraldehydeCHn, CCOO, CHO

(CH3)(CH3COO),

(CH3)(CH2)2(CHO) 313 VLE 15 1.00 Radnai et al. (1987)

methyl acetate,

butyraldehydeCHn, CCOO, CHO

(CH3)(CH3COO),

(CH3)(CH2)2(CHO) 323 VLE 15 1.00 Radnai et al. (1987)

benzene, butyraldehyde CHn, ACHn, CHO(ACH)6, (CH3)(CH2)2(CHO)

353 VLE 5 1.00 Leu et al. (1989)

benzene, butyraldehyde CHn, ACHn, CHO(ACH)6, (CH3)(CH2)2(CHO)

393 VLE 6 1.00 Leu et al. (1989)

a The datasets denoted by Ganbavale et al. are published in the companion paper to this

article.b M5 is a mixture of dicarboxylic acids consisting of: malic acid (2) + malonic acid (3) +

maleic acid (4) + glutaric acid (5) + methylsuccinic acid (6), where the numbers in brakets

indicate the component number with in the M5 multicomponent mixture.c The chemical subgroup formulas of the M5 components are given in the table for the

individual components, except for maleic acid, for which the subgroup formula is:

(CH=CH)(COOH)2.

d SLE data where the equilibrium is with respect to an organic compound in a solid

(crystalline) state.


Table 3.2: Matrix of AIOMFAC short-range group interaction parameters. Pa-

rameter values for a(i, j) (units of K) are from the literature a, b(i, j) (units of K),

c(i, j) (dimensionless) are determined in this study.

group no. j → 1 2 3 7 8 9 10 11 13 65 66 67 68 69

i ↓ main groups CHn C=C ACHn H2O ACOH CHnCO CHO[aldehyde] CCOO CHnO[ether] COOH CH[alc]n CH

[alc−tail]n CH

[OH]n OH

1 CHn a(i, j): 0.0 8.6020 ×101 6.1130 ×101 1.3180 ×103 1.3330 ×103 4.7640 ×102 6.7700 ×102 2.3210 ×102 2.5150 ×102 6.6350 ×102 0.0 c 0.0 c 0.0 c 9.8650 ×102

b(i, j): 0.0 0.0 c 2.0000 ×102 8.7765 ×102 1.3330 ×103 -4.7640 ×102 2.0000 ×102 2.3210 ×102 1.5817 ×102 6.6350 ×102 0.0 c 0.0 c 0.0 c 9.8650 ×102

c(i, j): 0.0 0.0 c 4.0000 ×10−1 2.6360 ×100 2.6660 ×100 9.5280 ×10−1 4.0000 ×10−1 4.6420 ×10−1 1.8636 ×10−1 1.3270 ×100 0.0 c 0.0 c 0.0 c 1.9730 ×100

2 C=C a(i, j): -3.5360 ×101 0.0 3.8810 ×101 2.7060 ×102 5.2610 ×102 1.8260 ×102 4.4880 ×102 3.7850 ×101 2.1450 ×102 3.1890 ×102 -3.5360 ×101 -3.5360 ×101 -3.5360 ×101 5.2410 ×102

b(i, j): 0.0 c 0.0 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c

c(i, j): 0.0 c 0.0 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c

3 ACHn a(i, j): -1.1120 ×101 3.4460 ×100 0.0 9.0380 ×102 1.3290 ×103 2.5770 ×101 3.4730 ×102 5.9940 ×100 3.2140 ×101 5.3740 ×102 -1.1120 ×101 -1.1120 ×101 -1.1120 ×101 6.3610 ×102

b(i, j): 5.3819 ×101 0.0 c 0.0 9.0380 ×102 -1.3290 ×103 -4.7477 ×101 -3.4730 ×102 0.0 c -4.3180 ×100 -5.3740 ×102 5.3819 ×101 5.3819 ×101 5.3819 ×101 -6.3610 ×102

c(i, j): 4.0000 ×10−1 0.0 c 0.0 1.8076 ×100 -2.4726 ×100 0.0 c 0.0 c 0.0 c 4.0000 ×10−1 -1.3577 ×10−1 4.0000 ×10−1 4.0000 ×10−1 4.0000 ×10−1 1.2722 ×100

7 H2O a(i, j): 3.0000 ×102 4.9610 ×102 3.6230 ×102 0.0 3.2450 ×102 -1.9540 ×102 -1.1600 ×102 7.2870 ×101 5.4050 ×102 -6.9290 ×101 1.6230 ×102 3.6210 ×102 -8.9710 ×101 -1.5300 ×102

b(i, j): 1.2542 ×101 0.0 c -3.6230 ×102 0.0 5.4808 ×101 8.2298 ×101 -5.9018 ×100 4.4441 ×100 1.7880 ×102 -8.6552 ×101 -2.0000 ×102 -2.3073 ×102 -2.0000 ×102 1.6393 ×102

c(i, j): -6.0000 ×10−1 0.0 c 7.2460 ×10−1 0.0 6.0210 ×10−1 -4.0000 ×10−1 0.0 c -4.0000 ×10−1 5.5486 ×10−1 -4.0000 ×10−1 3.6193 ×10−1 -7.2420 ×10−1 -4.0000 ×10−1 -4.0000 ×10−1

8 ACOH a(i, j): 2.7580 ×102 2.1750 ×102 2.5340 ×101 -6.0180 ×102 0.0 -3.5610 ×102 -2.7110 ×102 -4.4940 ×102 -1.6290 ×102 4.0890 ×102 2.7580 ×102 2.7580 ×102 2.7580 ×102 -4.5160 ×102

b(i, j): 2.7580 ×102 0.0 c 1.6367 ×102 6.1488 ×101 0.0 3.5610 ×102 0.0 c 3.9985 ×102 0.0 c 4.0890 ×102 2.7580 ×102 2.7580 ×102 2.7580 ×102 4.5160 ×102

c(i, j): 3.2281 ×10−1 0.0 c -4.0000 ×10−1 1.2036 ×100 0.0 0.0 c 0.0 c 0.0 c 0.0 c 8.8840 ×10−2 3.2281 ×10−1 3.2281 ×10−1 3.2281 ×10−1 9.0320 ×10−1

9 CHnCO a(i, j): 2.6760 ×101 4.2920 ×101 1.4010 ×102 4.7250 ×102 -1.3310 ×102 0.0 -3.7360 ×101 -2.1370 ×102 -1.0360 ×102 6.6940 ×102 2.6760 ×101 2.6760 ×101 2.6760 ×101 1.6450 ×102

b(i, j): 4.5409 ×101 0.0 c -1.8682 ×102 1.0675 ×102 2.0000 ×102 0.0 0.0 c 2.1370 ×102 0.0 c -5.7894 ×102 4.5409 ×101 4.5409 ×101 4.5409 ×101 2.0000 ×102

c(i, j): -4.0000 ×10−1 0.0 c 0.0 c -9.4500 ×10−1 0.0 c 0.0 0.0 c 0.0 c 0.0 c 1.9383 ×10−1 -4.0000 ×10−1 -4.0000 ×10−1 -4.0000 ×10−1 4.0000 ×10−1

10 CHO[aldehyde] a(i, j): 5.0570 ×102 5.6300 ×101 2.3390 ×101 4.8080 ×102 -1.5560 ×102 1.2800 ×102 0.0 -1.1030 ×102 3.0410 ×102 4.9750 ×102 5.0570 ×102 5.0570 ×102 5.0570 ×102 5.2900 ×102

b(i, j): 5.0570 ×102 0.0 c -2.0000 ×102 4.8080 ×102 0.0 c 0.0 c 0.0 0.0 c 0.0 c 4.9750 ×102 5.0570 ×102 5.0570 ×102 5.0570 ×102 -5.2900 ×102

c(i, j): 1.0114 ×100 0.0 c 0.0 c 0.0 c 0.0 c 0.0 c 0.0 0.0 c 0.0 c 9.9500 ×10−1 1.0114 ×100 1.0114 ×100 1.0114 ×100 -1.0580 ×100

11 CCOO a(i, j): 1.1480 ×102 1.3210 ×102 8.5840 ×101 2.0080 ×102 -3.6720 ×101 3.7220 ×102 1.8510 ×102 0.0 -2.3570 ×102 6.6020 ×102 1.1480 ×102 1.1480 ×102 1.1480 ×102 2.4540 ×102

b(i, j): 2.0000 ×102 0.0 c 0.0 c 1.3043 ×102 5.5875 ×101 -2.2930 ×101 0.0 c 0.0 2.2988 ×100 -3.5339 ×101 2.0000 ×102 2.0000 ×102 2.0000 ×102 2.4540 ×102

c(i, j): 4.0000 ×10−1 0.0 c 0.0 c -4.0160 ×10−1 0.0 c 0.0 c 0.0 c 0.0 5.9724 ×10−2 0.0 c 4.0000 ×10−1 4.0000 ×10−1 4.0000 ×10−1 4.9080 ×10−1

13 CHnO[ether] a(i, j): 8.3360 ×101 2.6510 ×101 5.2130 ×101 -3.1470 ×102 -1.7850 ×102 1.9110 ×102 -7.8380 ×100 4.6130 ×102 0.0 6.6460 ×102 8.3360 ×101 8.3360 ×101 8.3360 ×101 2.3770 ×102

b(i, j): 2.0000 ×102 0.0 c -2.0000 ×102 -3.1470 ×102 0.0 c 0.0 c 0.0 c -4.6130 ×102 0.0 -6.6460 ×102 2.0000 ×102 2.0000 ×102 2.0000 ×102 2.3770 ×102

c(i, j): -1.0905 ×10−1 0.0 c -6.1186 ×10−2 -6.2940 ×10−1 0.0 c 0.0 c 0.0 c -1.5826 ×10−1 0.0 7.3512 ×10−1 -1.0905 ×10−1 -1.0905 ×10−1 -1.0905 ×10−1 4.7540 ×10−1

65 COOH a(i, j): 3.1530 ×102 1.2640 ×103 6.2320 ×101 -1.4588 ×102 -1.1000 ×101 -2.9780 ×102 -1.6550 ×102 -2.5630 ×102 -3.3850 ×102 0.0 3.1530 ×102 3.1530 ×102 3.1530 ×102 -1.0303 ×102

b(i, j): 3.1530 ×102 0.0 c -1.9014 ×101 3.8158 ×101 2.0000 ×102 -1.1715 ×102 -2.0000 ×102 2.5630 ×102 1.1390 ×101 0.0 3.1530 ×102 3.1530 ×102 3.1530 ×102 2.0000 ×102

c(i, j): 6.3060 ×10−1 0.0 c 3.5606 ×10−1 -4.0000 ×10−1 -3.4849 ×10−2 -5.9560 ×10−1 4.0000 ×10−1 0.0 c 6.7700 ×10−1 0.0 6.3060 ×10−1 6.3060 ×10−1 6.3060 ×10−1 4.0000 ×10−1

66 CH[alc]n a(i, j): 0.0 c 8.6020 ×101 6.1130 ×101 1.8900 ×103 1.3330 ×103 4.7640 ×102 6.7700 ×102 2.3210 ×102 2.5150 ×102 6.6350 ×102 0.0 0.0 c 0.0 c 9.8650 ×102

b(i, j): 0.0 c 0.0 c 2.0000 ×102 1.8282 ×103 1.3330 ×103 -4.7640 ×102 2.0000 ×102 2.3210 ×102 1.5817 ×102 6.6350 ×102 0.0 0.0 c 0.0 c 9.8650 ×102

c(i, j): 0.0 c 0.0 c 4.0000 ×10−1 3.7800 ×100 2.6660 ×100 9.5280 ×10−1 4.0000 ×10−1 4.6420 ×10−1 1.8636 ×10−1 1.3270 ×100 0.0 0.0 c 0.0 c 1.9730 ×100

67 CH[alc−tail]n a(i, j): 0.0 c 8.6020 ×101 6.1130 ×101 1.3250 ×103 1.3330 ×103 4.7640 ×102 6.7700 ×102 2.3210 ×102 2.5150 ×102 6.6350 ×102 0.0 c 0.0 0.0 c 9.8650 ×102

b(i, j): 0.0 c 0.0 c 2.0000 ×102 6.7344 ×102 1.3330 ×103 -4.7640 ×102 2.0000 ×102 2.3210 ×102 1.5817 ×102 6.6350 ×102 0.0 c 0.0 0.0 c 9.8650 ×102

c(i, j): 0.0 c 0.0 c 4.0000 ×10−1 -2.6500 ×100 2.6660 ×100 9.5280 ×10−1 4.0000 ×10−1 4.6420 ×10−1 1.8636 ×10−1 1.3270 ×100 0.0 c 0.0 0.0 c 1.9730 ×100

68 CH[OH]n a(i, j): 0.0 c 8.6020 ×101 6.1130 ×101 2.3140 ×103 1.3330 ×103 4.7640 ×102 6.7700 ×102 2.3210 ×102 2.5150 ×102 6.6350 ×102 0.0 c 0.0 c 0.0 9.8650 ×102

b(i, j): 0.0 c 0.0 c 2.0000 ×102 -8.0335 ×102 1.3330 ×103 -4.7640 ×102 2.0000 ×102 2.3210 ×102 1.5817 ×102 6.6350 ×102 0.0 c 0.0 c 0.0 9.8650 ×102

c(i, j): 0.0 c 0.0 c 4.0000 ×10−1 -8.3200 ×10−1 2.6660 ×100 9.5280 ×10−1 4.0000 ×10−1 4.6420 ×10−1 1.8636 ×10−1 1.3270 ×100 0.0 c 0.0 c 0.0 1.9730 ×100

69 OH a(i, j): 1.5640 ×102 4.5700 ×102 8.9600 ×101 2.7640 ×102 -2.5970 ×102 8.4000 ×101 -2.0360 ×102 1.0110 ×102 2.8060 ×101 2.2439 ×102 1.5640 ×102 1.5640 ×102 1.5640 ×102 0.0

b(i, j): 2.0000 ×102 0.0 c 9.7617 ×101 2.7640 ×102 2.5970 ×102 2.0000 ×102 -2.0360 ×102 -6.4775 ×101 -2.6146 ×101 2.2439 ×102 2.0000 ×102 2.0000 ×102 2.0000 ×102 0.0

c(i, j): -4.0000 ×10−1 0.0 c 4.0000 ×10−1 -5.5280 ×10−1 2.7684 ×10−1 4.0000 ×10−1 -4.0720 ×10−1 4.0000 ×10−1 -3.1087 ×10−1 4.4878 ×10−1 -4.0000 ×10−1 -4.0000 ×10−1 -4.0000 ×10−1 0.0

a The values of ai,j for OH and COOH interactions with H2O are taken from Marcolli and

Peter (2005) and Peng et al. (2001), respectively. For all other functional groups the ai,j values

from the revised parameter set of Hansen et al. (1991) are used.

c Main group interactions bi,j and ci,j are set to zero since appropriate data to determine

these interactions are missing.


3.7

3.7.1 Appendix

The bulk water activities, aw, were measured for aqueous organic solu-

tions using an AquaLab water activity meter (Model 3TE, Decagon devices,

USA). The instrument applies the chilled mirror technique to determine the

dewpoint temperature of air equilibrated with the aqueous solution being

measured. The internal temperature control allows to perform measure-

ments under stable temperature from 289 - 313 K. The standard sample

block with a specified error of ±0.003 in aw was used for most measure-

ments. For aqueous mixtures with more volatile polyols (2,5-hexanediol, 1,2,6-

hexanetriol and glycerol), the volatile sample block, available as an accessory

to the instrument, was used to perform measurements. The manufacturer-

specified error for the volatile sample block is ±0.015 in aw. Potential In-

strument offsets were frequently diagnosed and corrected and the perfor-

mance of the sample block was controlled and readjusted with reference

samples of known water activity. All measurements were performed at sev-

eral temperatures in the range 289 K to 313 K. The substances were pur-

chased from Sigma-Aldrich in the best available purity. The following com-

pounds were investigated: glycerol (Sigma, >99%), 2,5-hexanediol (Fluka,

>97%), 1,2,6-hexanetriol (Fluka, >95%), 1,2,7,8-octanetetrol (Fluka, >97%),

2,2,6,6-tetrakis(hydroxymethyl)cyclohexanol (Aldrich, 97%), DL-4-hydroxy-

3-methoxy mandelic acid (Sigma, >95%), raffinose (Sigma, >98%). The sub-

stances were used without further purification. The water/polyol mixtures

were prepared by mass percent with MilliQ deionized water using an ana-

lytical balance. Each solution was measured at least three times at each

temperature.

3.7. 127

Table 3.3: Bulk water activity (aw) measurementsa of water (1) + glycerol (2) solu-

tions at three different temperatures at atmospheric pressure. Solution compositions

are given in mole fraction (x2) of the organic (component 2).

x2 aw(T = 289.15 K) aw(T = 298.15 K) aw(T = 313.15 K)

0.01769 0.976 0.980 0.980

0.03477 0.964 0.964 0.970

0.05128 0.956 0.953 0.955

0.06721 0.937 0.935 0.940

0.08263 0.916 0.920 0.920

0.09754 0.896 0.895 0.910

0.11199 0.872 0.875 0.883

0.12595 0.854 0.862 0.864

0.13950 0.838 0.841 0.856

0.15263 0.823 0.826 0.833

0.16960 0.802 0.802 0.815

0.22685 0.728 0.732 0.739

0.31338 0.622 0.628 0.628

0.43896 0.492 0.491 0.497

0.63774 0.297 0.298 0.299

a The accuracy of the water activity measurements is specified as ± 0.003 (absolute range) in

aw and ≤ 0.1 K in temperature.


Table 3.4: Bulk water activity (aw) measurements a of water (1) + 2,5-hexanediol

(2) solutions at three different temperatures at atmospheric pressure. Solution com-

positions are given in mole fraction (x2) of the organic (component 2).

x2 aw(T = 289.15 K) aw(T = 298.15 K) aw(T = 313.15 K)

0.0167 0.971 0.978 0.975

0.0365 0.974 0.978 0.973

0.0616 0.943 0.955 0.972

0.0934 0.917 0.937 0.953

0.1325 0.897 0.912 0.933

0.1790 0.882 0.894 0.912

0.2734 0.825 0.849 0.860

0.3607 0.781 0.790 0.804

0.5630 0.605 0.620 0.618

aThe accuracy of the water activity measurements is specified as ± 0.003 (absolute range) in


3.7. 129

Table 3.5: Bulk water activity (aw) measurements a of water (1) + 1,2,6-

hexanetriol (2) solutions at three different temperatures at atmospheric pressure.

Solution compositions are given in mole fraction (x2) of the organic (component 2).

x2 aw(T = 289.15 K) aw(T = 298.15 K) aw(T = 313.15 K)

0.014 0.975 0.988 0.985

0.032 0.957 0.974 0.973

0.055 0.944 0.962 0.966

0.080 0.919 0.934 0.943

0.114 0.890 0.895 0.909

0.171 0.834 0.847 0.853

0.216 0.784 0.802 0.802

0.340 0.664 0.673 0.681

0.539 0.456 0.458 0.465




Table 3.6: Bulk water activity (aw) measurements a of water (1) + 1,2,7,8-

octantetrol (2) solutions at three different temperatures at atmospheric pressure.

Solution compositions are given in mole fraction (x2) of the organic (component 2).

x2 aw(T = 289.15 K) aw(T = 298.15 K) aw(T = 313.15 K)

0.0109 0.987 0.988 0.993

0.0245 0.976 0.977 0.981

0.0407 0.963 0.965 0.969

0.0650 0.944 0.946 0.953

0.0878 0.927 0.924 0.933

0.1329 0.877 0.887 0.901

0.1890 0.803 0.817 0.837

0.2911 0.605 0.643 0.667



3.7. 131

Table 3.7: Bulk water activity (aw) measurements a of water (1) + 2,2,6,6-

tetrakis(hydroxymethyl)cyclohexanol (2) solutions at three different temperatures at

atmospheric pressure. Solution compositions are given in mole fraction (x2) of the

organic (component 2).

x2 aw(T = 289.15 K) aw(T = 298.15 K) aw(T = 313.15 K)

0.0999 0.990 0.992 0.993

0.1943 0.987 0.982 0.990

0.3029 0.973 0.974 0.979

0.3963 0.961 0.964 0.968

0.5010 0.929 0.938 0.942

0.6000 0.900 0.909 0.916

0.6519 0.881 0.887 0.895

0.7065 0.821 0.828 0.840



Table 3.8: Bulk water activity (aw) measurements a of water (1) + vanillylmandelic

acid (2) solutions at three different temperatures at atmospheric pressure. Solution

compositions are given in mole fraction (x2) of the organic (component 2).

x2 aw(T = 289.15 K) aw(T = 298.15 K) aw(T = 313.15 K)

0.0102 0.997 0.999 0.996

0.0354 0.981 0.987 0.985

0.0844 0.963 0.965 0.965

0.1201 0.940 0.945 0.949

0.1712 0.891 0.898 0.906

0.2107 0.851 0.857 0.860




Table 3.9: Bulk water activity (aw) measurements a of water (1) + raffinose (2)

solutions at three different temperatures at atmospheric pressure. Solution composi-

tions are given in mole fraction (x2) of the organic (component 2).

x2 aw(T = 289.15 K) aw(T = 298.15 K) aw(T = 313.15 K)

0.0089 0.993 0.993 0.992

0.0232 0.967 0.969 0.973

0.0364 0.938 0.944 0.948

0.0507 0.910 0.913 0.917

0.0781 0.835



Table 3.10: Bulk water activity (aw) measurements a of water (1) + sucrose (2)

solutions at three different temperatures at atmospheric pressure. Solution composi-

tions are given in mole fraction (x2) of the organic (component 2).

x2 aw(T = 289.15 K) aw(T = 298.15 K) aw(T = 313.15 K)

0.0104 0.992 0.992 0.998

0.0162 0.981 0.988 0.992

0.0230 0.977 0.977 0.985

0.0306 0.965 0.971 0.977

0.0394 0.952 0.955 0.963

0.0487 0.938 0.939 0.946

0.0606 0.906 0.914 0.922

0.0732 0.883 0.888 0.893



Chapter 4

Experimental determination of

the temperature dependence of

water activities for some

atmospherically relevant

aqueous organic solutions

G. Ganbavale 1, C. Marcolli 1, U. K. Krieger 1, A. Zuend 1,2,3, G. Stratmann 4,

T. Peter 1

1 Institute for Atmospheric and Climate Science, ETH Zurich, Zurich,

Switzerland2 Department of Chemical Engineering, California Institute of Technology,

Pasadena, California, USA3 Department of Atmospheric and Oceanic Sciences, McGill University, Mon-

treal, Quebec, Canada4 Department of Atmospheric Physics, DLR Oberpfaffenhofen, Germany

This chapter is a reproduction of a corresponding article, which is in prepa-

ration to be submitted to the journal “Atmospheric Chemistry and Physics”.

133

134 Chapter 4. Experimental temperature dependence of water activity

The layout of the article as well as the section, figure, and table numberings

have been adapted to match with the thesis structuring. Cited literature is

referenced in the bibliography of the thesis.


This work presents experimental data of the temperature dependence of water

activity in aqueous organic solutions relevant for tropospheric conditions (200

- 273 K). Water activity (aw) at low temperatures is a crucial parameter for

predicting homogeneous ice nucleation. We investigated temperature depen-

dent water activities, ice freezing and melting temperatures of solutions, and

vapour pressures of a selection of atmospherically relevant aqueous organic

systems. To measure aw over a wide composition range and with a focus

on low temperatures, we use various aw measurement techniques and instru-

ments: a dew point water activity meter, an electrodynamic balance (EDB),

differential scanning calorimetry (DSC) and a setup to measure the total gas

phase pressure at equilibrium over aqueous solutions. Water activity mea-

surements were performed for aqueous multicomponent and multifunctional

organic mixtures containing the functional groups typically found in the at-

mospheric organic aerosol, such as hydroxyl, carboxyl, ketone, ether, ester,

and aromatic groups. The aqueous organic systems studied at several fixed

compositions over a considerable temperature range show differing tempera-

ture dependence. Aqueous organic systems of 1,4-butanediol and methoxy-

acetic acid show a moderate decrease in aw with decreasing temperature. The

aqueous M5 system (a multicomponent system containing five different dicar-

boxylic acids) and aqueous 2-(2-ethoxyethoxy)ethanol solutions both show a

strong increase of water activity with decreasing temperature at high solute

concentrations for T < 270 K and 260 K, respectively. These measurements

show that temperature dependence of aw can be reversed at low temperatures

and that linear extrapolations of high temperature data may lead to erroneous

predictions. To avoid this, experimentally determined aw at low temperature

are needed to improve thermodynamic models towards lower temperatures

and for improved predictions of the ice nucleation ability of organic-water

systems.

4.1 Introduction

Organic compounds account for a large fraction of airborne particulate mat-

ter. They constitute around 50 % of the total mass of the fine aerosol fraction


in the continental mid-latitudes (Saxena and Hildemann, 1996; Novakov et al.,

1997; Murphy et al., 2006; Jimenez et al., 2009) while in the tropics they may

contribute up to 90 % (Yamasoe et al., 2000; Roberts et al., 2002). In the up-

per troposphere a high fraction of organic aerosols are internally mixed with

sulphate aerosols (Murphy et al., 2006, 2007). The organic aerosol fraction is

expected to remain in a liquid or amorphous (viscous) state since the large

number of organic compounds depresses the temperature at which crystal-

lization takes place (Marcolli et al., 2004). Studies show that the presence of

an organic aerosol fraction may inhibit ice nucleation and growth (DeMott

et al., 2003; Cziczo et al., 2004; Peter et al., 2006; Knopf and Lopez, 2009).

The organic aerosol fraction contributes to aerosol effects in the atmosphere

through interactions with water vapour, radiation, precipitation, and trace

gases (Fuzzi et al., 2006). In turn, these interactions influence the physical and

chemical properties of aerosol particles such as physical state, hygroscopicity,

size, and shape (e.g., Ming and Russell, 2002; Kanakidou et al., 2005; Zobrist

et al., 2008; Ciobanu et al., 2009; Mikhailov et al., 2009; Reid et al., 2011;

Song et al., 2012). The aerosol scattering intensity depends on growth and

evaporation of the particles due to uptake and release of water vapour driven

by changes in ambient relative humidity (RH) (Carrico et al., 2003; Baynard

et al., 2006; Zieger et al., 2013). For an accurate description of this process,

the hygroscopicity of the typical aerosol compositions have to be known. The

hygroscopicities of most organic mixtures depend on temperature. Water ac-

tivity aw is equal to RH, provided that the aqueous aerosol particles are in

equilibrium with the surrounding gas phase and are sufficiently large so that

the Kelvin effect is negligible. However, aw data of aqueous organic com-

pounds at low temperatures are scarce. Therefore, most estimates of the RH

dependence of the direct aerosol effect rely on data at or close to room tem-

perature. A lot of uncertainty is involved in trying to understand the upper

tropospheric ice nucleation process for use of better process paramterisations

in climate models (Knopf and Lopez, 2009; Swanson, 2009). The uncertainty

in predicted homogeneous ice nucleation temperatures is stated as ±0.025 of

aw at homogeneous melting temperatures and ±0.05 of aw at homogeneous

freezing temperatures (Koop et al., 2000; Koop, 2004; Knopf and Rigg, 2011).

These uncertainties may result in significantly lower or higher values of homo-


geneous ice nucleation rate coefficients (Jhom), which may significantly affect

predictions of the onset of ice crystal formation in cloud microphysical models

(Knopf and Rigg, 2011). However, at low tropospheric temperatures, hygro-

scopicity may be different and water uptake and release may be retarded due

to slow diffusion of water into highly viscous amorphous phases (glasses). (Zo-

brist et al., 2008, 2011; Bones et al., 2012).

Cloud formation and homogeneous ice nucleation in the upper troposphere

occur on aqueous aerosol particles that grow into ice crystals by uptake of

supersaturated water vapour. Unless solution droplets become glassy, homo-

geneous ice nucleation in supercooled aqueous solutions does not depend on

the specific nature of the solute; rather knowing the thermodynamic proper-

ties of the solution in terms of water activity (aw) is sufficient, which implicitly

accounts for specific properties of the solutes Koop et al. (2000) and Koop and

Zobrist (2009). Thus, aw of solutions is a crucial parameter for homogeneous

ice nucleation.

For a correct description of the ice nucleation process, deviations from ideal

mixing have to be taken into account. This is achieved by using activity co-

efficients to describe solution non-ideality. For water, with a thermodynamic

activity (aw) defined on a mole fraction basis, its mole fraction-based activity

coefficient is defined as γw = aw/xw, where xw is the water mole fraction

of the solution. Activity coefficients may exhibit considerable temperature

dependence, which has to be parametrised explicitly in thermodynamic mod-

els in order to give accurate predictions for the level of non-ideality over a

large range of temperatures. However, for aqueous organic solutions, ther-

modynamic models based on the UNIQUAC (UNIversal QUAsi Chemical)

model (Abrams and Prausnitz, 1975) or its group contribution version UNI-

FAC (UNIquac Functional group Activity Coefficients) (Fredenslund et al.,

1975) do not provide reliable predictions of activity coefficients, when they

are used outside of the temperature range for which the they have been pa-

rameterised, i.e., at T < 290 K or T > 400 K. Improvement of these models

for atmospheric conditions is strongly limited by the availability of reliable

activity data for T < 290 K (Saxena and Hildemann, 1997; Lohmann et al.,

2001; Marcolli and Peter, 2005). An approach of performing aw measurements

by combining different measurement techniques (e.g., EDB, DSC, total pres-


sure measurements) can provide the experimental data input for improved

parametrisations of these thermodynamic models especially at low temperat-

ues. Studies of the temperature dependence of activity coefficients have been

carried out for some atmospherically relevant inorganic acids and salts such as

H2SO4, (NH4)2SO4, and NH4NO3 (Knopf et al., 2003; Tang and Munkelwitz,

1993; Rodebush, 1918; Clegg et al., 1998), revealing quite distinct tendencies:

aw of dilute H2SO4 solutions is nearly independent of temperature, while in

case of NH4NO3, aw increases with decreasing temperature (Koop, 2004).

For organic solutions, Zobrist et al. (2003) compared the aw data of various

poly(ethylene glycol) (PEG) solutions in the stable and supercooled range

and noticed that the aw of PEG solutions decreases with decreasing temper-

ature. The influence of temperature on aw becomes more pronounced with

decreasing temperature as well as for increasing solute concentration. Studies

by Zobrist et al. (2008) determined the temperature dependence of activity

coefficients in polyols and sugars at atmospheric pressure in the temperature

range from the ice melting curve up to 313 K and used these data to convert

ice freezing temperatures from the mass fraction composition to the aw scale.

They found that if the temperature dependence is neglected, errors on the

order of 10 - 15 % may result for aw at the homogeneous ice freezing tem-

perature. These examples show that in the case of aqueous organic solutions,

the temperature dependence of aw can be atmospherically important.

Low temperature aw data from the peer-reviewed literature are mostly re-

stricted to solid-liquid equilibria (SLE). SLE measurements provide data on

the melting curves of ice and/or the organic component, i.e., for the specific

solution compositions referring to the solid-liquid phase boundary over a cer-

tain temperature range. To measure aw at low temperatures over a wide

composition and temperature range, we therefore combine different measure-

ment techniques including a dew point water activity meter for aw measure-

ments at temperatures higher than 273 K, differential scanning calorimetry

(DSC) to determine SLE, and hygroscopicity measurements of single levitated

particles in an electrodynamic balance (EDB). To complement these measure-

ments we developed a laboratory setup to measure total gas phase pressure

over solutions at low temperatures. A detailed description of the measure-

ment techniques is given in the next section. Measurements were done for

4.2. Measurement Techniques 139

binary aqueous organic mixtures covering functional groups and multifunc-

tional organic compounds which are abundantly available in the atmosphere.

The organic functional groups considered include hydroxyl (OH), carboxyl

(COOH), ketone (CHnCO), ester (CCOO), ether (CHnO) and aromatic car-

bon (ACHn, and aromatic carbon-alcohol) (“phenol group”, ACOH), where

“n” denotes the multiplicity of an atom in the compound. These new mea-

surements for atmospherically relevant functional groups provide useful data

for the improvement of thermodynamic models, and for ice nucleation studies.

4.2 Measurement Techniques

To perform measurements over a wide concentration and temperature range

for atmospherically relevant organic compounds we use different measurement

techniques, each of which covers a certain temperature range.

4.2.1 Differential Scanning Calorimetry (DSC)

Solid-liquid equilibria data were obtained by measuring the melting tempera-

tures (Tm) and homogeneous freezing temperatures (Thom) of aqueous organic

solutions for emulsified samples with a DSC instrument (Q10 from TA Instru-

ments) following the procedure described in Zobrist et al. (2008). Water-in-oil

emulsions with droplet diameters in the range of 0.5 µm to 5 µm were pre-

pared by adding four parts by volume of a 5 wt% lanolin/mineral oil solution

(Fluka/Aldrich) to one part by volume of an aqueous solution and stirring with

a rotor-stator homogenizer (Polytron PT 1300D with a PT-DA 1307/2EC dis-

persing aggregate) for 40 seconds at 7000 RPM. Samples (8 to 10 mg) were

pipetted into the DSC pans, which were immediately sealed to prevent any

evaporation. All aqueous solutions were made with distilled and deionized

water (resistivity >18.2 MΩ cm , total organic impurities < 5 ppb). Each

experiment comprised three subsequent cooling cycles starting from 293 K.

The first and the last cycle were run with a cooling rate of 10 K min−1 (used

as control for the emulsion stability) and the second cycle with a cooling rate


of 1 K min−1 (used for the melting and freezing point evaluation). Ice melting

temperatures were evaluated at the melting peak maxima of the heating cycle

run at a rate of 1 K min−1. A detailed calibration of the DSC resulted in a

maximum uncertainty for ice melting temperatures of ± 0.8 K (Zobrist et al.,

2008).

Aqueous solutions of the following organic compounds were investigated: 1,3-

propanediol (Aldrich, 98%), 1,5-pentanediol (Fluka, ≥ 97%), 1,2-hexanediol

(Aldrich, 97 %), glycolic acid (Aldrich, 99%), pyruvic acid (Aldrich, 99%),

methoxyacetic acid (Aldrich, 99%), 2-ethoxyethyl acetate (Aldrich, 99%), D-

sorbitol (Aldrich, ≥ 98%), sucrose (sigma, ≥99%), resorcinol (Aldrich, 99%),

2-(2-ethoxyethoxy)ethanol (Aldrich, ≥ 99%), and M5, a multicomponent di-

carboxylic acid mixture composed of DL-malic acid (Aldrich, 99%), maleic

acid (Aldrich, 99%), malonic acid (Aldrich, 99%), glutaric acid (Aldrich,

99%), and methylsuccinnic acid (Aldrich, 99%). Table 4.1 lists the com-

pounds used together with a selection of their physical properties. The ice

melting (Tm(xorg)) and homogeneous freezing (Thom(xorg)) temperatures ob-

tained by DSC measurements for the aqueous organic mixtures at different

liquid solution compositions given as mole fraction of the organic component,

xorg, are provided in Table 4.2. We use the parameterisation by Koop et al.

(2000) to calculate aw at the melting temperature of ice (Tm) for known so-

lution composition. The vapour pressures for solid phase (superscript S) of

pure hexagonal ice, p,Sice (= p,Sw ), and water in equilibrium with the liquid

(superscript L) solution, pLw on the melting curve (SLE) are the same:

pLw(T = Tm(xorg)) = p,Sice (T = Tm(xorg)), (4.1)

where vapour-liquid, vapour-solid, and solid-liquid equilibria (SLE) exist. At

SLE, the water activity of the aqueous organic solution, aSLEw (T, p), in equi-

librium with ice can therefore be expressed by:

aSLEw (T = Tm(xorg)) =

p,Sice (T = Tm)

p,Lw (T = Tm), (4.2)


where p,Lw (T = Tm) is the vapour pressure of pure liquid (supercooled) water

at Tm. The activity of water in a solution at thermodynamic equilibrium with

ice is related to chemical potentials via (Koop et al., 2000):

aSLEw (T, p) = exp

[µSw(T, p)− µ,L

w (T, p)]

RT

, (4.3)

where R is the ideal gas constant and p is the total mechanical pressure. The

µSw(T, p) and µ,L

w (T, p) are the pressure and temperature dependent chemi-

cal potentials of water in pure ice and pure liquid water. At the relatively

low atmospheric pressures, neglecting the pressure dependence of liquids and

solids is well justified. Koop et al. (2000) provide a temperature dependent

parameterisation for the difference in standard state chemical potentials of

pure water and ice (and hence water activity at SLE), valid at low (ambient)

pressure in the temperature range 150 K < T < 273 K:

µSw(T )− µ,L

w (T ) = 210368 + 131.438 T

−3.32373× 106T−1 − 41729.1 ln(T ). (4.4)

4.2.2 Water activity measurements

Water activity measurements were performed using an AquaLab water ac-

tivity meter (Model 3B, Decagon Devices, USA). The instrument employs

the chilled mirror method to determine the dew point temperature of the

gas phase in equilibrium with the sample. Infrared thermometry in addition

indicates the sample temperature. This instrument allows aw measurements

in the temperature range from 288 to 313 K for bulk samples. For most

measurements the volatile sample block available as an accessory with the

instrument was used since several of the organic compounds used have rather

low, but potentially significant vapour pressures at the probed temperatures.

Experimental errors for the volatile sample block are specified as ± 0.015 awby the manufacturer. The sample block was frequently calibrated and cor-

rected for drifts and offsets using saturated salt solutions and distilled water

samples covering the relevant aw range. For solutions of low-volatility organic


compounds and water, the standard sample block was used with a speci-

fied accuracy of ± 0.003 aw. Table 4.3 provides bulk solution aw data for

sorbitol, resorcinol, glycolic acid, pyruvic acid, and sucrose at 298.15 K. For

some selected systems additional bulk aw measurements at 279 K were carried

out by turning off the temperature control within the AquaLab instrument

and performing the measurements in a cold room at a constant temperature

of 279 K (with an uncertainty in room temperature, ± 0.5 K). Water ac-

tivity measurements were performed for aqueous solutions of 1,4-butanediol,

M5, methoxyacetic acid, and 2-(2-ethoxyethoxy) ethanol, for the temperature

range 279 K - 313 K.

4.2.3 Electrodynamic Balance (EDB) measurements

The basic experimental setup has been described previously (Krieger et al.,

2000; Zobrist et al., 2011). An electrically charged particle (typically 2-20 µm

in radius) is levitated in an electrodynamic balance. The balance is hosted

within a three wall glass chamber with a cooling agent flowing between the

inner walls and an insulation vacuum between the outer walls. A constant

flow of a N2/H2O mixture with a controlled H2O partial pressure is pumped

continuously through the chamber at a constant total pressure, adjustable

between 20 and 100 kPa. A charged, liquid particle is injected in the trap

using a single particle generator (Hewlett-Packard 51633A ink jet cartridge)

and is levitated in the balance, while keeping the temperature constant and

increasing or decreasing the relative humidity (RH) within the chamber con-

tinuously. This is achieved by changing the N2/H2O ratio in the gas phase,

using automatic mass flow controllers. The humidity sensor placed close to

the particle registers the RH with an accuracy of ±1.5 % RH (U.P.S.I. France,

Model G-TUS.13) between 10 and 90 % RH. The sensor was calibrated di-

rectly in the trap using the deliquescence relative humidity of different salts at

different temperatures in the range of interest. The concentration of the par-

ticle can be calculated from the DC voltage compensating the gravitational

force when the dry particle mass is known (measured at RH < 10 %). An

alternative, independent measure of concentration is based on Mie-resonance

spectroscopy. We use a ball lens type point source LED as a “white light”


source to focus the light on the levitated particle and a pierced mirror to

collect Mie resonance spectra in a backscattering geometry. Radius informa-

tion is retrieved from the Mie-resonance spectra as described by Zobrist et al.

(2011). To convert radius to mass and concentration, we assume ideal mixing

to calculate the density of aqueous solutions. For the M5 mixture we use

a molar volume of 86.62 cm3/mol for the M5 mixture and a molarity based

linear parametrisation to calculate the refractive index, nD, at 589 nm for

aqueous M5 solution (nD = 1.3334 + 0.01297× molarity). Since the optically

retrieved concentration data are less noisy and more stable with respect to

drifts over longer measurement periods, we use those for the water activity

versus concentration data presented in Table 4.9.

4.2.4 Total pressure measurements

We use total gas phase pressure measurements of binary aqueous organic so-

lutions to determine aw at low temperatures over a wide concentration range.

The organic components of the binary systems were selected such that their

vapour pressure contribution to the total pressure is irrelevant, i.e., substan-

tially lower than the vapour pressure of water in the considered temperature

range. Given this prerequisite, measured total pressures can be evaluated as

being the vapour pressures of water in vapour-liquid equilibrium with the bi-

nary solutions at measured temperatures and known compositions. Since we

attribute measured total pressures entirely to water vapour, we are restricted

to binary mixtures with low organic vapour pressures that lie within or below

the measurement uncertainty of the total pressure over the whole investigated

composition range. The experimental setup used for the total pressure mea-

surements is shown in Figure 4.1. The setup consists of a round bottom flask

(500 ml) in which an aqueous organic mixture of a particular composition is

filled. For the total pressure measurements, the flask is immersed in a ther-

mostated ethanol bath (initially set to 223 K) whose temperature is slowly

ramped up to 290 K. The flask can be evacuated to ∼ 10−6 Pa using a vac-

uum pump and cooled in liquid N2 for the purpose of degassing residual air

naturally present in the samples. Glass beads (∼ 50 g, ∼ 3 mm diameter)

are added to the flask to prevent undesirable foaming of the solution during


Glass beads

VacuumPump

Aqueous solution

ethanol bath

Figure 4.1: Setup for total gas phase pressure measurements of aqueous organic

solutions at room temperature and below.

degassing cycles, which may occur when a frozen solution thaws relatively

quickly at room temperature after cooling to liquid nitrogen temperature.

Foaming may often lead to splashing of solution droplets up to the neck of

the flask, where the droplets remain without contact to the rest of the sample.

If such droplets were present above the level of the ethanol bath into which

the flask is submerged to maintain a temperature-controlled environment (Ju-


labo, FP50 thermostat), they would be at a different temperature than the

bulk solution and present a cause of systematic errors. The pressure increase

during the temperature ramp is registered by two pressure heads operating in

the pressure ranges P1 (Pfeiffer Vacuum, CMR-262, range: 1 to 1× 104 Pa)

and P2 (Pfeiffer Vacuum, PKR-251, range: 1× 10−2 to 110 Pa).

A typical experiment involves the following operational procedure: A binary

aqueous solution mixture (volume of 3 to 5 ml) is added to the flask. To

remove the residual gases (e.g., N2, O2, Ar, and CO2) from the solution,

a thorough degassing procedure is carried out for which the flask is placed

in a liquid N2 bath (at T ≈ 77 K) and the valve to the vacuum pump is

opened to remove the gas phase above the solution until a low pressure of ∼10−6 Pa is reached. Thereafter, the valve is closed again and the solution is

slowly brought back to room temperature. These warming and cooling cycles

are carried out about 2 - 3 times so that all the residual gases are removed.

These degassing cycles are assumed to lead only to a very small loss of water

via the gas phase, so that the mixture composition remains practically unaf-

fected. Before the actual measurement starts, the sample flask is once more

cooled in liquid N2 and then transferred to the ethanol bath, which is held at

223 K, and further evacuated until a constant pressure is reached to allow the

removal of any remaining dissolved gases from the solution and ensuring that

the total pressure measured by the pressure sensors corresponds, within un-

certainty, to the one of water vapour alone. The valves to the pressure heads

are then opened and the total gas phase pressure measurement is carried out

in the temperature range from 228 to 290 K by increasing the temperature

of the ethanol bath at a constant rate of 10.6 K/h, over a time period of 350

minutes. The measured constant leak rate of the instrument, which we mea-

sured for the same time interval as the actual experiment, is subtracted from

the total pressure data and from the resulting values, water vapour pressure

data for the particular system compositions and temperatures are obtained.

For the investigated systems, aw values are derived by dividing the measured

water vapour pressure by the calculated liquid-state vapour pressure of pure

water (liquid-state saturation vapour pressure) at the prescribed temperature

of the ethanol bath using the parametrisation by Murphy and Koop (2006).


4.3 Results

The total pressure measurements were performed for four aqueous organic

mixtures namely for aqueous solutions of 1,4-butanediol, M5, 2-methoxyacetic

acid and 2-(2-ethoxyethoxy)ethanol. These measurements were comple-

mented by bulk solution aw measurements using the water activity meter

in the temperature range from 313 K to 279 K, and SLE ice melting and

freezing data measured with the DSC in the temperature range from 273 K

to ∼200 K, to obtain aw data coverage over a wide range of temperatures

and concentrations. In the case of aqueous M5 mixtures, additional aw mea-

surements to temperatures as low as 233 K were obtained from single-particle

measurements using the EDB.

4.3.1 1,4-Butanediol

Figure 4.2 shows aw measurements for aqueous 1,4-butanediol solutions ob-

tained from the different measurement techniques. The ice melting curve as

a function of aw (dashed blue curve) is calculated using the parametrisation

by Koop and Zobrist (2009). The dash-dotted lines represent the param-

eterisation by Zobrist et al. (2008) for the different compositions given in

terms of mass percent (wt%). Zobrist et al. (2008) used experimental ice

melting temperatures and bulk aw data and applied a composition and tem-

perature dependent parametrisation for water activities of specific mixtures

using 7 coefficients fitted to their data sets. The solid lines (both coloured

and grey) show the derived aw values from the total pressure measurements

for concentrations from 20 to 90 wt% over a temperature range from 240 to

290 K. The measured data is considered reliable within the uncertainty of

the method (± 0.03 of aw). The grey part of these lines do not reflect the

aw of the solutions; rather, they are substantially influenced by the presence

of solid phases and/or reflect undesired partial pressure contributions from

residual gases (N2, O2), which may be present to a small extent in the solu-

tions even after several cycles of degassing. Such artefacts caused by residual,

highly volatile (supercritical) gases become much more important toward low

4.3. Results 147

temperatures relative to the much lower partial pressure signal from water,

essentially enlarging the uncertainties of the derived water activities beyond

a practical limit for a meaningful data evaluation. The grey portions of the

curves merge within uncertainty on the SLE phase boundary of the aqueous

solution and ice for aw > 0.76 and analogously on the 1,4-butanediol SLE line

for aw < 0.70. Eutectic melting is observed at ∼ 243 to 245 K as a horizontal

peak to low aw because the sample temperature remains constant during the

melting process while the prescribed temperature in the surrounding ethanol

bath, which is used to derive the aw data shown in Figure 4.2, keeps increas-

ing. The area of the phase diagram below the melting curve of one of the

components, i.e., the green and blue shaded areas, is not accessible for awmeasurements by bulk techniques, because the presence of solid phases alters

the initial (known) composition of the remaining liquid solution. The dips

visible on the curves around T ≈ 275 K of the total pressure measurements

of the 20 wt% and 40 wt% mixtures, are artefacts, which are probably due to

the melting of tiny droplets that may have splashed to the neck of the flask

while degassing the solution prior to the actual measurements.

All measurements taken together provide a consistent picture of the temper-

ature dependence of aw of aqueous 1,4-butanediol, namely a more or less

constant decrease of aw with decreasing temperature, which is well described

by the parameterisation of Zobrist et al. (2008) (for bulk solutions at least

until the intersection with the melting curves). Table 4.4 provides aw data

obtained from the Aqualab water activity meter. Table 4.12 lists the aw data

derived from the total pressure measurements.

4.3.2 Methoxyacetic acid

Figure 4.3 shows aw measurements of aqueous methoxyacetic acid covering

the concentration range from 10 wt% to 90 wt% and temperatures from 240 to

298 K using different experimental techniques. Water activity of the aqueous

solution in equilibrium with ice, i.e., SLE for concentrations from 10 to 60

wt% covering a temperature range of 251 to 272 K are obtained using DSC

measurements (pentagons). The grey solid lines at lower concentrations rep-

resent the aw data derived from total pressure measurements which merge on


the ice melting curve (aw> 0.8), while the grey solid lines at higher concen-

trations (aw < 0.7) follow the melting curve of methoxyacetic acid. Eutectic

melting is observed at T ≈ 253 K. The colour shaded region below the

ice-melting and the methoxyacetic acid melting curves are not accessible for

total pressure measurements, because the presence of solid phases influences

the solution concentration (above the eutectic melting line), due to which the

measured aw will not correspond to the initial composition of the solution

being measured. The broad horizontal peaks to high aw on the 50, 60, and

70 wt% lines of the total pressure measurements indicate an increased total

pressure whose origin is unclear, but points to artefacts (perhaps due to the

melting of small solution droplets, splashed to the neck of the glass flask, that

are of lower organic concentrations).

The measured aw using the water activity meter and the aw data derived

from total pressure measurements indicate a small temperature dependence

and are consistent within 2 to 5 % at higher concentration wt% of methoxy-

acetic acid. A reason for this deviation might be that full equilibration of the

solution was not reached after the melting of solid methoxyacetic acid over

the timescale of the experiment. In the more dilute composition range with

respect to the organic component, deviations < 2 % aw are found, indicating

the principle validity of the total pressure data in the temperature range con-

sidered reliable. Measurements and the parametrisation for the temperature

dependence of water activity by (Zobrist et al., 2008) (dashed-dotted lines in

Figure 4.3) indicate a small aw decrease with the decrease in the temperature.

The bulk aw data from the water activity meter measurements is provided in

Table 4.5. The calculated aw data obtained from the total pressure experi-

ments for various solution compositions are given in Table 4.13.

4.3.3 2-(2-Ethoxyethoxy)ethanol

Water activity measurements for aqueous solutions of 2-(2-ethoxyethoxy)

ethanol (also known as carbitol) in the concentration range from 10 to 90

wt% of organic in the temperature range from 230 to 298 K are presented

in Fig 4.4. DSC measurements were performed for a concentration range

of 10 to 40 wt% covering the temperature range from 248 to 272 K. Bulk

4.3. Results 149

aw measurements using the water activity meter in the temperature range

from 279 to 298 K are listed in Table 4.6 and shown as coloured circles in

Fig 4.4. Eutectic melting was not observed since the melting point of pure

2-(2-ethoxyethoxy)ethanol (Tm = 197 K) is lower than the considered temper-

ature range. The aw data measured using the water activity meter and DSC

data are in good agreeement with the parametrised temperature dependence

by Zobrist et al. (2008), the latter is displayed by the coloured dash-dotted

lines. Both the aw measurements and the parametrisation indicate a decrease

in aw with decrease in temperature. The aw derived from the total pressure

measurements are in good agreement with the aw data obtained from the

water activity meter and the parametrisation for T > 265 K and for mass

fractions of the organic component, worg, of 60 wt% ≤ worg ≤ 90 wt%. At

lower temperatures, aw strongly increases with decreasing temperature for

worg ≥ 80%. At T = 225 K, all the solid lines converge. Such a temperature

dependence of water activity is expected for solutions that exhibit a phase

transition (as seen when the curves bend and follow the melting curves in the

previous systems, where two solid phases exist at lower T). Another possibility

for such a convergence would be that a phase transition in terms of a liquid-

liquid phase separation occurs at low temperatures. It should be noted that

the measurement artefacts would not be expected to lead to a convergence of

the curves onto the ice melting curve at a common temperature. Complemen-

tary measurements using the EDB could not be performed for this system due

to the comparably high vapour pressure of 2-(2-ethoxyethoxy)ethanol at room

temperature, which leads to fast evaporation during injection of the particle

into the EDB. At lower worg (10 to 50 wt%) the solid coloured lines show

good agreement with the parametrisation by Zobrist et al. (2008). The water

activity data derived from the total “equilibrium” pressure measurements for

various concentrations are provided in Table 4.14.

4.3.4 M5 (multicomponent dicarboxylic acid) mixture

Figure 4.5 shows aw measurements for aqueous solutions of M5 over a concen-

tration range from 10 to 84 wt% M5, i.e., up to the solution saturation limit

at 298 K, in the temperature range from 225 to 313 K. Bulk aw measurements


performed in the temperature range from 279 to 313 K are represented by the

coloured circles and are listed in Table 4.7 and Table 4.8. Humidity cycles

of single levitated particles in the EDB were performed in the temperature

range from 233 to 289 K and are represented by filled coloured diamonds.

M5 mixture compositions and aw data for the aqueous solutions are listed

in Table 4.9, Table 4.10, and Table 4.11. The total pressure measurements

are represented by solid and dashed lines (dashed for M5 mass fractions from

75 to 84 wt%) in Figure 4.5. For mass fractions wM5 > 50 wt% both EDB

measurements and total pressure measurements show an increase in aw with

decreasing temperature, with a stronger effect at higher concentrations. No

simple (pseudo-binary) eutectic melting was observed because in case of the

total pressure measurements individual components of the M5 mixture did

crystallise at different temperatures and mass fractions of M5. We assume

that the total pressure data for wM5 > 70 wt% (shown by dashed coloured

lines in Figure 4.5) are influenced by the crystallization of certain compo-

nents of the M5 mixture. These data are not considered to reflect the water

activity of the initial mixtures and are therefore not tabulated. The same

might be true for the bulk aw data points at 279 K and concentrations of 80

and 84 wt%, which might explain their high water activity. Table 4.15 pro-

vides the composition of the M5 + water mixtures used for the total pressure

measurements; corresponding water activities are listed in Table 4.16. The

parametrisation of Zobrist et al. (2008) is shown by the dash-dotted lines.

Thus it should be noted, both M5 and 2-(2-ethoxyethoxy)ethanol solutions

show a similar effect of increase in aw with decrease in temperature at higher

concentrations.

4.3. Results 151

0.4 0.5 0.6 0.7 0.8 0.9 1.0230

240

250

260

270

280

290

300

310

32010rwt,60rwt,

20rwt,30rwt,

40rwt,50rwt,62rwt,

65rwt,70rwt,75rwt,80rwt,85rwt,90rwt,

Te

mp

era

ture

rbK

d

waterractivity, aw

organicrbSdr+r solutionrbLd

icerbSdr+r1,4-butanediolrbSd

icerbSdr+rsolutionrbLd

Figure 4.2: Measured water activities of aqueous 1,4-butanediol solutions versus

temperature. The different colours indicate the solution compositions in wt% of

the organic component. The solid lines show data derived from the total pressure

measurements. The coloured portion of the solid lines represents the temperature

range for which the measurements are considered reliable within the uncertainty of

the method (±0.03 of aw). Water activities derived from DSC measurements on the

ice melting curve are represented by pentagons. Bulk aw measurements using the

water activity meter are represented by solid circles. The dash-dotted lines show the

composition and temperature dependent aw parametrisation by Zobrist et al. (2008).

The blue dashed line is the ice melting curve (Koop and Zobrist, 2009). In the colour

shaded regions one or both components are supersaturated with respect to the solid

phase and therefore, above the eutectic temperature (245 K), at equilibrium one solid

phase coexists with the remaining solution.


0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0230

240

250

260

270

280

290

300

310

32010+wtd

20+wtd30+wtd40+wtd

50+wtd60+wtd70+wtd80+wtd85+wtd90+wtd

Te

mp

era

ture

+sK

l

water+activity,+aw

organic+sSl+++ solution+sLl

ice+sSl+++2-methoxyacetic+acid+sSl

Figure 4.3: Measured water activities of aqueous 2-methoxyacetic acid solutions

versus temperature. The different colours indicate the solution compositions in wt%

of the organic component. The solid lines show data derived from the total pressure

measurements. The coloured portion of the solid lines represents the temperature

range for which the measurements are considered reliable within the uncertainty of

the method (±0.03 of aw). Water activities derived from DSC measurements on the

ice melting curve are represented by pentagons. Bulk aw measurements using the

water activity meter are represented by solid circles. The dash-dotted lines show the


The blue dashed line is the ice melting curve (Koop and Zobrist, 2009). In the colour

shaded regions one or both components are supersaturated with respect to the solid

phase and therefore, above the eutectic temperature ( 253 K), at equilibrium one

solid phase coexists with the remaining solution.

4.3. Results 153

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

230

240

250

260

270

280

290

300

310

320

75+wtL

10+wtL20+wtL

30+wtL40+wtL

50+wtL60+wtL

70+wtL80+wtL85+wtL90+wtL

Te

mp

era

ture

+(K

)

water+activity, aw

ice+(S)+++solution+(L)

Figure 4.4: Experimental water activities versus temperature for aqueous 2-(2-

ethoxyethoxy)ethanol solutions. The different colours indicate the solution composi-

tions in wt% of the organic component. The solid lines show data derived from the

total pressure measurements. The coloured portion of the solid lines represents the

temperature range for which the measurements are considered reliable. Water activ-

ity data obtained from DSC measurements on the water-ice melting curve Zobrist

et al. (2008) are represented by solid pentagons. The solid circles show the data from

bulk aw measurements with the water activity meter. The dash-dotted lines show the


The blue dashed line is the ice melting curve (Koop and Zobrist, 2009).


0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

230

240

250

260

270

280

290

300

310

32020rwt,

10rwt,

30rwt,40rwt,50rwt,60rwt,70rwt,75rwt,80rwt,84rwt,90rwt,

97rwt,

Te

mp

era

ture

r(K

)

waterractivity,raw

95rwt,

icer(S)r+rsolutionr(L)

Figure 4.5: Experimental water activities versus temperature for aqueous M5 so-

lutions. The solid and dotted lines are from the total pressure measurements. The

coloured dashed lines (75-84 wt%) represent measurements for which the solution

was not in a homogeneous liquid state but partly crystallized. The coloured portion

of the solid lines represents the temperature range for which the measurements are

considered reliable. Water activity data obtained from DSC measurements on the

water-ice melting curve from Zobrist et al. (2008) are shown by solid pentagons.

The solid circles show the data form bulk aw measurements with the water activity

meter. The dash-dotted lines show the composition and temperature dependent aw

parametrisation by Zobrist et al. (2008). The blue dashed line is the ice melting curve

(Koop and Zobrist, 2009). The aw data from EDB measurements are represented

by coloured diamonds.

4.4. Discussion 155

4.4 Discussion

4.4.1 Measurement techniques: scope and limitations

Water activity as a function of solute concentration can be measured either for

bulk solutions or by single particle measurement techniques. To measure aw of

bulk solutions, commercial dew point water activity meters are probably the

best choice because they have high accuracy and cover a wide concentration

range, thus, providing aw data from saturated to dilute solutions. However,

their accuracy decreases for water activities close to 1. Therefore they are

less suited for very dilute solutions. Moreover, the commercially available

water activity meters typically provide data for temperatures > 273 K and

experiments are time consuming when point-to-point measurements have to

be performed over a wide temperature range.

The DSC technique provides accurate measurements of solid-liquid equilib-

rium curves. DSC measurements are performed at fixed composition and can

provide Tm, Thom and glass transition temperatures (Tg) of aqueous solutions.

From the melting point depression of ice in aqueous solutions, accurate val-

ues of aw on the SLE phase boundary of the aqueous solution and ice can

be obtained, because the vapour pressures of ice and water at atmospheric

temperatures are well known (see Sect. 4.2.1). When oil-in-water emulsions

instead of bulk samples are measured, homogeneous freezing temperatures

Thom can also be obtained. This is especially useful because homogeneous ice

nucleation temperatures of aqueous solutions can be parametrised in terms

of water activity following Koop et al. (2000). The drawback of the DSC

technique is that only aw on the ice melting curve, thus, over a limited range

of compositison, are obtained.

The electrodynamic balance and optical tweezers in addition offer a possibil-

ity to measure the water activity via the mass or size change of hygroscopic,

micrometre-sized particles. In both techniques, the particles are levitated

without contact to any surface sites. Hence the supersatured concentration

range can be investigated when the particles are exposed to humidity cycles.

In the absence of liquid/gas phase diffusion limitations and given the rela-

tively large diameters of solution droplets (Kelvin effect can be ignored), RH


of the gas phase corresponds with aw of the condensed phase. Thus the water

content of the levitated particle in terms of a mass or molar fraction can be

calculated from the mass data when the dry particle mass is known. The

more accurate radius data obtained from the Mie scattering pattern can be

used to derive mass or mole fractions of solute but need refractive index data

and density data. The available temperature range depends on the cooling

and heating possibilities of the specific setup. Since the particles are airborne

without contact to any surface sites, aw of highly supersaturated solutions

can be obtained, covering thus a concentration range that is not accessible by

bulk methods.

Gas phase pressure measurements over aqueous solutions can provide water

activities when the measured total pressure can be totally ascribed to water

vapour. Since this technique needs bulk volumes, aw data can be obtained

from dilute upto saturated solutions. When the sample is kept in an ex-

ternally regulated thermostat, temperature profiles can be run that provide

water activity data of solutions at fixed concentration covering a wide tem-

perature range. Apart from deriving aw from the measured data, the total

pressure measurements can also provide information about eutectic melting

points and solid-liquid equilibria. However, accurate measurements of water

activities rely on efficient removal of the residual gases from the solution to

make sure that the measured total pressure corresponds to the water vapour

pressure over the solution. A lot of care needs to be taken during the re-

moval of residual gases since there is always the danger of overpumping which

may change the concentration of the solution (removal of significant amounts

of water vapour). The total pressure measurements are also prone to arte-

facts. The freezing/thawing process can lead to composition heterogeneity

in the sample. Some solutions show a strong foaming during melting lead-

ing to tiny droplets settling on the walls of the solution flask. These drops

can influence the gas phase pressure when they are at a higher temperature

than the bulk solution or when they are at a different composition due to

heterogeneity during freezing and thawing process. To cover a large concen-

tration and temperature range, substances with a low melting point and a

high water solubility are required. These conditions strongly reduce the num-

ber of compounds for which total pressure measurements may provide a large

4.4. Discussion 157

extension to composition/temperature ranges that are not covered by other

methods. These drawbacks together with the rather difficult handling of the

instrumental setup render total pressure measurements to be less attractive

than the alternative techniques such as DSC and EDB measurements. A

good temperature and concentration coverage can be achieved when aw bulk

measurements are combined with EDB experiments.

4.4.2 Hydrogen bonding in aqueous solutions

Hydrogen bonds are electrostatic dipole-dipole interactions that occur be-

tween covalently bound hydrogen atoms and the free electron pair of a highly

electronegative atom, such as nitrogen (N), oxygen (O) or fluorine (F). They

have some features of covalent bonding since they are directional and lead

to interatomic distances shorter than the sum of van der Waals radii of the

involved atoms. Hydrogen bonds have a strong influence on the activity of the

constituents in a solution. In aqueous solutions of alcohols and acids, solute-

solute, solute-water, and water-water hydrogen bonds can form. In general,

solute-water hydrogen bonds decrease aw while association of solute molecules

among each other leads to an increase in water activity. The strength and

average number of hydrogen bonds per molecule depends on concentration

and temperature. A strong increase of aw with decreasing temperature can

be rationalized in terms of association of solute molecules among each other.

Therefore, the analysis of the hydrogen bonds present in aqueous solutions

can help to understand the temperature dependence of water activity.

The carboxyl group of organic acids can build hydrogen bonds with water

or other dicarboxylic acids. The hydrogen atoms of the carboxyl group form

hydrogen bonds with the free electron pair of the oxygen atoms of a carboxyl

group of another acid molecule. Similarly, for dicarboxylic acids when the

hydrogen bonds connect to another dicarboxylic acid molecule this leads to

an association of dicarboxylic acids and formation of a dimer, which reduces

the effective number of dissolved species and leads to a relative increase in

water activity. Hydrogen bonds between water and dicarboxylic acids lead to

a decrease of water activtiy.

Aqueous solutions of poly(ethylene glycol) (PEG) have attracted much atten-


tion because of their extraordinary mixing behavior with water and their im-

portance in pharmaceutical and biomedical appliances Dormidontova (2002).

2-(2-ethoxyethoxy)ethanol shares with PEG the −CH2−CH2−O− repetition

unit as the main structural feature. Different from PEG with two termi-

nal hydroxyl groups, 2-(2-ethoxyethoxy)ethanol carries one terminal methyl

and one terminal hydroxyl group, which makes it less hydrophilic. The

−CH2−CH2−O− repetition unit lends 2-(2-ethoxyethoxy)ethanol and PEG a

higher solubility than typical for ethers. This behavior can be rationalized by

the increased structuring of water around PEG molecules. This structuring

is a consequence of hydrogen bonding and the ability of the PEG structure

to fill out the natural cavities in the hydrogen bonded network of water. The

increased structuring of the water is reflected in the large negative excess en-

tropy of the solution, while its large positive excess heat capacity is due to the

temperature sensitivity of the structure (Kjellander and Florin, 1981). This

interplay of entropic and enthalpy contributions to the Gibbs energy leads to a

closed loop miscibility gap at elevated temperature for aqueous PEG solutions

with PEG molecular weights of 2200 g mol−1 and higher (e.g.(Dormidontova,

2004; Kjellander and Florin, 1981; Zobrist et al., 2003)).

PEG with molecular weight below 2000 do not show liquid-liquid phase sep-

aration close to room temperature. However liquid-liquid equilibria are ob-

served for aqueous triethylene glycol solutions (Salabat, 2010) and smaller

poly(ethylene glycols) when a salt is added (e.g.(Marcolli and Krieger, 2006;

Ciobanu et al., 2009)). Liquid-liquid phase separation below room temper-

ature is not easily accessible because of the competition with crystallization

of either the PEG, the ice phase or both phases depending on solution com-

position and temperature. Ciobanu et al. (2009) have investigated in detail

liquid-liquid phase separation of aqueous PEG-400 solutions when ammonium

sulfate is added as a salting-out agent. This phase separation seems to persist

or even grow with decreasing temperature. At the onset of liquid-liquid phase

separation water activity lines of different concentration converge at a high awvalue. Therefore strong increase of aw with decreasing temperature in aque-

ous M5 and 2-(2-ethoxyethoxy) ethanol solutions can be rationalized in terms

of approaching a low temperature miscibility gap that is not experimentally

4.4. Discussion 159

accessible because it falls in the concentration/temperature range where ice

forms.

4.4.3 Atmospheric Implications

In the upper troposphere, one of several pathways for cirrus cloud formation is

by means of homogeneous ice nucleation on/in liquid aqueous aerosol particles,

which subsequently grow into supermicron-sized ice crystals by condensing

water vapour. Water-activity-based ice nucleation theory can be employed to

predict homogeneous ice nucleation temperatures and corresponding ice nucle-

ation rate coefficients for aqueous solution droplets without explicit knowledge

of the nature of the solute (Koop et al., 2000; Koop and Zobrist, 2009). While

for known mixture compositions the ice melting temperature and the corre-

sponding equilibrium water activity can be measured and/or parametrised

precisely (Koop et al., 2000; Koop, 2004; Knopf and Rigg, 2011), the same

level of information is not accessible experimentally in the case of supercooled

aqueous solution droplets, which exhibit homogeneous freezing of ice at a

temperature lower than the corresponding melting point, denoted as Thom.

As is well understood in the case of the ice melting process, with the melting

temperature being a function of aqueous solution composition (melting point

depression), similar behavior is observed in droplet freezing experiments for

the composition and temperature dependence of the homogeneous freezing

process. However, the water activity at the freezing temperature is typically

not accessible in experiments and/or subject to relatively large uncertainty.

Koop et al. (2000) suggest that aw at the homogeneous freezing tempera-

ture Thom(xorg) can be obtained from the corresponding aw determined at

the melting temperature Tm(xorg) of the solution at the same composition

with the assumption that aw does not change significantly over the tempera-

ture difference from Tm(xorg) to Thom(xorg). This approach has been tested

and shown to be a good approximation for a variety of inorganic solutions

(Koop et al., 2000; Koop, 2004), but may lead to significant errors in predic-

tions of the homogeneous freezing temperatures for aqueous organic solutions.

Figure 4.6 shows aw at melting and freezing conditions for aqueous organic

solutions investigated in this work. The Thom(xorg) and Tm(xorg) tempera-


tures for the investigated organic solutions are measured using the different

measurement techniques stated in this paper. The aw data for the melting

curve are calculated using the parametrisation by Koop et al. (2000). By

applying the assumption that solutions do not show significant change in awwith temperature from Tm(xorg) to Thom(xorg), i.e., neglecting the tempera-

ture dependence, the aw values evaluated this way show considerable scatter

around the ice-freezing curve in Fig 4.6 a. The homogeneous ice freezing

curve, shown as dashed black line in Figure 4.6, is calculated by shifting the

melting curve by ∆aw = 0.305 as suggested by Koop et al. (2000). The awdata derived with this assumption in most cases lie below the ice freezing

180

190

200

210

220

230

240

250

260

270

280

290

1.0 0.9 0.8 0.7 0.6 0.5 0.4

Water,activity,,aw

Te

mp

era

ture

,sK

b

a b

180

190

200

210

220

230

240

250

260

270

280

290

1.0 0.9 0.8 0.7 0.6 0.5 0.4

Water,activity,,aw

Te

mp

era

ture

,sK

b

1,3-propanediol1,5-propanediol1,2-hexanediolresorcinolsucroseglycolic,acidpyruvic,acidmethoxyacetic,acidsorbitol2-s2-ethoxyethoxyb,ethanol2-ethoxyethyl,acetateM51,4-butanediola

wsT

homsx

orgbb

awsT

msx

orgbb

awsT

homsx

orgbb ± 0.025

awsT

homsx

orgbb ± 0.05

Figure 4.6: Homogeneous freezing Thom(xorg) and melting points Tm(xorg) as a

function of aw for various aqueous organic solutions. The blue dashed line in panel

(a, b) is the homogeneous ice-melting curve (Tm) (Koop et al., 2000). The black

dashed line is the homogeneous ice-freezing curve (Thom) calculated by shifting the

melting curve by ∆aw = 0.305 (Koop and Zobrist, 2009; Koop et al., 2000). The

grey dotted line and grey dashed-dotted lines indicate a deviation of 2.5% and 5% in

aw, respectively. In panel a, aw at Thom(xorg) is taken to be equal as aw determined

at Tm(xorg) i.e., with the assumption that there is no significant change in aw with

temperature from Tm(xorg) to Thom(xorg). In panel b, aw at Tm(xorg) is determined

using Eq. 4.3 and the corresponding aw at the freezing temperature are estimated by

using Eq. 4.3 at Thom(xorg) with a constant ∆aw = 0.305 added to the aw value

determined on the melting curve.

4.4. Discussion 161

curve. This result could be interpreted as freezing occurring at lower tem-

peratures than predicted by the water-activity-based ice nucleation theory

for the known mixture compositions Fig 4.6 a. However, since the water-

activity-based freezing curve parametrisation is actually very successful in

describing the freezing behavior of many inorganic solutions, for which the

experimental or predicted temperature dependence in water activity is small,

it is more likely the case that assuming constant water activity from Tm(xorg)

to Thom(xorg) is the reason for the apparent discrepancy. Figure 4.6 b shows

the same experimental homogeneous freezing data, but in contrast to Fig 4.6a,

the corresponding water activities at the freezing temperature are estimated

by using Eq. 4.3 at Thom(xorg) with a constant ∆aw = 0.305 added to the

aw value determined on the melting curve for the same mixture. With this

method of aw data evaluation, the experimental data show much less scatter

and outline in good approximation a single “experimental” freezing curve.

This freezing curve described by the ensemble of aqueous organic solution

data is in relatively good agreement with the estimated homogeneous freezing

curve according to Koop et al. (2000). A slight, yet systematic deviation of

the experimental data towards higher freezing temperatures / lower water ac-

tivities is found, especially for the freezing temperatures below 220 K. These

deviations may result in significantly lower values of homogeneous nucleation

rate coefficients (Jhom) (Knopf and Rigg, 2011). A change of aw by 0.025 may

result in a change of Jhom by 6 orders of magnitude, which may significantly

affect predictions of the onset of ice crystal formation in cloud microphysical

models. A difference of 3 orders of magnitude in Jhom could delay or accel-

erate homogeneous ice nucleation by about an hour in a simulation shown by

Knopf and Rigg (2011).

The strong increase of aw with decreasing temperature of aqueous M5 and

2-(2-ethoxyethoxy)ethanol solutions at low temperatures and high solute con-

centrations has consequences for the hygroscopic growth of these systems at

low temperatures. First, the water uptake assuming thermodynamic equilib-

rium between the gas and the condensed phase will be much smaller. Second,

the low water content at these low temperatures will promote high viscosi-

ties or even glass formation leading in addition to kinetic limitations of water

uptake.


4.5 Conclusions

Water activity measurements for selected atmospherically relevant aqueous

organic systems were carried out using different experimental techniques with

the aim of covering a broad concentration and temperature range. Hygro-

scopicity measurements of single levitated aerosol particles with an electrody-

namic balance cover RH between 10 and 90 % RH and temperature from 200

to 300 K. DSC measurements provide the melting and freezing point data

at various solution concentrations and can be used to derive composition and

temperature dependent water activities. To complement these measurements,

total pressure measurements were performed for aqueous organic mixtures.

The measured aw data obtained from the different experiments are consistent

with each other and reveal that organic solutes exhibit differing water activ-

ity temperature dependence; both substantially increasing and decreasing awvalues with decreasing temperatures below 298 K are observed.

More accurate aw data at low temperatures are needed in the context of appli-

cations of homogeneous ice nucleation theory at upper tropospheric tempera-

tures. The experiments presented in this study provide new equilibrium data

sets useful for the development and improvement of thermodynamic activ-

ity coefficient models, such as UNIFAC (UNIquac Functional group Activity

Coefficients) and AIOMFAC (Aerosol Inorganic-Organic Mixtures Functional

groups Activity Coefficients). In turn, improved thermodynamic models can

be used for more accurate predictions of the temperature dependence of activ-

ity coefficients of water and other solution constituents, as well as equilibrium

compositions of multiphase systems for mixtures and environmental condi-

tions, for which experimental data is unavailable.

Acknowledgements

This work was supported by the Swiss National Foundation, project 200020-

125151 and by the CCES projects IMBALANCE and OPTIWARES funded

by the ETH Domain.


Table 4.1: Selected physical properties of organic components used for the experi-

ments: molar mass (M), melting point (Tm), boiling point (Tb) at standard pressure

(101.325 kPa), functional groups, and structure.

Organic compounds Chemical formula M (g mol-1) Tm( K) Tb(K) Functional groups Structure

1,3-propanediol C3H8O2 76.094 246.15 487.55 CHn,OH HO OH

1,4-butanediol C4H10O2 90.121 292.15 501.15 CHn,OH HO

OH

1,5-pentanediol C5H12O2 104.148 257.15 512.15 CHn,OH HO OH

1,2-hexanediol C6H14O2 118.174 318.15 496.65 CHn,OH OH

OH

sorbitol C6H14O2 182.172 372.15 568.15 CHn,OHOH

OH

OH

OH

OH

HO

resorcinol C6H6O2 110.111 383.15 549.2 CHn,OH

HO OH

sucrose C12H22O11 342.116 459.15 decomposes CHn,OH,CHnOHO

OH

OH

O

HO

O

OH

OH

HO

HO

O

glycolic acid C2H4O3 76.051 353.15 385 CHn,OH,COOH HO

O

OH

pyruvic acid C3H4O3 88.062 284.65 438.37 CHnCO,COOH

O

O

OH

2-methoxyacetic acid C3H6O3 90.077 281.15 477.5 CHn,CHnO,COOH

O

OH

O

2-ethoxyethyl acetate C6H12O3 132.158 212.15 428.71 CHn,CHnO,CCOO

O

O

O

2-(2-ethoxyethoxy)ethanol

(carbitol)C6H14O3 134.174 193.15 475.49 CHn,CHnO,OH

O

OHO

malic acid C4H6O5 134.087 405.15 509.13 CHn,OH,COOH

OH

O

OH

O

HO

malonic acid C3H4O4 104.061 409.15 339.99 CHn,COOH

O

OH

O

HO

maleic acid C4H4O4 116.011 403.15 256 CHn,COOHO

OH

OHO

glutaric acid C5H8O4 132.042 369.65 575.96 CHn,COOH

O

OH

O

HO

methylsuccinic acid C5H8O4 132.042 383− 388 decomposes CHn,COOH

O

OH

O

HO


Table 4.2: Homogeneous ice freezing Thom(xorg), and ice melting Tm(xorg) tem-

peratures of the investigated aqueous organic solutions. Solution compositions are

expressed in mole fraction x(organic) and temperature in Ka.

x(1,3-propanediol) Thom(xorg) Tm(xorg) x(1,5-pentanediol) Thom(xorg) Tm(xorg)

0.02637 231.23 270.47 0.01924 232.38 270.91

0.05656 223.55 266.78 0.04240 225.04 269.23

0.09225 212.74 262.08 0.06971 214.41 266.51

0.13630 200.20 255.69 0.10300 200.14 263.28

0.15035 259.59

x(glycolic acid) Thom(xorg) Tm(xorg) x(pyruvic acid) Thom(xorg) Tm(xorg)

0.02549 230.37 270.54 0.02223 231.72 276.76

0.05612 224.03 267.46 0.04879 223.68 267.06

0.09191 217.77 262.99 0.08090 210.84 261.65

0.13601 205.90 258.56 0.12026 209.45 253.58

x(1,2-hexanediol) Thom(xorg) Tm(xorg) x(sucrose) Thom(xorg) Tm(xorg)

0.01664 232.13 271.57 0.01040 232.00 272.00

0.03658 225.01 271.54 0.01624 230.68 271.34

0.06099 223.21 271.32 0.02303 227.51 270.35

0.09197 223.17 271.05 0.03056 225.33 269.08

0.13202 222.20 270.69 0.03937 221.09 267.75

0.18637 221.35 269.71 0.04870 217.10 265.75

0.25834 216.14 268.86 0.06064 263.15

0.07317 260.95

x(methoxyacetic acid) Thom(xorg) Tm(xorg) x(sorbitol) Thom(xorg) Tm(xorg)

0.02180 231.96 271.48 0.01103 233.35 272.19

0.04757 244.69 268.98 0.02422 230.00 270.63

0.07904 218.43 265.95 0.04077 225.40 268.68

0.11765 208.79 262.64 0.06174 218.67 265.94

0.16709 193.55 257.67 0.08948 208.42 261.84

0.22937 251.31 0.12867 255.75

x(resorcinol) Thom(xorg) Tm(xorg) x(2-ethoxyethyl acetate) Thom(xorg) Tm(xorg)

0.01818 231.55 271.98 0.01516 232.65 272.24

0.03956 228.20 270.67 0.03318 228.62 271.22

0.06567 226.60 268.84 0.05509 207.81 270.75

0.09848 223.28 267.15

x(2-(2-ethoxyethoxy)ethanol) Thom(xorg) Tm(xorg)

0.01463 232.52 272.03

0.03245 224.30 269.16

0.05412 210.51 265.18

0.08215 259.56

a The accuracies of the freezing and melting point measurements are ±0.5 K and ±0.4 K,

respectively.


Table 4.3: Bulk water activity (aw) measurements a at 298.15 K of different aqueous

organic solutions. Solution compositions are expressed in mole fraction x(organic).

x(sorbitol) aw x(resorcinol) aw x(glycolic acid) aw x(pyruvic acid) aw x(sucrose) aw

0.0110 0.981 0.01818 0.985 0.02564 0.962 0.02267 0.966 0.01040 0.992

0.0242 0.956 0.03956 0.950 0.05588 0.934 0.04868 0.925 0.01624 0.988

0.0408 0.934 0.06567 0.938 0.09209 0.887 0.08068 0.883 0.02303 0.977

0.0617 0.898 0.09848 0.893 0.13644 0.838 0.12020 0.836 0.03056 0.971

0.0895 0.834 0.13992 0.877 0.19154 0.767 0.16986 0.778 0.03937 0.955

0.1287 0.752 0.19312 0.843 0.25957 0.665 0.23467 0.704 0.04870 0.939

0.1872 0.621 0.27629 0.836 0.35447 0.570 0.32089 0.596 0.06064 0.914

0.48560 0.451 0.45005 0.434 0.07317 0.888

0.61773 0.193 0.16527 0.692



Table 4.4: Bulk water activity (aw) measurements a of water (1) + 1,4-butanediol

(2) solutions at three different temperatures. Solution compositions are given in mole

fraction (x2) of the organic component (2).

x2 aw(T = 289.15 K) aw(T = 298.15 K) aw(T = 313.15 K)

0.02192 0.978 0.985 0.982

0.04731 0.940 0.959 0.965

0.08020 0.918 0.935 0.958

0.11570 0.888 0.899 0.914

0.16604 0.844 0.860 0.871

0.23358 0.787 0.808 0.816

0.31787 0.717 0.733 0.745

0.44309 0.611 0.626 0.631

0.61890 0.424 0.445 0.450




Table 4.5: Bulk water activity (aw) measurements a of water (1) + 2-methoxyacetic

acid (2) solutions at three different temperatures. Solution compositions are given

in mole fraction (x2) of the organic component (2).

x2 aw(T = 279.15 K) aw(T = 289.15 K) aw(T = 298.15 K)

0.02185 0.953 0.961 0.969

0.04762 0.947 0.934 0.958

0.07433 0.922 0.920 0.939

0.11760 0.897 0.874 0.912

0.16659 0.866 0.844 0.880

0.22976 0.830 0.789 0.807

0.31637 0.702 0.720 0.715

0.44445 0.555 0.560 0.564

0.63961 0.346 0.346 0.354




Table 4.6: Bulk water activity (aw) measurements a of water (1) + 2-(2-

ethoxyethoxy)ethanol (2) solutions at three different temperatures. Solution com-

positions are given in mole fraction(x2) of the organic component (2).

x2 aw(T = 279.15 K) aw(T = 289.15 K) aw(T = 298.15 K)

0.01455 0.943 0.995 0.997

0.03275 0.935 0.986 0.982

0.05434 0.916 0.978 0.975

0.08182 0.900 0.929 0.933

0.11827 0.862 0.870 0.904

0.16747 0.822 0.839 0.866

0.23851 0.742 0.772 0.813

0.28670 0.703 0.738 0.780

0.34830 0.645 0.690 0.711

0.43139 0.582 0.613 0.641

0.54853 0.485 0.498 0.540

0.71022 0.328 0.333 0.367




Table 4.7: Bulk water activity (aw) measurements a of aqueous M5 solutions at

289.15, 298.15 and 313.15 K. The total organic fraction of the solution is given in

the first column in wt% and the composition in terms of individual components is

given in mole fractions of the dicarboxylic acids constituting the M5 mixture.

wt% x(DL-malic) x(malonic) x(maleic) x(glutaric) x(methylsuccinic) aw(289.15) aw(298.15) aw(313.15)

10.01 0.00247 0.00537 0.00193 0.00450 0.00200 0.984 0.991 0.989

20.01 0.00545 0.01184 0.00423 0.00991 0.00441 0.966 0.974 0.977

30.01 0.00908 0.01978 0.00711 0.01659 0.00735 0.945 0.951 0.960

39.68 0.01370 0.02852 0.01065 0.02492 0.01106 0.917 0.922 0.930

49.97 0.01962 0.04270 0.01528 0.03577 0.01587 0.873 0.877 0.880

54.99 0.02332 0.05074 0.01819 0.04251 0.01889 0.844 0.853 0.860

60.01 0.02766 0.06021 0.02157 0.05043 0.02241 0.804 0.807 0.807

69.98 0.03904 0.08503 0.03042 0.07120 0.03158 0.694 0.700 0.702

79.87 0.05631 0.12246 0.04385 0.10263 0.04556 0.542 0.543 0.543

84.41 0.06768 0.14725 0.05279 0.12339 0.05478 0.450 0.447 0.458



Table 4.8: Bulk water activity (aw) measurements a of aqueous M5 solutions at

279.15 K. The total organic fraction of the solution is given in the first column

in wt% and the composition in terms of individual components is given in mole

fractions of the dicarboxylic acids constituting the M5 mixture.

wt% x(DL-malic) x(malonic) x(maleic) x(glutaric) x(methylsuccinic) aw

10.01 0.00339 0.00602 0.00335 0.00259 0.00112 0.986

20.01 0.00768 0.00794 0.00921 0.00824 0.00248 0.968

30.04 0.01262 0.01436 0.01509 0.01368 0.00386 0.945

40.10 0.01910 0.03182 0.01385 0.02043 0.00556 0.907

50.07 0.02461 0.04022 0.02371 0.03281 0.00792 0.880

60.09 0.03001 0.04019 0.04088 0.05020 0.01840 0.804

70.21 0.04604 0.07285 0.06374 0.05439 0.02133 0.683

75.06 0.05403 0.08763 0.06039 0.06100 0.04357 0.625

80.07 0.06972 0.08695 0.08148 0.07131 0.05207 0.568




Table 4.9: EDB measurements: The total organic fraction of the solution is given

in the first column in wt% and the composition in terms of individual components

is given in mole fractions of the dicarboxylic acids constituting the M5 mixture.

wt% x(DL-malic) x(malonic) x(maleic) x(glutaric) x(methylsuccinic)

10.01 0.00247 0.00537 0.00193 0.00450 0.00200

20.01 0.00545 0.01184 0.00423 0.00991 0.00441

30.01 0.00908 0.01978 0.00711 0.01659 0.00735

39.68 0.01370 0.02852 0.01065 0.02492 0.01106

49.97 0.01962 0.04270 0.01528 0.03577 0.01587

54.99 0.02332 0.05074 0.01819 0.04251 0.01889

60.01 0.02766 0.06021 0.02157 0.05043 0.02241

69.98 0.03904 0.08503 0.03042 0.07120 0.03158

79.87 0.05631 0.12246 0.04385 0.10263 0.04556

84.41 0.06768 0.14725 0.05279 0.12339 0.05478


Table 4.10: Water activity (aw) measurements a of aqueous M5 solutions from

EDB hygroscopic growth curves evaluated at the indicated weight fractions Mention

that the corresponding M5 solution compositions in terms of organic components are

given in Table 4.9.

wt% aw(289 K) aw(273 K) aw(263 K) aw(244 K) aw(233 K)

10.01 0.987

20.01 0.969

30.01 0.947

39.68 0.915

49.97 0.874 0.867

54.99 0.839 0.839 0.841

60.01 0.805 0.802 0.805

69.98 0.694 0.699 0.714

79.87 0.540 0.562 0.572 0.648 0.667

84.41 0.448 0.470 0.490 0.569 0.626

90.00 0.318 0.329 0.362 0.429 0.544




Table 4.11: Water activity (aw) measurements a of aqueous M5 solutions from

EDB hygroscopic growth curves evaluated at the indicated weight fractions.

wt% aw(268 K) aw(253 K) aw(236 K)

60.00 0.791

65.00 0.767

70.00 0.725

73.00 0.700

75.00 0.676

80.00 0.602 0.611

85.00 0.504 0.507

90.00 0.364 0.385

95.60 0.172 0.216 0.347

97.20 0.264




Table 4.12: Measured water activities of water (1) + 1,4-butanediol (2) mixtures

derived from total pressure measurements, listed for a selection of temperatures in

the range 270 K < T < 291 K. Solution compositions are given in mole fractions

of the organic (x2).

x2 aw(270.15 K) x2 aw(273.15 K) x2 aw(275.15 K)

0.24441 0.762 0.24441 0.766 0.23200 0.812

0.26933 0.751 0.26933 0.754 0.24441 0.768

0.31705 0.688 0.31705 0.694 0.26933 0.755

0.37368 0.654 0.37368 0.658 0.31705 0.697

0.44332 0.604 0.44332 0.606 0.37368 0.660

0.52556 0.502 0.44332 0.607

0.52556 0.504

x2 aw(278.15 K) x2 aw(280.15 K) x2 aw(283.15 K)

0.24441 0.772 0.04797 0.971 0.04797 0.971

0.26933 0.757 0.11823 0.900 0.11823 0.901

0.31705 0.702 0.16701 0.868 0.16701 0.868

0.37368 0.663 0.23200 0.813 0.23200 0.813

0.44332 0.608 0.24441 0.775 0.24441 0.776

0.52556 0.507 0.26933 0.759 0.26933 0.761

0.64080 0.396 0.31705 0.706 0.31705 0.710

0.37368 0.665 0.37368 0.667

0.44332 0.608 0.44332 0.610

0.52556 0.508 0.52556 0.510

0.64080 0.399 0.64080 0.402

x2 aw(285.15 K) x2 aw(288.15 K) x2 aw(290.15 K)

0.04797 0.969 0.04797 0.967 0.04797 0.969

0.11823 0.901 0.11823 0.901 0.11823 0.906

0.16701 0.869 0.16701 0.868 0.16701 0.873

0.23200 0.814 0.23200 0.815 0.23200 0.818

0.24441 0.778 0.24441 0.780 0.24441 0.786

0.26933 0.763 0.26933 0.766 0.26933 0.770

0.31705 0.712 0.31705 0.716 0.31705 0.724

0.37368 0.667 0.37368 0.669 0.37368 0.673

0.44332 0.611 0.44332 0.611 0.44332 0.615

0.52556 0.512 0.52556 0.513 0.52556 0.517

0.64080 0.403 0.64080 0.404 0.64080 0.406


Table 4.13: Measured water activities of water (1) + 2-methoxyacetic acid (2)

mixtures derived from total pressure measurements, listed for a selection of temper-

atures in the range 268 K < T < 291 K. Solution compositions are given in mole

fractions of the organic (x2).

x2 aw (268.15 K) x2 aw(270.15 K) x2 aw(273.15 K) x2 aw(275.15 K)

0.43712 0.607 0.31301 0.750 0.31301 0.749 0.22937 0.835

0.53152 0.509 0.43712 0.608 0.43712 0.608 0.31301 0.749

0.63995 0.328 0.53152 0.510 0.53152 0.511 0.43712 0.608

0.63995 0.331 0.63995 0.336 0.53152 0.511

0.63995 0.337

x2 aw(278.15 K) x2 aw(280.15 K) x2 aw(283.15 K) x2 aw(285.15 K)

0.02180 0.983 0.02180 0.983 0.02180 0.983 0.02180 0.983

0.04757 0.974 0.04757 0.974 0.04757 0.975 0.04757 0.975

0.07904 0.960 0.07904 0.958 0.07904 0.954 0.07904 0.952

0.11765 0.924 0.11765 0.921 0.11765 0.918 0.11765 0.916

0.16696 0.893 0.16696 0.888 0.16696 0.885 0.16696 0.884

0.22937 0.832 0.22937 0.831 0.22937 0.830 0.22937 0.829

0.31301 0.748 0.31301 0.748 0.31301 0.747 0.31301 0.747

0.43712 0.607 0.43712 0.605 0.43712 0.604 0.43712 0.603

0.53152 0.511 0.53152 0.510 0.53152 0.509 0.53152 0.509

0.63995 0.340 0.63995 0.341 0.63995 0.343 0.63995 0.344

x2 aw(288.15 K) x2 aw(290.15 K)

0.02180 0.983 0.02180 0.987

0.04757 0.976 0.04757 0.980

0.07904 0.946 0.07904 0.949

0.11765 0.915 0.11765 0.918

0.16696 0.882 0.16696 0.884

0.22937 0.829 0.22937 0.830

0.31301 0.745 0.31301 0.747

0.43712 0.601 0.43712 0.602

0.53152 0.507 0.53152 0.508

0.63995 0.346 0.63995 0.348


Table 4.14: Measured water activities of water (1) + 2-(2-ethoxyethoxy)ethanol (2)

mixtures derived from total pressure measurements, listed for a selection of temper-

atures in the range 265 K < T < 291 K. Solution compositions are given in mole

fractions of the organic (x2).


0.54409 0.469 0.54409 0.477 0.54409 0.481 0.54409 0.488

0.42719 0.573 0.42719 0.583 0.42719 0.589 0.42719 0.598

0.34418 0.644 0.34418 0.654 0.34418 0.660 0.34418 0.669

0.23774 0.732 0.23774 0.742 0.23774 0.748 0.23774 0.756

0.16753 0.801 0.16753 0.811 0.16753 0.817 0.16753 0.824


0.54409 0.492 0.54409 0.497 0.54409 0.501 0.54409 0.506

0.42719 0.603 0.42719 0.610 0.42719 0.616 0.42719 0.622

0.34418 0.674 0.34418 0.681 0.34418 0.685 0.34418 0.692

0.23774 0.762 0.23774 0.769 0.23774 0.774 0.23774 0.782

0.16753 0.828 0.16753 0.835 0.16753 0.839 0.16753 0.845

0.11810 0.878 0.11810 0.883 0.11810 0.886 0.11810 0.891

0.08217 0.932 0.08217 0.932 0.08217 0.935

0.05412 0.961 0.05412 0.961 0.05412 0.961

0.03245 0.988 0.03245 0.989 0.03245 0.990

x2 aw(285.15 K) x2 aw(288.15 K) x2 aw(290.15 K)

0.54409 0.509 0.54409 0.514 0.54409 0.518

0.42719 0.627 0.42719 0.634 0.42719 0.643

0.34418 0.696 0.34418 0.704 0.34418 0.711

0.23774 0.787 0.23774 0.795 0.23774 0.815

0.16753 0.849 0.16753 0.855 0.16753 0.864

0.11810 0.895 0.11810 0.900 0.11810 0.907

0.08217 0.938 0.08217 0.941 0.08217 0.946

0.05412 0.962 0.05412 0.964 0.05412 0.967

0.03245 0.992 0.03245 0.994 0.03245 0.998


Table 4.15: Mixture compositions at different overall organic mass fractions (wt%)

of the aqueous M5 system used for total pressure measurements. Component mole

fractions (x) of the dicarboxylic acids constituting the M5 mixture are given, with

water accounting for the remaining fraction.

wt% x(DL-malic acid) x(malonic acid) x(maleic acid) x(glutaric acid) x(methylsuccinic acid)

20 0.00545 0.01183 0.00428 0.00992 0.00441

40 0.01353 0.02815 0.01064 0.02453 0.01085

50 0.01959 0.04236 0.01526 0.03581 0.01672

60 0.02746 0.06049 0.02135 0.04971 0.02241

70 0.03833 0.08327 0.02997 0.06984 0.03090

75 0.04547 0.09924 0.03549 0.08310 0.03700


Table 4.16: Measured water activities of water (1) + M5 (2) mixtures derived

from total pressure measurements, listed for a selection of temperatures in the range

265 K < T < 291 K. The total organic fraction of the solution is given in the first

column in (wt%).

wt% aw(265.15 K) aw(268.15 K) aw(270.15 K) aw(273.15 K)

20

40

50 0.872 0.881 0.886 0.890

60 0.822 0.823 0.820 0.819

70 0.724 0.719 0.719 0.709

wt% aw(275.15 K) aw(278.15 K) aw(280.15 K) aw(283.15 K)

20 0.982 0.982 0.981

40 0.943 0.939 0.934

50 0.892 0.890 0.888 0.887

60 0.817 0.817 0.817 0.815

70 0.706 0.701 0.699 0.696

wt% aw(285.15 K) aw(288.15 K) aw(290.15 K)

20 0.980 0.980 0.982

40 0.933 0.932 0.934

50 0.886 0.884 0.934

60 0.814 0.812 0.826

70 0.695 0.695 0.701

Chapter 5

Conclusions

In this thesis we investigated the temperature dependence of activity coeffi-

cients in aqueous organic and water-free organic mixtures at tropospheric low

temperatures. For this we use a thermodynamic model, AIOMFAC which is a

group contribution model developed by (Zuend et al., 2008, 2011) to compute

the activity coefficients of mixed organic, inorganic and organic-inorganic mix-

tures. AIOMFAC is based on the group contribution model LIFAC by Yan

et al. (1999) and includes the semi-empirical group contribution model UNI-

FAC (Fredenslund et al., 1975; Hansen et al., 1991) for the description of

organic mixtures and aqueous organic solutions. The UNIFAC within the

AIOMFAC uses a simple temperature dependence parameterisation around

∼ 275 to ∼ 400 K. To study the temperature dependence of activity coeffi-

cients at atmospheric low temperatures we develop a new improved temper-

ature dependence parametrization to include the organic functional groups

such as carboxyl, hydroxyl, ketone, aldehyde, ether, ester, alkyl, aromatic

carbon-alcohol, and aromatic hydrocarbon. For reliable estimation of group

interaction parameters, database covering organic functional groups covering

a wide composition and temperature range using different thermodynamic

data types such as vapour-liquid equilibria (VLE), liquid-liquid equilibria

(LLE), solid-liquid equilibria (SLE), and water activity (aw) measurements

was collected. Considering the apparent gaps in the database especially in

the low temperature range, for which literature data were missing or of in-

sufficient quality, we performed aw measurements for aqueous organic mix-

tures which included the selected monofunctional and multifunctional, this

177

178 Chapter 5. Conclusions

additional data is included in the AIOMFAC database. Measurement tech-

niques such dewpoint water activity meter covering the temperature range

from 289 K - 313 K, ice melting curves obtained from differential scanning

calorimetry (DSC) measurements yielding SLE data, and the hygroscopicity

measurements of single levitated aerosol particles with an electrodynamic bal-

ance covering dry conditions up to ice saturation from 200 K to 300 K were

used to obtain aw data at low temperatures. To complement these measure-

ment techniques we developed a setup to measure total gas phase pressure

at equilibrium over the solutions at low temperatures. The aw data obtained

from the different experiments are consistent with each other and reveal that

different organic solutes can lead to distinct aw temperature dependence; both

substantially increasing and decreasing aw values with decreasing temperature

are observed. In general, the new temperature dependence parameterisation

is in good agreement with most of the experimental datasets. The AIOM-

FAC model can be used for more accurate predictions of the temperature

dependence of activity coefficients of water and other solution constituents,

ice nucleation studies, phase transitions, and gas-particle partitioning. With

a combined approach of performing experimental and model predictions have

facilitated in better understanding the temperature dependence of organic

mixtures at low temperatures. The new parameterisation is implemented to

selected number of organic functional groups and can be further extended to

include new additional organic functional groups. The AIOMFAC model with

the new temperature dependence is parameterised for only aqueous/water-free

organic mixtures. For a wider atmospheric application of AIOMFAC model to

atmospheric conditions, implementing the temperature dependence parame-

terisation for aqueous/water-free inorganic and inorganic-organic mixtures is

essential. However, the database for low temperature measurements is in-

sufficient or of poor quality. A similar approach of combining experimental

studies with model calculations will be beneficial. The improved thermody-

namic models can be used for more accurate predictions of the temperature

dependence of activity coefficients of water and other solution constituents,

as well as equilibrium compositions of multiphase systems for mixtures and

environmental conditions, for which experimental data of insufficient quality.

List of Figures

1.1 Vertical temperature structure of the atmosphere extending

from the surface of the Earth to approximately 110-km alti-

tude as given in the U.S. Standard Atmosphere, 1976. Source:

Brasseur et al. (1999). . . . . . . . . . . . . . . . . . . . . . . . 7

1.2 Estimates (in Tg per year) for the year 2000 of (a) direct par-

ticle emissions into the atmosphere and (b) in-situ production.aSizes refer to diameters. [Adapted from Intergovernmental

Panel on Climate Change, 2001, Cambridge University Press,

pp.297 and 301, 2001.] Source: Wallace et al. (2006) . . . . . . 11

1.3 shows composites of MODIS/Terra, Aerosol optical Depth

(AOD) by the MODIS/Terra (550 nm) for the April 13 (top

row) and August 22 (bottom row), 2001. Red colour indicates

fine mode aerosols and green colour coarse mode aerosols. On

April 13, 2001, heavy dust and pollution is transported from

Asia to the Pacific and dust is transported from Africa to At-

lantic. On August 22 large smoke plumes from South America

and South Africa are evident. Adapted from Chin et al. (2007);

(original figure from Yoram Kaufman and Reto Stockli). . . . . 12

1.4 Schematic representation of distribution of particle surface

area of atmospheric aerosols. Principle modes, formation and

conversion processes, and removal mechanisms are indicated.

Source: Whitby and Cantrell (1976). . . . . . . . . . . . . . . . 14

179

180 List of Figures

1.5 Figure illustrates the direct and various indirect aerosol effects.

The aerosol particles are represented as small black dots; cloud

droplets are represented by the larger open circles. Straight

lines represent the incident and reflected solar radiation, and

wavy lines represent long wave radiation. The vertical grey

dashes represent rainfall, and LWC refers to the liquid water

content. [Source: IPCC AR4 Report Forster et al. (2007)] . . . 17

1.6 Summary of the principal components of the radiative forcing

of climate change. [Source: IPCC AR4 Report Forster et al.

(2007)] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.7 Hysteresis behavior of an aqueous ammonium sulphate particle

at ambient temperature. Open circles represents measurements

when RH is increasing, whereas the closed circles are points

with decreasing RH. Particle mass change is represented by

the ratio m/mo where m is the dry particle mass and mo is

the particle mass at particular RH. The deliquescence relative

humidity is about 80 %, the efforescence point around 37 % RH

in case of ammonium sulphate, but sometimes the efflorescence

may also occur at slightly higher or lower values (always below

DRH). Source: Tang and Munkelwitz (1994) . . . . . . . . . . . 21

2.1 Vapour pressure over a non-ideal liquid mixture of components

A and B at vapour-liquid equilibrium (VLE).(a) Positive devi-

ations from ideality, (b) negative deviations from ideality. pAand pB are vapour pressures and p

A and pB are the satura-

tion vapour pressures of the pure components A and B in gas

phase. Ptot represents the total pressure and is the sum of

partial pressure pA and pB . . . . . . . . . . . . . . . . . . . . . 36

2.2 Intramolecular and intermolecular forces in HCl molecules.

The intramolecular interactions within a HCl molecule is repre-

sented by a solid line while intermolecular interactions between

the two HCl molecules are represented by the dash/dotted line. 48

List of Figures 181

2.3 Ion-dipole interaction of Na+ and Cl− with water molecules.

δ+ and δ− are partial positive and negative charges created due

to asymmetrical distribution of electrons in chemical bonds. . . 49

2.4 Dipole-Dipole interactions. Solid red lines: strong interac-

tion forces between any two opposite charges, dashed red lines:

strong repulsive interaction forces between the like charges. . . 51

2.5 Dispersion forces. (a) Spherically symmetric charge distribu-

tion in He atom 1. (b) The uneven electron distribution pro-

duces a momentary dipoles and allows temporary electrostatic

attraction between atoms. . . . . . . . . . . . . . . . . . . . . . 52

2.6 Hydrogen bonding between H2O and NH3 molecules. . . . . . . 53

2.7 Hydrogen bonding between water molecules. The red dash-

lines are the hydrogen bonds between the water molecules. . . . 54

3.1 Database distribution for the water ↔ organic and organic ↔organic interaction parameters. The table lists the total num-

ber of datasets (set count) available for each main group inter-

action at temperatures substantially different from the chosen

reference temperature (T = 298.15 K). The total number of

datasets available for each main group interaction pair are visu-

alized by the green coloured bars. The percentile-wise colouring

is used to visualize the lowest temperature (Tlow, blue colour)

and the highest temperature (Thigh, red colour) (units of K) of

the data points available for each main group interaction pair. . 72

182 List of Figures

3.2 Measurements for 1,2-ethanediol + water solutions, corre-

sponding calculations of AIOMFAC-P1 in (panels a-c) and

AIOMFAC-P3 (panels d-f). The coloured curves in panels (c,

f) represents the temperature dependence of water activities

predicted for the range from 150 - 480 K. Panels (a, d): Low

temperature experimental SLE data (crosses) are compared

with the predictions for water activity at the same composi-

tions and temperatures (blue circles). Predictions of the corre-

sponding organic activities are shown as well (green triangles).

The dashed line represents the hypothetical water activity of

an ideal mixture. The error bars represent the model sensi-

tivity to a composition variation by xtol = 0.01. The middle

panels (b and e) show the model predictions of the activity co-

efficients compared to VLE data covering temperatures signifi-

cantly higher than room temperature. The temperature of the

individual data points are given in the boxes below the main

panels. Experimental data: Ott et al. (1972) and Gmehling

and Onken (2003a). . . . . . . . . . . . . . . . . . . . . . . . . 83

List of Figures 183

3.3 Measurements for acetic acid + water solutions, correspond-

ing calculations of AIOMFAC-P1 in and AIOMFAC-P3. The

coloured curves in panels (c, f) represents the temperature de-

pendence of water activities predicted for the range from 150

- 480 K. Panels (a, d): Low temperature experimental SLE

data (crosses) are compared with the predictions for water ac-

tivity at the same compositions and temperatures (blue circles).

Predictions of the corresponding organic activities are shown

as well (green triangles). The dashed line represents the hy-

pothetical water activity of an ideal mixture. The error bars

represent the model sensitivity to a composition variation by

xtol = 0.01. The middle panels (b and e) show the model

predictions of the activity coefficients compared to VLE data

covering temperatures significantly higher than room tempera-

ture. The temperature of the individual data points are given

in the boxes below the main panels. Experimental data: Fau-

con (1910) and Narayana et al. (1985). . . . . . . . . . . . . . . 84

3.4 Measurements for malonic acid + water solutions, corre-


AIOMFAC-P3 (panels d-f). Panels (c, f) show the temper-

ature dependence of water activities predicted for the range

from 150 - 480 K. Panels(a, d): Low temperature experimen-

tal SLE data (crosses) are compared with the predictions for

water activity at the same compositions and temperatures (blue

circles). Predictions of the corresponding organic activities are

shown as well (green triangles) while panels b and e show anal-

ogous data for the malonic acid melting curve. The error bars

represent the model sensitivity to a composition variation by

xtol =0.01. The dashed line represents the hypothetical water

activity of an ideal mixture. The temperature of the individ-

ual data points are given in the boxes below the main panels.

Experimental data: Braban et al. (2003) and Apelblat and

Manzurola (1987). . . . . . . . . . . . . . . . . . . . . . . . . . 89

184 List of Figures

3.5 Measurements for 2-butanone + water solutions, corresponding

calculations of AIOMFAC-P1 in (panels a-c) and AIOMFAC-

P3 (panels d-f). Panels (c, f) show the temperature depen-

dence of water activities predicted for the range from 150 -

480 K. Panels (a, d): Low temperature experimental SLE

data (crosses) are compared with the predictions for water ac-

tivity at the same compositions and temperatures (blue circles).

Predictions of the corresponding organic activities are shown as

well (green triangles). The error bars represent the model sensi-

tivity to a composition variation by xtol =0.01. The dashed line

represents the hypothetical water activity of an ideal mixture.

The middle panels (b and e) show the model predictions of the

activity coefficients compared to VLE data covering tempera-

tures significantly higher than room temperature. The temper-

ature of the individual data points are given in the boxes below

the main panels. Experimental data: Lohmann et al. (1997)

and Gmehling et al. (1981). . . . . . . . . . . . . . . . . . . . . 90

3.6 Measurements for 2-butoxyethanol + water solutions, corre-


AIOMFAC-P3 (panels d-f). Panels (c, f) show the temper-

ature dependence of water activities predicted for the range

from 150 - 480 K. Panels (a, d): Low temperature experimen-

tal SLE data (crosses) are compared with the predictions for

water activity at the same compositions and temperatures (blue

circles). Predictions of the corresponding organic activities are

shown as well (green triangles). The error bars represent the

model sensitivity to a composition variation by xtol =0.01. The

dashed line represents the hypothetical water activity of an

ideal mixture. The middle panels (b and e) show the model




in the boxes below the main panels. Experimental data: Koga

et al. (1994) and Schneider and Wilhelm (1959). . . . . . . . . 91

List of Figures 185

3.7 Measurements for cyclohexanol + adipic acid solutions, cor-

responding calculations of AIOMFAC-P1 in panels (a, b) and

AIOMFAC-P3 (c, d). Panels (b, d) represent the temperature

dependence predictions from AIOMFAC-P1 and AIOMFAC-P3

for temperature range of 150 - 480 K. Panel (a, c): SLE of

adipic acid shown vs. mole fraction of cyclohexanol (compo-

nent 1). The error bars represent the model sensitivity to a

composition variation by xtol = 0.01. The dashed line is the

ideal solution curve for component 1. The temperature of the


panels. Experimental data: Lihua et al. (2007). . . . . . . . . . 92

3.8 Measurements for ethanol + acetone solutions, corresponding

calculations of AIOMFAC-P1 in panels (a-c) and AIOMFAC-

P3 (d-e). Panels (c, f) show the temperature dependence as

predicted by AIOMFAC-P1 and AIOMFAC-P3 for the tem-

perature range of 150 - 480 K. Panels (a, d): Low tempera-

ture experimental SLE data (crosses), shown as mole fraction

of ethanol, x(1), versus activity (a(x)org2) of acetone. The error

bars represent the model sensitivity to a composition variation

by xtol = 0.01. The dashed line is the ideal solution curve for

component 1. The middle panels (b and e) show the model




in the boxes below the main panels. The temperature of the


panels. Experimental data: Sapgir (1929) and Amer et al. (1956). 93

186 List of Figures

3.9 Measurements for ethanol + 3-heptanone solutions, corre-

sponding calculations of AIOMFAC-P1 in panels (a, b) and

AIOMFAC-P3 (c, d). Panels (b, d) shows the temperature

dependence predictions from AIOMFAC-P1 and AIOMFAC-

P3 for temperature range of 150 - 480 K. The SLE data in

panel (a, c) show the composition (mole fraction of ethanol)

against activity of 3-heptanone. The error bars represent the

model sensitivity to a composition variation by xtol = 0.01.

The dashed line is the ideal solution curve for component 1.

Experimental data: Fiege et al. (1996). . . . . . . . . . . . . . . 94

3.10 Measurements for ethanol + diethyl ether solutions, corre-

sponding calculations of AIOMFAC-P1 and AIOMFAC-P3.

Panels (c, f) show the temperature dependence of the ethanol

activity, as predicted by AIOMFAC-P1 and AIOMFAC-P3 for

the temperature range 150 - 480 K. Panel (a,d): Experimental

SLE data (crosses) compared with model predictions (triangles)

for the activity of diethyl ether in the very low temperature

range 149 to 156 K. The dashed line is the ideal solution curve

for component 1. The middle panels (b and e) show the model



ture. The temperature of the individual data points are given in

the boxes below the main panels. Experimental data:Lalande

(1934) and Moeller et al. (1951). . . . . . . . . . . . . . . . . . 95

4.1 Setup for total gas phase pressure measurements of aqueous

organic solutions at room temperature and below. . . . . . . . 144

List of Figures 187

4.2 Measured water activities of aqueous 1,4-butanediol solutions

versus temperature. The different colours indicate the solu-

tion compositions in wt% of the organic component. The solid

lines show data derived from the total pressure measurements.

The coloured portion of the solid lines represents the tempera-

ture range for which the measurements are considered reliable

within the uncertainty of the method (±0.03 of aw). Water

activities derived from DSC measurements on the ice melting

curve are represented by pentagons. Bulk aw measurements

using the water activity meter are represented by solid circles.

The dash-dotted lines show the composition and temperature

dependent aw parametrisation by Zobrist et al. (2008). The

blue dashed line is the ice melting curve (Koop and Zobrist,

2009). In the colour shaded regions one or both components

are supersaturated with respect to the solid phase and there-

fore, above the eutectic temperature (245 K), at equilibrium

one solid phase coexists with the remaining solution. . . . . . . 151

4.3 Measured water activities of aqueous 2-methoxyacetic acid so-

lutions versus temperature. The different colours indicate the

solution compositions in wt% of the organic component. The

solid lines show data derived from the total pressure measure-

ments. The coloured portion of the solid lines represents the

temperature range for which the measurements are considered

reliable within the uncertainty of the method (±0.03 of aw).

Water activities derived from DSC measurements on the ice

melting curve are represented by pentagons. Bulk aw mea-

surements using the water activity meter are represented by

solid circles. The dash-dotted lines show the composition and

temperature dependent aw parametrisation by Zobrist et al.

(2008). The blue dashed line is the ice melting curve (Koop

and Zobrist, 2009). In the colour shaded regions one or both

components are supersaturated with respect to the solid phase

and therefore, above the eutectic temperature ( 253 K), at equi-

librium one solid phase coexists with the remaining solution. . 152

188 List of Figures

4.4 Experimental water activities versus temperature for aqueous

2-(2-ethoxyethoxy)ethanol solutions. The different colours in-

dicate the solution compositions in wt% of the organic compo-

nent. The solid lines show data derived from the total pressure

measurements. The coloured portion of the solid lines repre-

sents the temperature range for which the measurements are

considered reliable. Water activity data obtained from DSC

measurements on the water-ice melting curve Zobrist et al.

(2008) are represented by solid pentagons. The solid circles

show the data from bulk aw measurements with the water ac-

tivity meter. The dash-dotted lines show the composition and

temperature dependent aw parametrisation by Zobrist et al.

(2008). The blue dashed line is the ice melting curve (Koop

and Zobrist, 2009). . . . . . . . . . . . . . . . . . . . . . . . . . 153

4.5 Experimental water activities versus temperature for aqueous

M5 solutions. The solid and dotted lines are from the to-

tal pressure measurements. The coloured dashed lines (75-

84 wt%) represent measurements for which the solution was

not in a homogeneous liquid state but partly crystallized. The

coloured portion of the solid lines represents the temperature

range for which the measurements are considered reliable. Wa-

ter activity data obtained from DSC measurements on the

water-ice melting curve from Zobrist et al. (2008) are shown

by solid pentagons. The solid circles show the data form bulk

aw measurements with the water activity meter. The dash-

dotted lines show the composition and temperature dependent

aw parametrisation by Zobrist et al. (2008). The blue dashed

line is the ice melting curve (Koop and Zobrist, 2009). The

aw data from EDB measurements are represented by coloured

diamonds. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

List of Figures 189

4.6 Homogeneous freezing Thom(xorg) and melting points Tm(xorg)

as a function of aw for various aqueous organic solutions. The

blue dashed line in panel (a, b) is the homogeneous ice-melting

curve (Tm) (Koop et al., 2000). The black dashed line is the

homogeneous ice-freezing curve (Thom) calculated by shifting

the melting curve by ∆aw = 0.305 (Koop and Zobrist, 2009;

Koop et al., 2000). The grey dotted line and grey dashed-dotted

lines indicate a deviation of 2.5% and 5% in aw, respectively. In

panel a, aw at Thom(xorg) is taken to be equal as aw determined

at Tm(xorg) i.e., with the assumption that there is no significant

change in aw with temperature from Tm(xorg) to Thom(xorg).

In panel b, aw at Tm(xorg) is determined using Eq. 4.3 and the

corresponding aw at the freezing temperature are estimated

by using Eq. 4.3 at Thom(xorg) with a constant ∆aw = 0.305

added to the aw value determined on the melting curve. . . . . 160

List of Tables

3.1 Database used for the parameterisation of organic main group

↔ water and organic ↔ organic main group interactions of

AIOMFAC-P3. Listed are components, main groups, tempera-

ture range, number of data points (Nd), initial weighting (winitd )

and references of “water + organic ” and “organic + organic ”

datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

191

192 List of Tables

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

3.1 Continued. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

3.2 Matrix of AIOMFAC short-range group interaction parameters.

Parameter values for a(i, j) (units of K) are from the literaturea, b(i, j) (units of K), c(i, j) (dimensionless) are determined in

this study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

3.3 Bulk water activity (aw) measurementsa of water (1) + glycerol

(2) solutions at three different temperatures at atmospheric

pressure. Solution compositions are given in mole fraction (x2)

of the organic (component 2). . . . . . . . . . . . . . . . . . . 127

List of Tables 193

3.4 Bulk water activity (aw) measurements a of water (1) + 2,5-

hexanediol (2) solutions at three different temperatures at at-

mospheric pressure. Solution compositions are given in mole

fraction (x2) of the organic (component 2). . . . . . . . . . . . 128

3.5 Bulk water activity (aw) measurements a of water (1) + 1,2,6-

hexanetriol (2) solutions at three different temperatures at at-



3.6 Bulk water activity (aw) measurements a of water (1) + 1,2,7,8-

octantetrol (2) solutions at three different temperatures at at-



3.7 Bulk water activity (aw) measurements a of water (1) + 2,2,6,6-

tetrakis(hydroxymethyl)cyclohexanol (2) solutions at three dif-

ferent temperatures at atmospheric pressure. Solution compo-

sitions are given in mole fraction (x2) of the organic (component

2). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

3.8 Bulk water activity (aw) measurements a of water (1) + vanil-

lylmandelic acid (2) solutions at three different temperatures

at atmospheric pressure. Solution compositions are given in

mole fraction (x2) of the organic (component 2). . . . . . . . . 131

3.9 Bulk water activity (aw) measurements a of water (1) + raf-

finose (2) solutions at three different temperatures at atmo-

spheric pressure. Solution compositions are given in mole frac-

tion (x2) of the organic (component 2). . . . . . . . . . . . . . 132

3.10 Bulk water activity (aw) measurements a of water (1) + sucrose

(2) solutions at three different temperatures at atmospheric

pressure. Solution compositions are given in mole fraction (x2)

of the organic (component 2). . . . . . . . . . . . . . . . . . . 132

194 List of Tables

4.1 Selected physical properties of organic components used for

the experiments: molar mass (M), melting point (Tm), boil-

ing point (Tb) at standard pressure (101.325 kPa), functional

groups, and structure. . . . . . . . . . . . . . . . . . . . . . . . 163

4.2 Homogeneous ice freezing Thom(xorg), and ice melting Tm(xorg)

temperatures of the investigated aqueous organic solutions. So-

lution compositions are expressed in mole fraction x(organic)

and temperature in Ka. . . . . . . . . . . . . . . . . . . . . . . 164

4.3 Bulk water activity (aw) measurements a at 298.15 K of dif-

ferent aqueous organic solutions. Solution compositions are

expressed in mole fraction x(organic). . . . . . . . . . . . . . . 165

4.4 Bulk water activity (aw) measurements a of water (1) + 1,4-

butanediol (2) solutions at three different temperatures. Solu-

tion compositions are given in mole fraction (x2) of the organic

component (2). . . . . . . . . . . . . . . . . . . . . . . . . . . 165

4.5 Bulk water activity (aw) measurements a of water (1) + 2-

methoxyacetic acid (2) solutions at three different tempera-

tures. Solution compositions are given in mole fraction (x2) of

the organic component (2). . . . . . . . . . . . . . . . . . . . . 166

4.6 Bulk water activity (aw) measurements a of water (1) + 2-(2-

ethoxyethoxy)ethanol (2) solutions at three different tempera-

tures. Solution compositions are given in mole fraction(x2) of

the organic component (2). . . . . . . . . . . . . . . . . . . . . 167

4.7 Bulk water activity (aw) measurements a of aqueous M5 solu-

tions at 289.15, 298.15 and 313.15 K. The total organic fraction

of the solution is given in the first column in wt% and the com-

position in terms of individual components is given in mole

fractions of the dicarboxylic acids constituting the M5 mixture. 168

List of Tables 195

4.8 Bulk water activity (aw) measurements a of aqueous M5 solu-

tions at 279.15 K. The total organic fraction of the solution is

given in the first column in wt% and the composition in terms

of individual components is given in mole fractions of the di-

carboxylic acids constituting the M5 mixture. . . . . . . . . . . 168

4.9 EDB measurements: The total organic fraction of the solution

is given in the first column in wt% and the composition in

terms of individual components is given in mole fractions of

the dicarboxylic acids constituting the M5 mixture. . . . . . . . 169

4.10 Water activity (aw) measurements a of aqueous M5 solutions

from EDB hygroscopic growth curves evaluated at the indi-

cated weight fractions Mention that the corresponding M5 so-

lution compositions in terms of organic components are given

in Table 4.9. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

4.11 Water activity (aw) measurements a of aqueous M5 solutions

from EDB hygroscopic growth curves evaluated at the indicated

weight fractions. . . . . . . . . . . . . . . . . . . . . . . . . . . 171

4.12 Measured water activities of water (1) + 1,4-butanediol (2)

mixtures derived from total pressure measurements, listed for

a selection of temperatures in the range 270 K < T < 291 K.

Solution compositions are given in mole fractions of the organic

(x2). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172

4.13 Measured water activities of water (1) + 2-methoxyacetic

acid (2) mixtures derived from total pressure measurements,

listed for a selection of temperatures in the range 268 K < T

< 291 K. Solution compositions are given in mole fractions of

the organic (x2). . . . . . . . . . . . . . . . . . . . . . . . . . . 173

196 List of Tables

4.14 Measured water activities of water (1) + 2-(2-ethoxyethoxy)ethanol (2)

mixtures derived from total pressure measurements, listed for

a selection of temperatures in the range 265 K < T < 291 K.

Solution compositions are given in mole fractions of the organic

(x2). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

4.15 Mixture compositions at different overall organic mass fractions

(wt%) of the aqueous M5 system used for total pressure mea-

surements. Component mole fractions (x) of the dicarboxylic

acids constituting the M5 mixture are given, with water ac-

counting for the remaining fraction. . . . . . . . . . . . . . . . 175

4.16 Measured water activities of water (1) + M5 (2) mixtures de-

rived from total pressure measurements, listed for a selection

of temperatures in the range 265 K < T < 291 K. The total

organic fraction of the solution is given in the first column in

(wt%). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176

Bibliography

Abbas, R. and Gmehling, J.: Vapour–liquid equilibria, azeotropic data, excess

enthalpies, activity coefficients at infinite dilution and solid–liquid equilib-

ria for binary alcohol–ketone systems, Fluid Phase Equilib., 267, 119–126,

2008.

Ablett, S., Izzard, M., and Lillford, P.: Differential scanning calorimetric

study of frozen sucrose and glycerol solutions, J. Chem. Soc., Faraday

Trans., 88, 789–794, doi:10.1039/FT9928800789, 1992.

Abrams, D. S. and Prausnitz, J. M.: Statistical thermodynamics of liquid mix-

tures: a new expression for the excess Gibbs energy of partly or completely

miscible systems, AIChE Journal, 21, 116–128, 1975.

Ahlers, J.: Measurement, modeling and evaluation of solid-liquid phase equi-

libria, 1998.

Al-Muhtaseb, S. and Fahim, M.: Phase equilibria of the ternary system wa-

ter/acetic acid/2-pentanol, Fluid Phase Equilib., 123, 189–203, 1996.

Al-Rub, F., Abdel-Jabbar, N., Darwish, N., and Ghanem, H.: Vapor–liquid

equilibrium data for the 2-methoxy-2-methylpropane (MTBE)–ethanol,

MTBE–ethanol–calcium chloride, and MTBE–ethanol–copper chloride,

Sep. Sci. Technol., 37, 1911–1926, doi:10.1081/SS-120003051, 2002.

Albrecht, B.: Aerosols, cloud microphysics, and fractional cloudiness., Science

(New York, NY), 245, 1227, 1989.

Alexander, D.: The solubility of benzene in water, J. Phys. Chem, 63, 1021–

1022, doi:10.1021/j150576a608, 1959.

197

198 Bibliography

Alvarez, V., Mattedi, S., Iglesias, M., Gonzalez-Olmos, R., and Resa, J.:

Phase equilibria of binary mixtures containing methyl acetate, water,

methanol or ethanol at 101.3 kPa, Phys. Chem. Liq., 49, 52–71, 2011.

Alvarez Gonzalez, J., Macedo, E., Soares, M., and Medina, A.: Liquid-liquid

equilibria for ternary systems of water-phenol and solvents: data and rep-

resentation with models, Fluid phase equilib., 26, 289–302, 1986.

Amer, H., Paxton, R., and Winkle, M.: Methanol-Ethanol-Acetone, Ind. Eng.

Chem., 48, 142–146, doi:10.1021/ie50553a041, 1956.

Ansari, A. S. and Pandis, S.: Prediction of multicomponent inorganic atmo-

spheric aerosol behavior, Atmospheric Environment, 33, 745–757, 1999.

Anttila, T., Kiendler-Scharr, A., Tillmann, R., and Mentel, T.: On the reac-

tive uptake of gaseous compounds by organic-coated aqueous aerosols: The-

oretical analysis and application to the heterogeneous hydrolysis of N2O5,

J. Phys. Chem. A, 110, 10 435–10 443, doi:10.1021/jp062403c, 2006.

Anttila, T., Kiendler-Scharr, A., Mentel, T., and Tillmann, R.: Size depen-

dent partitioning of organic material: evidence for the formation of or-

ganic coatings on aqueous aerosols, J. Atmos. Chem., 57, 215–237, doi:

10.1007/s10874-007-9067-9, 2007.

Apelblat, A. and Manzurola, E.: Solubility of oxalic, malonic, succinic, adipic,

maleic, malic, citric, and tartaric acids in water from 278.15 to 338.15 K,

J. Chem. Thermodyn., 19, 317–320, 1987.

Apelblat, A. and Manzurola, E.: Solubility of ascorbic, 2-furancarboxylic,

glutaric, pimelic, salicylic, and o-phthalic acids in water from 279.15 to

342.15 K, and apparent molar volumes of ascorbic, glutaric, and pimelic

acids in water at 298.15 K, J. Chem. Thermodyn., 21, 1005–1008, 1989.

Arce, A., Blanco, A., Souza, P., and Vidal, I.: Liquid-liquid equilibria of the

ternary mixtures water+ propanoic acid+ methyl ethyl ketone and water+

propanoic acid+ methyl propyl ketone, J. Chem. Eng. Data, 40, 225–229,

doi:10.1021/je00017a047, 1995.

Bibliography 199

Arich, G. and Tagliavini, G.: Isotherme di equilibrio liquido-vapore nel sis-

tema acqua-acido acetico., La Ricerca scientifica, 28, 2493–2500, 1958.

Backes, H., Jing Jun, M., et al.: Interfacial tensions in binary and ternary

liquid-liquid systems, Chem. Eng. Sci., 45, 275–286, 1990.

Bailey, A., Harris, J., and Skau, E.: Solubilities of some normal saturated

and unsaturated long-chain fatty acid methyl esters in acetone, n-Hexane,

Toluene, and 1, 2-Dichloroethane, J. Chem. Eng. Data, 15, 583–585, doi:

10.1021/je60047a027, 1970.

Baker, M. and Peter, T.: Small-scale cloud processes and climate, Nature,

451, 299–300, doi:10.1038/nature06594, 2008.

Baustian, K., Wise, M., Jensen, E., Schill, G., Freedman, M., and Tolbert,

M.: State transformations and ice nucleation in glassy or (semi-) solid amor-

phous organic aerosol, Atmos. Chem. Phys. Discuss, 12, 27 333–27 366, doi:

10.5194/acpd-12-27333-2012, 2012.

Baynard, T., Garland, R., Ravishankara, A., Tolbert, M., and Lovejoy, E.:

Key factors influencing the relative humidity dependence of aerosol light

scattering, Geophys. Res. Lett., 33, L06 813, doi:10.1029/2005GL024898,

2006.

Bertram, A., Martin, S., Hanna, S., Smith, M., Bodsworth, A., Chen, Q.,

Kuwata, M., Liu, A., You, Y., and Zorn, S.: Predicting the relative hu-

midities of liquid-liquid phase separation, efflorescence, and deliquescence

of mixed particles of ammonium sulfate, organic material, and water using

the organic-to-sulfate mass ratio of the particle and the oxygen-to-carbon

elemental ratio of the organic component, Atmos. Chem. Phys, 11, 10 995–

11 006, doi:10.5194/acp-11-10995-2011, 2011.

Beyer, K., Friesen, K., Bothe, J., and Palet, B.: Phase Diagrams and Water

Activities of Aqueous Dicarboxylic Acid Systems of Atmospheric Impor-

tance, J. Phys. Chem. A, 112, 11 704–11 713, doi:10.1021/jp805985t, 2008.

Blando, J., Porcja, R., Li, T., Bowman, D., Lioy, P., and Turpin, B.: Sec-

ondary formation and the Smoky Mountain organic aerosol: An examina-

200 Bibliography

tion of aerosol polarity and functional group composition during SEAVS,

Environ. Sci. Technol, 32, 604–613, doi:10.1021/es970405s, 1998.

Blond, G., Simatos, D., Catte, M., Dussap, C., and Gros, J.: Modeling of

the water-sucrose state diagram below 0 C, Carbohydrate research, 298,

139–145, 1997.

Boese, A. B., Fink, C., Goodman, H., Hillenbrand, E., Holden, R., Jones, W.,

Livrngood, S., Rector, P.R.and Schultze, H., Smyth, H., Tamplin, W., and

Toussaint, W.: Glycols. Chapters 1 - 16, vol. 114, 1953.

Bomshtein, A.L.and Trofimov, A., Kudakina, E., and Serafimov, L.: Vapor-

liquid phase equilibrium in binary systems formed by water, carboxylic

acids C2 - C4 and esters, Deposited Doc. Oniitekhim, pp. 1–12, 1983.

Bones, D., Reid, J., Lienhard, D., and Krieger, U.: Comparing the mechanism

of water condensation and evaporation in glassy aerosol, Proc. Natl. Acad.

Sci. U.S.A., 109, 11 613–11 618, 2012.

Bonner, O. and Breazeale, W.: Osmotic and Activity Coefficients of Some

Nonelectrolytes., Journal of Chemical and Engineering Data, 10, 325–327,

doi:10.1021/je60027a007, 1965.

Borisova, I., Erlykina, M., Vatskova, V., Sokolov, N., and Mikhailov, V.:

Vapor-liquid equilibrium in the diethyl ether - water system at atmospheric

pressure, Deposited Doc. Oniitekhim, pp. 1–9, 1983.

Bower, V. and Robinson, R.: Isopiestic Vapor Pressure Measurements Of The

Ternary System: Sorbitol-Sodium Chloride-Water At 25 J. Phys. Chem, 67,

1540–1541, doi:10.1021/j100801a033, 1963.

Braban, C., Carroll, M., Sarah, A., and Abbatt, J.: Phase transitions of

malonic and oxalic acid aerosols, J. Phys. Chem. A, 107, 6594–6602, doi:

10.1021/jp034483f, 2003.

Brasseur, G., Orlando, J., and Tyndall, G.: Atmospheric chemistry and global

change, Oxford University Press New York, 1999.

Bibliography 201

Brooks, S., Wise, M., Cushing, M., and Tolbert, M.: Deliquescence behavior

of organic/ammonium sulfate aerosol, Geophys. Res. Lett., 29, 1917, doi:

10.1029/2002GL014733, 2002.

Brown, I. and Smith, F.: Liquid-Vapor equilibria VIII.The systems ace-

tone+benzene and Acetone+carbon tetrachloride at 45C, Aust. J. Chem.,

10, 423–428, 1957.

Brown, T., LeMay, H., Bursten, B., Murpy, C., and Woodward, P.: Chem-

istry: The central science, Pearson Education, 2009.

Cabezas, J., Arranz, J., and Berrueta, J.: Binary Vapor-liquid equilibrium of

benzene-alcohol systems., Rev.Roum.Chim, 30, 903–908, 1985.

Calvar, N., Dominguez, A., and Tojo, J.: Vapor–liquid equilibria for the

quaternary reactive system ethyl acetate+ ethanol+ water+ acetic acid and

some of the constituent binary systems at 101.3 kPa, Fluid phase equilib.,

235, 215–222, 2005.

Campbell, A., Kartzmark, E., and Gieskes, J.: Vapor-liquid equilibria, den-

sities, and refractivities in the system acetic acid-chloroform-water at 25,

Can. J. Chem., 41, 407–429, 1963.

Campbell, S., Wilsak, R., and Thodos, G.: Vapor-liquid equilibrium mea-

surements for the ethanol-acetone system at 372.7, 397.7, and 422.6 K, J.

Chem. Eng. Data, 32, 357–362, doi:10.1021/je00049a021, 1987.

Cappa, C. D. and Wilson, K. R.: Evolution of organic aerosol mass

spectra upon heating: implications for OA phase and partitioning

behavior, Atmospheric Chemistry and Physics, 11, 1895–1911, doi:

10.5194/acp-11-1895-2011, URL http://www.atmos-chem-phys.net/11/

1895/2011/, 2011.

Carr, A. and Kropholler, H.: Vapor Liquid Equilibria at Atmospheric Pres-

sure. Binary Systems of Ethyl Acetate-Benzene, Ethyl Acetate-Toluene,

and Ethyl Acetate-p-Xylene, J. Chem. Eng. Data, 7, 26–28, doi:10.1021/

je60012a007, 1962.

http://www.atmos-chem-phys.net/11/1895/2011/


202 Bibliography

Carrico, C., Kus, P., Rood, M., Quinn, P., and Bates, T.: Mixtures of pol-

lution, dust, sea salt, and volcanic aerosol during ACE-Asia: Radiative

properties as a function of relative humidity, J. Geophys. Res., 108, 8650,

doi:10.1029/2003JD003405, 2003.

Carta, R. and Dernini, S.: Solubility of solid acetic acid in liquid organic

solvents, J. Chem. Eng. Data, 28, 328–330, doi:10.1021/je00033a013, 1983.

Carta, R., Dernini, S., and De Santis, R.: Activity coefficients from solid-

liquid and vapor-liquid equilibriums of some associated solutions, J. Chem.

Eng. Data, 24, 100–103, doi:10.1021/je60081a025, 1979.

Carvoli, G. and Delogu, P.: Phase equilibria of the quaternary system acetic

acid-ethylene glycol monoethyl ether acetate-ethylene glycol monoethyl

ether-water, Fluid phase equilibria, 25, 91–105, 1986.

Chan, M., Choi, M., Ng, N., and Chan, C.: Hygroscopicity of water-soluble

organic compounds in atmospheric aerosols: Amino acids and biomass

burning derived organic species, Environ. Sci. Technol., 39, 1555–1562, doi:

10.1021/es049584l, 2005.

Chandak, B., Nageshwar, G., and Mene, P.: Excess enthalpy, volume, and

Gibbs free energy and viscosity of ethyl acetate-methyl cellosolve mixtures,

J. Chem. Eng. Data, 22, 137–141, doi:10.1021/je60073a022, 1977.

Chang, E. and Pankow, J.: Prediction of activity coefficients in liquid aerosol

particles containing organic compounds, dissolved inorganic salts, and water

– Part 2: Consideration of phase separation effects by an X-UNIFAC model,

Atmospheric Environment, 40, 6422–6436, 2006.

Chapoy, A., Anderson, R., Haghighi, H., Edwards, T., and Tohidi, B.: Can

n-Propanol Form Hydrate?, Ind.Eng.Chem.Res., 47, 1689–1694, 2008.

Chesnokov, V.: Uber die Stabilitat der Komplexe von Dimethylsulfoxyd

mit Essigsaure in Anwesenheit von Carbamid, Acetamid und Aceton,

Zh.Obshch.Khim., 39, 1437–1442, 1969.

Bibliography 203

Chiavone-Filho, O., Proust, P., and Rasmussen, P.: Vapor-liquid equilibria

for glycol ether+ water systems, J. Chem. Eng. Data, 38, 128–131, doi:

10.1021/je00009a031, 1993.

Chin, M., Diehl, T., Ginoux, P., and Malm, W.: Intercontinental transport

of pollution and dust aerosols: implications for regional air quality, Atmo-

spheric Chemistry and Physics, 7, 5501–5517, doi:10.5194/acp-7-5501-2007,

2007.

Cho, T., Ochi, K., and Kojima, K.: Measurement of vapor-liquid equilibrium

for systems with limited miscibility, Fluid Phase Equilib., 11, 137–152, 1983.

Choi, J., Park, D., and Rhim, J.: Prediction of ternary Liquid-liquid equilibria

using the NRTL and the UNIQUAC Models, Korean J. Chem. Eng., 3, 141–

151, 1986.

Choi, M. and Chan, C.: The effects of organic species on the hygroscopic

behaviors of inorganic aerosols, Environ. Sci. Technol., 36, 2422–2428, doi:

10.1021/es0113293, 2002.

Chylinski, K., Fras, Z., and Malanowski, S.: Vapor-Liquid Equilibrium in

Phenol+ 2-Ethoxyethanol at 363.15 to 383.15 K, J. Chem. Eng. Data, 46,

29–33, doi:10.1021/je0001072, 2001.

Ciobanu, V., Marcolli, C., Krieger, U., Weers, U., and Peter, T.: Liquid-

Liquid Phase Separation in Mixed Organic/Inorganic Aerosol Particles, J.

Phys. Chem. A., 113, 10 966–10 978, doi:10.1021/jp905054d, 2009.

Clarke, E. C. W. and Glew, D. N.: Evaluation of thermodynamic functions

from equilibrium constants, Trans. Faraday Soc., 62, 539–547, 1966.

Clegg, S. L., Brimblecombe, P., and Wexler, A. S.: Thermodynamic model

of the system H+–NH+4 –SO2−

4 –NO−3 –H2O at tropospheric temperatures, J.

Phys. Chem. A, 102, 2137–2154, 1998.

Clendenning, K.: Production and properties of 2,3-butanediol. XI. Evaluation

of levo-2,3-butanediol as a non-volatile antifreeze compound, Can. J. Res.

Sect., 24, 249–271, 1946.

204 Bibliography

Colberg, C., Luo, B., Wernli, H., Koop, T., and Peter, T.: A novel model to

predict the physical state of atmospheric H2SO4/NH3/H2O aerosol parti-

cles, Atmos.Chem.Phys., 3, 909–924, 2003.

Coles, K. and Popper, F.: Vapor-Liquid Equilibria. Ethylene Oxide-

Acetaldehyde and Ethylene Oxide-Water Systems, Ind. Eng. Chem., 42,

1434–1438, doi:10.1021/ie50487a046, 1950.

Colombo, A., Battilana, P., Ragaini, V., Bianchi, C., and Carvoli, G.: Liquid-

liquid equilibria of the ternary systems water+ acetic acid+ ethyl acetate

and water+ acetic acid+ isophorone (3, 5, 5-trimethyl-2-cyclohexen-1-one),

J. Chem. Eng. Data, 44, 35–39, doi:10.1021/je9702910, 1999.

Correa, J. M., Arce, A., Blanco, A., and Correa, A.: Liquid-liquid equilib-

ria of the system water + acetic acid + methyl ethyl ketone at several

temperatures, Fluid Phase Equilib., 32, 151–162, 1987.

Cziczo, D. J., DeMott, P. J., Brooks, S. D., Prenni, A. J., Thomson, D. S.,

Baumgardner, D., Wilson, J. C., and Kreidenweis, S. M.and Murphy, D. M.:

Observations of organic species and atmospheric ice formation, Geophys.

Res. Lett., 31, 2004.

Dakshinamurty, P., Rao, G. J., and Rao, C. V.: Vapour-liquid equilibria in

the system water-propionic acid, J. Appl. Chem., 11, 226–228, 1961.

Dallos, A., Laszlo-Parragi, M., Ratkovics, F., Hahn, G., Kalali, H., and

Kohler, F.: The thermodynamics of the system acetic acid - 2-butanone, Z.

Chem., 26, 35–36, 1986.

Danciu, E.: Echilibrul Lichid-Vapori. IV. Binarele Sistemului Ternar:

Propenoxid - Aldehida Propionica - Acetona in Condetii Izobare, Rev.

Chim. Bucharest., 21, 149–157, 1970.

dAvila, S. and Silva, R.: Isothermal vapor-liquid equilibrium data by total

pressure method. Systems acetaldehyde-ethanol, acetaldehyde-water, and

ethanol-water, J. Chem. Eng. Data, 15, 421–424, doi:10.1021/je60046a010,

1970.

Bibliography 205

de Leeuw, H.: About the System Acetaldehyde - Ethanol, Z. Phys. Chem.

Stoechiom. Verwandtschaftsl, 77, 284–314, 1911.

De Oliveira, L. and Aznar, M.: (Liquid+ liquid) equilibrium of water+ phe-

nol+ (1-butanol, or 2-butanol, or tert-butanol) systems, J. Chem. Ther-

modyn., 42, 1379–1385, 2010.

Dean, J. A. e. a.: Langes Handbook of Chemistry, McGraw-Hill, 15 edn.,

1999.

Debenedetti, P. and Stillinger, F.: Supercooled liquids and the glass transi-

tion, Nature, 410, 259–267, 2001.

DeMott, P. J., Cziczo, D. J., Prenni, A. J., Murphy, D. M., Kreidenweis,

S. M., Thomson, D. S., Borys, R., and Rogers, D. C.: Measurements of the

concentration and composition of nuclei for cirrus formation, PNAS, 100,

14 655–14 660, 2003.

Diedrichs, A. and Gmehling, J.: Solubility calculation of active pharmaceu-

tical ingredients in alkanes, alcohols, water and their mixtures using vari-

ous activity coefficient models, Ind. Eng. Chem. Res, 50, 1757–1769, doi:

10.1021/ie101373k, 2010.

Domalski, E. S. and Hearing, E. D.: Heat capacities and entropies of organic

compounds in the condensed phase. Volume III, Journal of Physical and

Chemical Reference Data, 25, 1, 1996.

Domanska, U., Morawski, P., and Piekarska, M.: Solid-Liquid Phase Equilib-

ria of 1-Decanol and 1-Dodecanol with Fragrance Raw Materials Based on

Cyclohexane, J. Chem. Eng. Data, 54, 1271–1276, 2009.

Donahue, N., Robinson, A., Stanier, C., and Pandis, S.: Coupled partitioning,

dilution, and chemical aging of semivolatile organics, Environ. Sci. Technol.,

40, 2635–2643, doi:10.1021/es052297c, 2006.

Dormidontova, E. E.: Role of competitive PEO-water and water-water hy-

drogen bonding in aqueous solution PEO behavior, Macromolecules, 35,

987–1001, 2002.

206 Bibliography

Dormidontova, E. E.: Influence of end groups on phase behavior and proper-

ties of PEO in aqueous solutions, Macromolecules, 37, 7747–7761, 2004.

Dykyj, J., V., K., and Seprakova, M.: Physical Properties of Ethylene Glycol

and its Derivatives. I. Solidification Points of Solutions of Ethylene Glycols,

Chem.Zvesti, 10, 193–203, 1956.

Dykyj, J., Svoboda, J., Wilhoit, R. C., Frenkel, M., and Hall, K. R.: Or-

ganic Compounds, C1 to C57. Part 1., in: Landolt-Bornstein - Group

IV Physical Chemistry Numerical Data and Functional Relationships in

Science and Technology, edited by Hall, K. R., vol. 20B: Vapor Pres-

sure and Antoine Constants for Oxygen Containing Organic Compounds,

pp. 14–110, SpringerMaterials - The Landolt-Bornstein Database, doi:

10.1007/10688583 3, URL http://www.springermaterials.com, 2000.

Erdakos, G. and Pankow, J.: Gas/particle partitioning of neutral and ionizing

compounds to single-and multi-phase aerosol particles. 2. Phase separation

in liquid particulate matter containing both polar and low-polarity organic

compounds, Atmospheric Environment, 38, 1005–1013, 2004.

Escobedo-Alvarado, G. and Sandler, S.: Vapor-liquid equilibrium of two aque-

ous systems that exhibit liquid-liquid phase separation, J. Chem. Eng. Data,

44, 319–322, doi:10.1021/je980228q, 1999.

Esquıvel, M. and Bernardo-Gil, M.: Liquid-liquid equilibria for the systems

water-alcohols-acetic acid, Fluid Phase Equilib., 57, 307–316, 1990.

Fandary, M., Aljimaz, A., Al-Kandary, J., and Fahim, M.: Liquid-liquid equi-

libria for the system water+ ethanol+ ethyl tert-butyl ether, J. Chem. Eng.

Data, 44, 1129–1131, doi:10.1021/je980253w, 1999.

Fang, W., Liu, G., Wang, L., Zhang, X., Mi, Z., Zhang, S., and Yin, Y.:

Liquid–Liquid Equilibria for the Ternary System Water+ Methyl Isobutyl

Ketone+ tert-Butyl Alcohol at Several Temperatures, J. Chem. Eng. Data,

53, 466–470, doi:10.1021/je700554u, 2008.

Farina, S., Adams, P., and Pandis, S.: Modeling global secondary organic

aerosol formation and processing with the volatility basis set: Implica-

http://www.springermaterials.com

Bibliography 207

tions for anthropogenic secondary organic aerosol, J. Geophys. Res., 115,

D09 202, doi:10.1029/2009JD013046, 2010.

Faucon, M.: Recherches sur les Melanges d’Eau et d’Acides Gras, Ann. Chim.

Phys., 19, 70–152, 1910.

Ferino, I., Marongiu, B., Monaci, R., Solinas, V., and Torrazza, S.: Thermo-

dynamic properties of aqueous non-electrolyte mixtures. Enthalpy of mixing

and liquid-liquid equilibrium of water+ aliphatic aldehyde mixtures, Ther-

mochimica Acta, 65, 157–168, 1983.

Fernandez-Torres, M., Gomis-Yagues, V., Ramos-Nofuentes, M., and Ruız-

Bevia, F.: The influence of the temperature on the liquid–liquid equilibrium

of the ternary system 1-pentanol–ethanol–water, Fluid phase equilib., 164,

267–273, 1999.

Fiege, C., Joh, R., Petri, M., and Gmehling, J.: Solid-liquid equilibria for

different heptanones with benzene, cyclohexane, and ethanol, J. Chem. Eng.

Data, 41, 1431–1433, doi:10.1021/je960140h, 1996.

Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fa-

hey, D. W., Haywood, J., Lean, J., Lowe, D. C., Myhre, G., Nganga,

J.and Prinn, R., Raga, G., Schulz, M., and Van Dorland, R.: Climate

Change 2007: The Physical Science Basis. Contribution of Working Group

I to the Fourth Assessment Report of the Intergovernmental Panel on Cli-

mate Change, chap. Changes in Atmospheric Constituents and in Radiative

Forcing., Cambridge University Press, Cambridge, UK and New York, USA,

2007.

Fredenslund, A., Jones, R. L., and Prausnitz, J. M.: Group-Contribution

Estimation of Activity Coefficients in Nonideal Liquid Mixtures, AIChE J.,

21, 1086–1099, 1975.

Fu, H., Cheng, G., and Han, S.: Vapor-Liquid Equilibrium for the Systems

Containing Association Component. II. Acetic Acid and Water; Acetic Acid

and 2-Butanone; Acetic Acid and 2-Pentanone; Acetic Acid and Acetic Acid

Propyl Ester, Huaxue Gongcheng, 6, 56–61, 1986.

208 Bibliography

Fu, H., Mo, X., Han, S., and Li, H.: Study on Isothermal Vapor-Liquid equi-

librium for ethanol-Benzene,chloroform-Benzene and ethanol-Chhloroform

Binary Systems, Shiyou Xuebao Shiyou Jiagong, 11, 87–92, 1995.

Fuzzi, S., Decesari, S., Facchini, M., Matta, E., Mircea, M., and Tagliavini, E.:

A simplified model of the water soluble organic component of atmospheric

aerosols, Geophys. Res. Lett, 28, 4079–4082, doi:10.1029/2001GL013418,

2001.

Fuzzi, S., Andreae, M., Huebert, B., Kulmala, M., Bond, T., Boy, M., Doherty,

S., Guenther, A., Kanakidou, M., Kawamura, K., Kerminen, V., Lohmann,

U., Russell, L., and Poschl, U.: Critical assessment of the current state of

scientific knowledge, terminology, and research needs concerning the role

of organic aerosols in the atmosphere, climate, and global change, Atmos.

Chem. Phys., 6, 2017–2038, 2006.

Gaube, J., Hammer, S., and Pfennig, A.: A new equilibrium cell with variable

cell volume for static vapour-pressure measurements, Fluid phase equilib.,

123, 245–257, 1996.

Gilburd, M., Yurkevich, B., and Polytanskaya, T.: Liquid-Vapor Phase equi-

librium in an acetaldehyde-Propylene oxide system, J.Appl.Chem.USSR,

52, 2247–2249, 1979.

Gilburd, M., Politanskaya, T., and Yurkevich, B.: Liquid-Vapor Phase equi-

librium in the systems ethyl acetate-acetone-allyl alcohol,and ethyl acetate-

acetic acid under reduced pressure, J.Appl.Chem.USSR, 54, 938–940, 1981.

Gmehling, J.: From UNIFAC to modified UNIFAC to PSRK with the help of

DDB, Fluid phase equilibria, 107, 1–29, 1995.

Gmehling, J.: Potential of thermodynamic tools (group contribution methods,

factual data banks) for the development of chemical processes, Fluid phase

equilibria, 210, 161–173, 2003.

Gmehling, J.: Present status and potential of group contribution methods

for process development, The Journal of Chemical Thermodynamics, 41,

731–747, 2009.

Bibliography 209

Gmehling, J. and Onken, U.: Vapor-Liquid Equilibrium Data Collec-

tion: Volume I, Part 1-Aqueous-Organic Systems, Chemistry Data Series,

DECHEMA, 1977.

Gmehling, J. and Onken, U.: Vapor-Liquid Equilibrium Data Collection: Vol-

ume I, Part 1c(in conjunction with part 1d)-Aqueous Systems (Supplement

3), Chemistry Data Series, DECHEMA, 2003a.

Gmehling, J. and Onken, U.: Vapor-Liquid Equilibrium Data Collection: Vol-

ume I, Part 1d(in conjunction with part 1c)-Aqueous Systems (Supplement

4), Chemistry Data Series, DECHEMA, 2003b.

Gmehling, J., Onken, U., and Arlt, W.: Vapor-Liquid Equilibrium Data

Collection: Volume I, Part 1a-Aqueous-Organic Systems(Supplement 1),

Chemistry Data Series, DECHEMA, 1981.

Gmehling, J., Onken, U., and Rarey-Nies, J. R.: Vapor-Liquid Equilibrium

Data Collection: Volume I, Part 1b-Aqueous Systems (Supplement 2),

Chemistry Data Series, DECHEMA, 1988.

Gmehling, J., Li, J., and Schiller, M.: A modified UNIFAC model. 2. Present

parameter matrix and results for different thermodynamic properties, Ind.

Eng. Chem. Res., 32, 178–193, doi:10.1021/ie00013a024, 1993.

Gmehling, J., Lohmann, J., Jakob, A., Li, J., and Joh, R.: A modified UNI-

FAC (Dortmund) model. 3. Revision and extension, Ind. Eng. Chem. Res.,

37, 4876–4882, doi:10.1021/ie980347z, 1998.

Gmehling, J., Wittig, R., Lohmann, J., and Joh, R.: A modified UNIFAC

(Dortmund) model. 4. Revision and extension, Ind. Eng. Chem. Res., 41,

1678–1688, doi:10.1021/ie0108043, 2002.

Gmehling, J., Kolbe, B., Kleiber, M., and Rarey, J.: Chemical Thermody-

namics for Process Simulation, Wiley-VCH:Weinheim, 2012.

Goldstein, A. H. and Galbally, I. E.: Known and unexplored organic con-

stituents in the earth’s atmosphere, Environ. Sci.Technol., 41, 1514–1521,

doi:10.1021/es072476p, 2007.

210 Bibliography

Gomis-Yagues, V., Ruız-Bevia, F., Ramos-Nofuentes, M., and Fernandez-

Torres, M.: The influence of the temperature on the liquid–liquid equilib-

rium of the ternary system 1-butanol–1-propanol–water, Fluid phase equi-

lib., 149, 139–145, 1998.

Gonzalez, E.: Vapor-liquid equilibria at 101.32 kPa mixtures of butyl esters

and propan-2-ol., J. Chem. Eng. Jpn., 29, 294–299, 1996.

Griswold, J. and Wong, S.: Phase-equilibria of the actone-methanol-water

system from 100- into the critical region, Chem.eng.symp.ser., 48, 18–34,

1952.

Hallquist, M., Wenger, J., Baltensperger, U., Rudich, Y., Simpson, D., Claeys,

M., Dommen, J., Donahue, N., George, C., Goldstein, A., Hamilton, J. F.,

Herrmann, H., Hoffmann, T., Iinuma, Y., Jang, M., Jenkin, M. E., Jimenez,

J. L., Kiendler-Scharr, A., Maenhaut, W., McFiggans, G., Mentel, T. F.,

Monod, A., Prevot, A. S. H., Seinfeld, J. H., Surratt, J. D., Szmigielski, R.,

and Wildt, J.: The formation, properties and impact of secondary organic

aerosol: current and emerging issues, Atmos. Chem. Phys., 9, 5155–5236,

2009.

Hansen, H. K., Rasmussen, P., Fredenslund, A., Schiller, M., and Gmehling,

J.: Vapor–liquid-equilibria by UNIFAC group contribution. 5. Revision and

extension, Ind. Eng. Chem. Res., 30, 2352–2355, 1991.

Hansen, J., Sato, M., and Ruedy, R.: Radiative forcing and climate response,

J. Geophys. Res., 102, 6831–6864, 1997.

Haughton, C.: V-I equilbria of benzene and acetic acid, Br.Chem.Eng., 12,

1102–1103, 1967.

Hill, A.: The mutual solubility of liquids. I. The mutual solubility of ethyl

ether and water. II. The solubility of water in benzene, J. Am. Chem. Soc.,

45, 1143–1155, doi:10.1021/ja01658a007, 1923.

Hirata, M. and Hoshino, D.: A Study on Vapor-Liquid Equilibria Based on

the Theory of Solution of Groups Model, Asahi Garasu Kogyo Gijutsu

Shoreikai Kenkyu Hokoku, 41, 115–122, 1982.

Bibliography 211

Hirata, M., Ohe, S., and Nagahama, K.: Computer aided data book of vapor-

liquid equilibria, Elsevier Science & Technology, 1975.

Ito, T. and Yoshida, F.: Vapor-Liquid Equilibria of Water-Lower Fatty Acid

Systems: Water-Formic Acid, Water Acetic Acid and Water-Propionic

Acid., J. Chem. Eng. Data, 8, 315–320, 1963.

Jacobson, M., Hansson, H., Noone, K., and Charlson, R.: Organic atmo-

spheric aerosols: Review and state of the science, Rev.Geophys., 38, 267–

294, doi:10.1029/1998RG000045, 2000.

Jakob, A.: unpublished data, Universitaet Oldenburg, 1994.

Jakob, A., Joh, R., Rose, C., and Gmehling, J.: Solid-liquid equilibria in

binary mixtures of organic compounds, Fluid Phase Equilib., 113, 117–126,

1995.

Jakob, A., Grensemann, H., Lohmann, J., and Gmehling, J.: Further devel-

opment of modified UNIFAC (Dortmund): Revision and extension 5, Ind.

Eng. Chem. Res., 45, 7924–7933, doi:10.1021/ie060355c, 2006.

Jaoui, M., Achard, C., and Rogalski, M.: Solubility as a function of tem-

perature of selected chlorophenols and nitrophenols in aqueous solutions

containing electrolytes or surfactants, J. Chem. Eng. Data, 47, 297–303,

doi:10.1021/je0102309, 2002.

Jathar, S., Farina, S., Robinson, A., and Adams, P.: The influence of semi-

volatile and reactive primary emissions on the abundance and properties

of global organic aerosol, Atmospheric Chemistry and Physics Discussions,

11, 5493–5540, doi:10.5194/acp-11-7727-2011, 2011.

Jimenez, J. L., Canagaratna, M.R.and Donahue, N. M., Prevot, A. S. H.,

Zhang, Q., Kroll, J. H., DeCarlo, P. F., Allan, J. D., Coe, H., Ng,

N. L., Aiken, A. C., Docherty, K., Ulbrich, I., Grieshop, A., Robinson,

A. L.and Duplissy, J., Smith, J. D., Wilson, K., Lanz, V. A. Hueglin, C.,

Sun, Y., Tian, J., Laaksonen, A., Raatikainen, T., Rautiainen, J., Vaatto-

vaara, P., Ehn, M., Kulmala, M., Tomlinson, J. M., Collins, D., Cubison,

M., Dunlea, E., Huffman, J., Onasch, T., Alfarra, M., Williams, P., Bower,

212 Bibliography

K., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S.,

Demerjian, K., Salcedo, D., Cottrell, L., Griffin, R., Takami, A., Miyoshi,

T., Hatakeyama, S., Shimono, A., Sun, J. Y., Zhang, Y. M., Dzepina, K.,

Kimmel, J. R., Sueper, D., Jayne, J. T., Herndon, S. C., Trimborn, A. M.,

Williams, L. R., Wood, E., Middlebrook, A. M., Kolb, C., Baltensperger,

U., and Worsnop, D. R.: Evolution of organic aerosols in the atmosphere,

Science, 326, 1525–1529, doi:10.1126/science.1180353, 2009.

Johnson, D., Utembe, S., and Jenkin, M.: Simulating the detailed chemical

composition of secondary organic aerosol formed on a regional scale during

the TORCH 2003 campaign in the southern UK, Atmos. Chem. Phys., 6,

419–431, 2006.

Kalberer, M., Paulsen, D., Sax, M., Steinbacher, M., Dommen, J., Prevot,

A., Fisseha, R., Weingartner, E., Frankevich, V., Zenobi, R., and Bal-

tensperger, U.: Identification of polymers as major components of at-

mospheric organic aerosols, Science, 303, 1659–1662, doi:10.1126/science.

1092185, 2004.

Kanakidou, M., Seinfeld, J. H., Pandis, S. N., Barnes, I., Dentener, F. J.,

Facchini, M. C., Van Dingenen, R., Ervens, B., Nenes, A., Nielsen, C. J.,

Swietlicki, E., Putaud, J. P., Balkanski, Y., Fuzzi, S., Horth, J., Moort-

gat, G. K., Winterhalter, R., Myhre, C. E. L., Tsigaridis, K., Vignati,

E., Stephanou, E. G., and Wilson, J.: Organic aerosol and global climate

modelling: a review, Atmos. Chem. Phys., 5, 1053–1123, 2005.

Kanno, H., Miyata, K., Tomizawa, K., and Tanaka, H.: Additivity rule holds

in supercooling of aqueous solutions, J. Phys. Chem. A, 108, 6079–6082,

doi:10.1021/jp048676u, 2004.

Kanno, H., Soga, M., and Kajiwara, K.: Linear relation between

TH(homogeneous ice nucleation temperature) and Tm(melting tempera-

ture) for aqueous solutions of sucrose, trehalose, and maltose, Chem. Phys.

Lett., 443, 280–283, 2007.

Keyes, D.: Liquid-Vapor Composition Curves of Acetic Acid and Wa-

ter at Subatmospheric Pressures, Ind. Eng. Chem., 25, 569–569, doi:

10.1021/ie50281a026, 1933.

Bibliography 213

Kim, J. and Park, D.: Liquid-liquid equilibrium for the ternary systems of

solvents+ water+ propionic acid at 25 and atmospheric pressure, Korean

J. Chem. Eng., 22, 256–263, 2005.

Kjellander, R. and Florin, E.: Water structure and changes in thermal sta-

bility of the system poly (ethylene oxide)–water, J. Chem. Soc., Faraday

Trans. 1, 77, 2053–2077, 1981.

Kleinman, L. I., Springston, S. R., Daum, P. H., Lee, Y.-N., Nunnerma-

cker, L. J., Senum, G. I., Wang, J., Weinstein-Lloyd, J., Alexander,

M. L., Hubbe, J., Ortega, J., Canagaratna, M. R., and Jayne, J.: The

time evolution of aerosol composition over the Mexico City plateau, At-

mos. Chem. Phys, 8, 1559–1575, doi:10.5194/acp-8-1559-2008, URL http:

//www.atmos-chem-phys.net/8/1559/2008/, 2008.

Kliment, V., Fried, V., and Pick, J.: Gleichgewicht Flussigkeit-dampf

XXXIII.Systeme Butylacetat-Phenol und wasser-Phenol, Collect. Czech.

Chem. Commun., 29, 2008–2015, 1964.

Knight, W.: Thermodynamics of Aqueous Solutions of Alcohols and P-

dioxane, Princeton University, URL http://books.google.ch/books?id=

D3icPQAACAAJ, 1962.

Knopf, D. and Rigg, Y.: Homogeneous ice nucleation from aqueous inor-

ganic/organic particles representative of biomass burning: Water activity,

freezing temperatures, nucleation rates, J. Phys. Chem. A, 115, 762–773,

doi:10.1021/jp109171g, 2011.

Knopf, D., Luo, B., Krieger, U., and Koop, T.: Thermodynamic dissociation

constant of the bisulfate ion from Raman and ion interaction modeling

studies of aqueous sulfuric acid at low temperatures, J. Phys. Chem. A.,

107, 4322–4332, doi:10.1021/jp027775+, 2003.

Knopf, D., Anthony, L., and Bertram, A.: Reactive uptake of O3 by multi-

component and multiphase mixtures containing oleic acid, J. Phys. Chem.

A,, 109, 5579–5589, doi:10.1021/jp0512513, 2005.



http://books.google.ch/books?id=D3icPQAACAAJ

http://books.google.ch/books?id=D3icPQAACAAJ

214 Bibliography

Knopf, D. A. and Lopez, M. D.: Homogeneous ice freezing temperatures and

ice nucleation rates of aqueous ammonium sulfate and aqueous levoglucosan

particles for relevant atmospheric conditions, Phys. Chem. Chem. Phys., 11,

8056–8068, 2009.

Koga, Y., Tanaka, T., Atake, T., Westh, P., and Hvidt, A.: Mixing Schemes

and Liquid-Solid Phase Diagram in the Water-Rich Region of Aqueous 2-

Butoxyethanol., Bull. Chem. Soc. Jpn., 67, 2393–2397, 1994.

Kolb, D.: Solubilities of fatty acids in selected organic solvents at low tem-

peratures, Diss. Abstr., 20, 82–86, 1959.

Koop, T.: Homogeneous ice nucleation in water and aqueous solutions, Z.

Phys. Chem., 218, 1231–1258, doi:10.1524/zpch.218.11.1231.50812, 2004.

Koop, T. and Zobrist, B.: Parameterizations for ice nucleation in biological

and atmospheric systems, Phys. Chem. Chem. Phys., 11, 10 839–10 850,

2009.

Koop, T., Luo, B., Tsias, A., and Peter, T.: Water activity as the determinant

for homogeneous ice nucleation in aqueous solutions, Nature, 406, 611–614,

doi:10.1038/35020537, 2000.

Koop, T., Bookhold, J., Shiraiwa, M., and Poschl, U.: Glass transition and

phase state of organic compounds: dependency on molecular properties and

implications for secondary organic aerosols in the atmosphere, Phys. Chem.

Chem. Phys., 13, 19 238–19 255, doi:10.1039/C1CP22617G, 2011.

Krieger, U., Colberg, C., Weers, U., Koop, T., and Peter, T.: Supercool-

ing of single H2SO4/H2O aerosols to 158 K: No evidence for the oc-

currence of the octrahydrate, Geophys. Res. Lett, 27, 2097–2100, doi:

10.1029/2000GL011613, 2000.

Krieger, U. K., Marcolli, C., and Reid, J. P.: Exploring the complexity of

aerosol particle properties and processes using single particle techniques,

Chem. Soc. Rev., 41, 6631 – 6662, 2012.

Bibliography 215

Kurihara, K., Hori, H., and Kojima, K.: Vapor-liquid equilibrium data for

acetone + methanol + benzene, chloroform + methanol + benzene, and

constituent binary systems at 101.3 kPa, J. Chem. Eng. Data, 43, 264–268,

doi:10.1021/je970231u, 1998.

Lalande, A.: Equilibres entre Phases Condensees dans le systeme eau-alcool-

ether, J. Chim. Phys. Phys. Chim. Biol., 31, 583–609, 1934.

Larsen, B., Rasmussen, P., and Fredenslund, A.: A modified UNIFAC group-

contribution model for prediction of phase equilibria and heats of mixing,

Ind. Eng. Chem. Res., 26, 2274–2286, doi:10.1021/ie00071a018, 1987.

Lee, M. and Hu, C.: Isothermal vapor-liquid equilibria for mixtures of ethanol,

acetone, and diisopropyl ether, Fluid phase equilib., 109, 83–98, 1995.

Lerici, C., Piva, M., and ROSA, M.: Water activity and freezing point de-

pression of aqueous solutions and liquid foods, J. Food Sci., 48, 1667–1669,

2006.

Letcher, T., Redhi, G., Radloff, S., and Domanska, U.: Liquid-Liquid Equi-

libria for Mixtures of Butanal+ an Alkanol+ Water at 298.15 K, J. Chem.

Eng. Data, 41, 707–712, 1996.

Leu, A., Chen, C., and Robinson, D.: Vapor-Liquid Equilibrium in Selected

Binary Systems, AIChE Symp. Ser., 85, 11–16, 1989.

Lewis, G.: Outlines of a new system of thermodynamic chemistry, in: Pro-

ceedings of the American Academy of Arts and Sciences, pp. 259–293, JS-

TOR, 1907.

Lewis, G. and Randall, M.: Thermodynamics, Mc-Graw-Hill, New York, ed,

2, 236, 1961.

Lienhard, D., Bones, D., Zuend, A., Krieger, U., Reid, J., and Peter, T.:

Measurements of Thermodynamic and Optical Properties of Selected Aque-

ous Organic and Organic-Inorganic Mixtures of Atmospheric Relevance, J.

Phys. Chem. A, doi:10.1021/jp3055872, 2012.

216 Bibliography

Lihua, F., Peisheng, M., and Zhengle, X.: Measurement and correlation for

solubility of adipic acid in several solvents, Chin. J. Chem. Eng., 15, 110–

114, 2007.

Lin, C., Qing, A., and Feng, Q.: A new differential mutation base generator

for differential evolution, J. Glob. Optim, 49, 69–90, 2011.

Lin, G., Penner, J., Sillman, S., Taraborrelli, D., and Lelieveld, J.:

Global modeling of SOA formation from dicarbonyls, epoxides, organic

nitrates and peroxides, Atmos. Chem. Phys, 12, 4743–4774, doi:10.5194/

acp-12-4743-2012, 2012.

Lin, H., Tien, H., Hone, Y., and Lee, M.: Solubility of selected dibasic

carboxylic acids in water, in ionic liquid of [Bmim][BF4], and in aqueous

[Bmim][BF4] solutions, Fluid Phase Equilib., 253, 130–136, 2007.

Lintomen, L., Pinto, R., Batista, E., Meirelles, A., and Maciel, M.: Liquid-

liquid equilibrium of the water+ citric acid+ short chain alcohol+ tr-

icaprylin system at 298.15 K, J. Chem. Eng. Data, 46, 546–550, doi:

10.1021/je000226h, 2001.

Liousse, C., Penner, J., Chuang, C., Walton, J., Eddleman, H., and Cachier,

H.: A global three-dimensional model study of carbonaceous aerosols, J.

Geophys. Res., 101, 19 411–19, 1996.

Loehe, J. R., Van Ness, H. C., and Abbott, M. M.: Vapor/liquid/liquid equi-

librium. Total-pressure data and GE for water/methyl acetate at 50, J.

Chem. Eng. Data, 28, 405–407, doi:10.1021/je00034a017, 1983.

Lohmann, J., Joh, R., and Gmehling, J.: Solid-liquid equilibria of viscous

binary mixtures with alcohols, J. Chem. Eng. Data, 42, 1170–1175, doi:

10.1021/je9700683, 1997.

Lohmann, J., Joh, R., and Gmehling, J.: From UNIFAC to modified UNIFAC

(Dortmund), Ind. Eng. Chem. Res., 40, 957–964, doi:10.1021/ie0005710,

2001.

Bibliography 217

Maffia, M. and Meirelles, A.: Water activity and pH in aqueous polycarboxylic

acid systems, J. Chem. Eng. Data, 46, 582–587, doi:10.1021/je0002890,

2001.

Mafra, M. and Krahenbuhl, M.: Liquid-Liquid Equilibrium of (Water+ Ace-

tone) with Cumene or α-Methylstyrene or Phenol at Temperatures of

(323.15 and 333.15) K, J. Chem. Eng. Data, 51, 753–756, 2006.

Marcolli, C. and Krieger, U. K.: Phase changes during hygroscopic cycles of

mixed organic/inorganic model systems of tropospheric aerosols, J. Phys.

Chem. A, 110, 1881–1893, doi:10.1021/jp0556759, 2006.

Marcolli, C. and Peter, T.: Water activity in polyol/water systems: new

UNIFAC parameterization, Atmos. Chem. Phys., 5, 1545–1555, 2005.

Marcolli, C., Luo, B. P., Peter, T., and Wienhold, F. G.: Internal mixing

of the organic aerosol by gas phase diffusion of semivolatile organic com-

pounds, Atmos. Chem. Phys., 4, 2593–2599, doi:10.5194/acp-4-2593-2004,

URL http://www.atmos-chem-phys.net/4/2593/2004/, 2004.

Martin, M., Cocero, M., and Mato, R.: Vapor-Liquid Equilibrium Data at

298.15 K for Binary Systems Containing Methyl Acetate or Methanol with

2-Methoxyethanol or 2-Ethoxyethanol, J. Chem. Eng. Data, 39, 535–537,

doi:10.1021/je00015a030, 1994.

Martin, S., Rosenoern, T., Chen, Q., and Collins, D.: Phase changes of am-

bient particles in the Southern Great Plains of Oklahoma, Geophys. Res.

Lett., 35, L22 801, doi:10.1029/2008GL035650, 2008.

Martınez, N., Lladosa, E., Burguet, M., and Monton, J.: Isobaric vapour–

liquid equilibria for binary systems of 2-butanone with ethanol, 1-propanol,

and 2-propanol at 20 and 101.3 kPa, Fluid Phase Equilib., 270, 62–68, 2008.

May, W., Wasik, S., Miller, M., Tewari, Y., Brown-Thomas, J., and Gold-

berg, R.: Solution thermodynamics of some slightly soluble hydrocarbons

in water, J. Chem. Eng. Data, 28, 197–200, doi:10.1021/je00032a021, 1983.

Meehan, G. and Murphy, N.: A new correlation for binary systems with an

associating component, Chemical Engineering Science, 20, 757–769, 1965.


218 Bibliography

Mejıa, A., Segura, H., Cartes, M., Cifuentes, L., and Flores, M.: Phase equi-

libria and interfacial tensions in the systems methyl tert-butyl ether+ ace-

tone+ cyclohexane, methyl tert-butyl ether+ acetone and methyl tert-butyl

ether+ cyclohexane, Fluid Phase Equilib., 273, 68–77, 2008.

Mertl, I.: Liquid-Vapor Equilibrium.IL.Phase Equilibria in the ternary system

ethyl acetate-ethanol-water, Collect. Czech. Chem. Commun., 37, 366–374,

1972.

Miao, X., Zhang, H., Wang, T., and He, M.: Liquid-liquid equilibria of the

ternary system water+ acetic acid+ methyl tert-butyl ether, J. Chem. Eng.

Data, 52, 789–793, doi:10.1021/je060409p, 2007.

Mikhailov, E., Vlasenko, S., Martin, S., Koop, T., and Poschl, U.: Amorphous

and crystalline aerosol particles interacting with water vapor: conceptual

framework and experimental evidence for restructuring, phase transitions

and kinetic limitations, Atmos. Chem. Phys, 9, 9491–9522, 2009.

Ming, Y. and Russell, L.: Thermodynamic equilibrium of organic-electrolyte

mixtures in aerosol particles, AIChE journal, 48, 1331–1348, doi:10.1002/

aic.690480619, 2002.

Miyamoto, S., Nakamura, S., Iwai, Y., and Arai, Y.: Measurement of isother-

mal vapor-liquid equilibria for hydrocarbon+ monocarboxylic acid binary

systems by a flow-type apparatus, J. Chem. Eng. Data, 45, 857–861, doi:

10.1021/je000015c, 2000.

Miyamoto, S., Nakamura, S., Iwai, Y., and Arai, Y.: Measurement of isother-

mal vapor-liquid equilibria for binary and ternary systems containing mono-

carboxylic acid, J. Chem. Eng. Data, 46, 1225–1230, doi:10.1021/je0003849,

2001.

Miyata, K. and Kanno, H.: Supercooling behavior of aqueous solutions of

alcohols and saccharides, Journal of molecular liquids, 119, 189–193, 2005.

Moeller, W., Englund, S., Tsui, T. K., and Othmer, D.: Compositions of

Vapors from Boiling Solutions - Equilibria under Pressure of Systems:

Ethyl Ether-Ethyl Alcohol and Ethyl Ether-Water-Ethyl Alcohol, Ind. Eng.

Chem., 43, 711–717, doi:10.1021/ie50495a039, 1951.

Bibliography 219

Monton, J., Munoz, R., Burguet, M., and Torre, J.: Isobaric vapor–liquid

equilibria for the binary systems isobutyl alcohol+ isobutyl acetate and

tert-butyl alcohol+ tert-butyl acetate at 20 and 101.3 kPa, Fluid phase

equilib., 227, 19–25, 2005.

Mozzhukhin, A., Mitropolskaya, V., Serafimov, L., and Torubarov, A.:

Liquid-Vapor Phase Equilibrium in Binary Mixtures of Some Oxygen-

Containing Products at 760 mm Hg, Zh. Fiz. Khim., 41, 116–119, 1967.

Mun, S. and Lee, H.: Vapor-liquid equilibria of the water+ 1, 3-propanediol

and water+ 1, 3-propanediol+ lithium bromide systems, J. Chem. Eng.

Data, 44, 1231–1234, 1999.

Murphy, D. and Koop, T.: Review of the vapour pressures of ice and su-

percooled water for atmospheric applications, Q.J.R.Meteorol. Soc., 131,

1539–1565, doi:10.1256/qj.04.94, 2006.

Murphy, D., Cziczo, D., Froyd, K., Hudson, P., Matthew, B., Middlebrook,

A., Peltier, R., Sullivan, A., Thomson, D., and Weber, R.: Single-particle

mass spectrometry of tropospheric aerosol particles, J. Geophys. Res., 111,

D23S32, doi:10.1029/2006JD007340, 2006.

Murphy, D., Cziczo, D., Hudson, P., and Thomson, D.: Carbonaceous mate-

rial in aerosol particles in the lower stratosphere and tropopause region, J.

Geophys. Res., 112, D04 203, doi:10.1029/2006JD007297, 2007.

Narayana, A., Naik, S., and Rath, P.: Salt effect in isobaric vapor-liquid

equilibria of acetic acid-water system, J. Chem. Eng. Data, 30, 483–485,

doi:10.1021/je00042a035, 1985.

Naumann, D. and Wagner, H.: Vapour-liquid equilibria of several mixtures

containing 2-butanone, 2-butenal, ethoxyethanol, and toluene at 330.15 K,

J. Chem. Thermodyn., 18, 81–87, 1986.

Negadi, L., Wilken, M., and Gmehling, J.: Solid-liquid equilibria for binary

organic systems containing 1-methoxy-2-propanol and 2-butoxy ethanol, J.

Chem. Eng. Data, 51, 1873–1876, 2006.

220 Bibliography

Nelder, J. A. and Mead, R.: A simplex method for function minimization,

Comput. J, 7, 308–313, 1965.

Newman, M., Hayworth, C., and Treybal, R.: Dehydration of Aqueous Methyl

Ethyl Ketone-Equilibrium Data for Extractive Distillation and Solvent Ex-

traction, J. Chem. Eng. Data, 41, 2039–2043, doi:10.1021/je00009a031,

1949.

Nordstrom, F.: Solid-liquid Phase Equilibria and Crystallization of Disubsti-

tuted Benzene Derivatives, Ph.D. thesis, KTH, 2008.

Novakov, T., Hegg, D., and Hobbs, P.: Airborne measurements of carbona-

ceous aerosols on the East Coast of the United States, J. Geophys. Res.,

102, 30 023–30, doi:10.1029/97JD02793, 1997.

Oh, J. and Park, S.: Isothermal vapor-liquid equilibria at 333.15 K and excess

molar volumes at 298.15 K of ethyl tert-butyl ether (ETBE)+ alcoh-1-

ol (C1-C4) mixtures, J. Chem. Eng. Data, 43, 1009–1013, doi:10.1021/

je980089c, 1998.

Ohta, T., Koyabu, J., and Nagata, I.: Vapor-liquid equilibria for the ternary

Ethanol–2-butanone–benzene system at 298.15 K, Fluid Phase Equilib., 7,

65–73, 1981.

Okamoto, B., Wood, R., and Thompson, P.: Freezing points of aqueous alco-

hols. Free energy of interaction of the CHOH, CH2, CONH and C=C func-

tional groups in dilute aqueous solutions, J. Chem. Soc., Faraday Trans. 1,

74, 1990–2007, 1978.

Olsen, J., Brunjes, A., and Olsen, J.: Freezing and Flow Points for Glycerol,

Prestone, Denatured Alcohol, and Methanol, Ind. Eng. Chem., 22, 1315–

1317, 1930.

Omar, W. and Ulrich, J.: Solid liquid equilibrium, metastable zone, and

nucleation parameters of the oxalic acid-water system, Crystal growth &

design, 6, 1927–1930, doi:10.1021/cg060112n, 2006.

Othmer, D.: Composition of vapors from boiling binary solutions, nd. Eng.

Chem, 35, 614–620, doi:10.1021/ie50401a018, 1943.

Bibliography 221

Othmer, D., Chudgar, M., and Levy, S.: Binary and ternary systems of ace-

tone, methyl ethyl ketone, and water, Ind. Eng. Chem., 44, 1872–1881,

doi:10.1021/ie50512a042, 1952.

Othmer, D. F. and Benenati, R.: Composition of Vapors from Boiling Binary

Solutions., Ind. Eng. Chem, 37, 299–303, doi:10.1021/ie50423a024, 1945.

Ott, J., Goates, J., and Lamb, J.: Solid-liquid phase equilibria in water+

ethylene glycol, J. Chem. Thermodyn., 4, 123–126, 1972.

Pankow, J.: Gas/particle partitioning of neutral and ionizing compounds to

single and multi-phase aerosol particles. 1. Unified modeling framework,

Atmospheric Environment, 37, 3323–3333, 2003.

Park, S., Han, K., and Gmehling, J.: Vapor–liquid equilibria and excess prop-

erties for methyl¡ i¿ tert¡/i¿-butyl ether (MTBE) containing binary systems,

Fluid phase equilib., 200, 399–409, 2002.

Paterno, E. and Ampola, G.: SUl Massimo abassamento nella temperatura

di congelamento dei miscugli, Gazz.Chim.Ital, 27, 481–536, 1897.

Peng, C., Chan, M. N., and Chan, C. K.: The hygroscopic properties of dicar-

boxylic and multifunctional acids: Measurements and UNIFAC predictions,

Environ. Sci. Technol., 35, 4495–4501, doi:10.1021/es0107531, 2001.

Perrakis, N.: The physical properties of liquid double mixtures in the neigh-

borhood of the critical state of miscibility, J. Chim. Phys., 22, 280–305,

1925.

Peter, T., Marcolli, C., Spichtinger, P., Corti, T., Baker, M. B., and Koop,

T.: When dry air is too humid, Science, 314, 1399–1402, 2006.

Pickering, S.: LXXI. A study of the properties of some strong solutions, J.

Chem.Soc., 63, 998–1027, 1893.

Pio, C., Alves, C., and Duarte, A.: Identification, abundance and origin of

atmospheric organic particulate matter in a Portuguese rural area, Atmo-

spheric Environment, 35, 1365–1375, 2001.

Pitzer, K.: Activity coefficients in electrolyte solutions, CRC press, 1991.

222 Bibliography

Poschl, U.: Atmospheric aerosols: Composition, transformation, climate and

health effects, Angew. Chem. Int. Ed., 44, 7520–7540, doi:10.1002/anie.

200501122, 2005.

Poschl, U.: Gas–particle interactions of tropospheric aerosols: Kinetic and

thermodynamic perspectives of multiphase chemical reactions, amorphous

organic substances, and the activation of cloud condensation nuclei, Atmo-

spheric Research, 101, 562–573, 2011.

Powell, M.: The BOBYQA algorithm for bound constrained optimization

without derivatives, Cambridge NA Report NA2009/06, University of Cam-

bridge, Cambridge, 2009.

Prausnitz, J., Lichtenthaler, R., and de Azevedo, E.: Molecular thermody-

namics of fluid-phase equilibria, Prentice-Hall Inc., Englewood Cliffs, New

Jersey, USA. 2nd edn, 1986.

Proust, P. and Fernandez, J.: Experimental solid-liquid equilibria of binary

mixtures of organic compounds, Fluid Phase Equilib., 29, 265–272, 1986.

Pushin, N. and Glagoleva, A.: CCCXXXVII. The equilibrium in systems

composed of water and alcohols: methyl alcohol, pinacone, glycerol, and

erythritol, J. Chem. Soc., 121, 2813–2822, 1922.

Pye, H. and Seinfeld, J.: A global perspective on aerosol from low-volatility

organic compounds, Atmos. Chem. Phys., 10, 4377–4401, doi:10.5194/

acp-10-4377-2010, 2010.

Raatikainen, T. and Laaksonen, A.: Application of several activity coefficient

models to water-organic-electrolyte aerosols of atmospheric interest, Atmos.

Chem. Phys., 5, 2475–2495, 2005.

Radnai, G., Rasmussen, P., and Fredenslund, A.: :Vapor-Liquid Equilib-

rium data for binary mixtures contianing aldehydes and esters and for

the mixture 1,1,2 Tricholoro-1,2,2 Trifluoroethane plus n-hexane, AIChE

Symp.Ser., 83, 70–79, 1987.

Bibliography 223

Ramanathan, V., Crutzen, P., and Kiehl, J.T.and Rosenfeld, D.: Aerosols,

climate, and the hydrological cycle, science, 294, 2119–2124, doi:10.1126/

science.1064034, 2001.

Randles, C., Russell, L., and Ramaswamy, V.: Hygroscopic and optical prop-

erties of organic sea salt aerosol and consequences for climate forcing, Geo-

phys. Res. Lett., 31, L16 108, doi:10.1029/2004GL020628, 2004.

Rarey, J., Horstmann, S., and Gmehling, J.: Vapor-liquid equilibria and vapor

pressure data for the systems ethyl tert-butyl ether+ ethanol and ethyl

tert-butyl ether+ water, J. Chem. Eng. Data, 44, 532–538, doi:10.1021/

je980229i, 1999.

Rasmussen, P.and Kierkegaard, L. and Fredenslund, A.: Vapor-Liquid Equi-

libria in Ketone - Organic Acid Mixtures, Chemical Engineering with Per

Soltoft, Copenhagen, pp. 129–139, 1977.

Reichl, A., Daiminger, U., Schmidt, A., Davies, M., Hoffmann, U.,

Brinkmeier, C., Reder, C., and Marquardt, W.: A non-recycle flow still

for the experimental determination of vapor–liquid equilibria in reactive

systems, Fluid phase equilib., 153, 113–134, 1998.

Reid, J., Dennis-Smither, B., Miles, R., Hanford, K., Homer, C., et al.:

The morphology of aerosol particles consisting of hydrophobic and hy-

drophilic phases: hydrocarbons, alcohols and fatty acids as the hydropho-

bic component, Phys. Chem. Chem. Phys., 13, 15 559–15 572, doi:10.1039/

c1cp21510h, 2011.

Rius, A., Otero, J., and Macarron, A.: Equilibres liquide-vapeur de melanges

binaires donnant une reaction chimique: systemes methanol-acide acetique

; ethanol-acide acetique ; n-propanol-acide acetique ; n-butanol-acide

acetique, Chem. Eng. Sci., 10, 105 – 111, doi:10.1016/0009-2509(59)

80029-4, 1959.

Roberts, G., Artaxo, P., Zhou, J., Swietlicki, E., and Andreae, M.: Sensitivity

of CCN spectra on chemical and physical properties of aerosol: A case

study from the Amazon Basin, J. Geophys. Res., 107, 8070, doi:10.1029/

2001JD000583, 2002.

224 Bibliography

Robinson, A., Donahue, N., Shrivastava, M., Weitkamp, E., Sage, A.,

Grieshop, A., Lane, T., Pierce, J., and Pandis, S.: Rethinking organic

aerosols: Semivolatile emissions and photochemical aging, Science, 315,

1259–1262, doi:10.1126/science.1133061, 2007.

Rodebush, W.: The freezing points of concentrated solutions and the free

energy of solutions of salts., J. Am. Chem. Soc., 40, 1204–1213, doi:10.

1021/ja02241a008, 1918.

Rogge, W., Mazurek, M., Hildemann, L., Cass, G., and Simoneit, B.: Quan-

tification of urban organic aerosols at a molecular level: identification, abun-

dance and seasonal variation, Atmos. Environ. Part A, 27, 1309–1330, 1993.

Roloff, M.: Beitrage zur Kenntnis der Kryohydrate, Z.Phys.Chem.Leipzig.,

17, 325–356, 1895.

Ross, H.: Cryoscopic Studies-Concentrated Solutions of Hydroxy Compounds,

Ind. Eng. Chem, 46, 601–610, doi:10.1021/ie50531a054, 1954.

Ruegg, M. and Blanc, B.: The water activity of honey and related sugar

solutions, Lebensm.-Wiss.u.-Technol., 14, 1–6, 1981.

Ruiz Bevia, F., Prats Rico, D., Gomis Yagues, V., and Varo Galvan, P.:

Quaternary liquid-liquid equilibrium: water-acetic acid-1-butanol-n-butyl

acetate at 25, Fluid Phase Equilibria, 18, 171–183, 1984.

Salabat, A.: Liquid–liquid equilibrium in the ternary systems triethylene gly-

col+ salts+ water at different temperatures: Experimental determination

and correlation, Fluid Phase Equilibria, 288, 63–66, 2010.

Sanz, M., Blanco, B., Beltran, S., Cabezas, J., and Coca, J.: Vapor liquid

equilibria of binary and ternary systems with water, 1, 3-propanediol, and

glycerol, J. Chem. Eng. Data, 46, 635–639, 2001.

Sapgir, S.: Studies on the theory of concentrated solutions. VII. Application

of thermal analysis to binary mixtures consisting of organic substances with

very low melting points, Bull.Soc.Chim.Belg, 38, 392–408, 1929.

Bibliography 225

Saxena, P. and Hildemann, L.: Water-soluble organics in atmospheric parti-

cles: A critical review of the literature and application of thermodynamics

to identify candidate compounds, J. Atmos. Chem., 24, 57–109, 1996.

Saxena, P. and Hildemann, L. M.: Water absorption by organics: Survey of

laboratory evidence and evaluation of UNIFAC for estimating water activ-

ity, Environ. Sci. Technol., 31, 3318–3324, 1997.

Scatchard, G. and Wilson, G.: Vapor-liquid equilibrium. XIII. the system

water-butyl glycol from 5 to 85 J. Am. Chem. Soc., 86, 133–137, doi:10.

1021/ja01056a004, 1964.

Schneider, G. and Wilhelm, G.: Vapor-Liquid Equilibrium of the System

Water - 2-Butoxyethanol, Z. Phys. Chem. NF, 20, 219–232, 1959.

Schreinemakers, F.: Dampfdrucke binarer und ternarer Gemische,

Z.Phys.Chem., 35, 459–479, 1900.

Sebastiani, E. and Lacquaniti, L.: Acetic acid-water system thermodynamical

correlation of vapor-liquid equilibrium data, Chem. Eng. Sci., 22, 1155–

1162, 1967.

Sei, T. and Gonda, T.: Melting point of ice in aqueous saccharide solutions,

Journal of crystal growth, 293, 110–112, 2006.

Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From

Air Pollution to Climate Change, J. Wiley and Sons, New York, USA,,

1998.

Senol, A.: Phase equilibria for ternary liquid systems of (water+ carboxylic

acid or alcohol+ 1-hexanol) at T = 293.15 K: modelling considerations, J.

Chem. Thermodyn., 36, 1007–1014, 2004.

Shalmashi, A. and Eliassi, A.: Solubility of salicylic acid in water, ethanol,

carbon tetrachloride, ethyl acetate, and xylene, J. Chem. Eng. Data, 53,

199–200, 2008.

Shanghai-Inst. and Zhejiang, U.: Measurement of Vapor-Liquid Equilib-

rium for the Systems in the Industrial Preparation of Acetic Acid (I),

Huaxue.Gongcheng, 1, 75–85, 1978.

226 Bibliography

Simpson, D., Yttri, K., Klimont, Z., Kupiainen, K., Caseiro, A., Gelencser,

A., Pio, C., Puxbaum, H., and Legrand, M.: Modeling carbonaceous aerosol

over Europe: Analysis of the CARBOSOL and EMEP EC/OC campaigns,

J. Geophys. Res., 112, D23S14, doi:10.1029/2006JD008158, 2007.

Smith, M., Kuwata, M., and Martin, S.: Secondary Organic Material Pro-

duced by the Dark Ozonolysis of α-Pinene Minimally Affects the Deliques-

cence and Efflorescence of Ammonium Sulfate, Aerosol Sci. Technol., 45,

244–261, doi:10.1080/02786826.2010.532178, 2011.

Solimo, H., Bonatti, C., Zurita, J., and Gramajo de Doz, M.: Liquid-liquid

equilibria for the system water+ propionic acid + 1-butanol at 303.2 K.

Effect of addition of sodium chloride, Fluid Phase Equilib., 137, 163–172,

1997.

Song, M., Marcolli, C., Krieger, U., Zuend, A., and Peter, T.: Liquid-

liquid phase separation and morphology of internally mixed dicarboxylic

acids/ammonium sulfate/water particles, Atmos. Chem. Phys., 12, 2691–

2712, doi:10.5194/acp-12-2691-2012, 2012.

Soujanya, J., Satyavathi, B., and Vittal Prasad, T.: Experimental (vapour+

liquid) equilibrium data of (methanol+ water),(water+ glycerol) and

(methanol+ glycerol) systems at atmospheric and sub-atmospheric pres-

sures, J. Chem. Thermodyn., 42, 621–624, 2010.

Stephenson, R.: Mutual solubilities: water-ketones, water-ethers, and water-

gasoline-alcohols, J. Chem. Eng. Data, 37, 80–95, doi:10.1021/je00005a024,

1992.

Stephenson, R. and Stuart, J.: Mutual binary solubilities: water-alcohols

and water-esters, J. Chem. Eng. Data, 31, 56–70, doi:10.1021/je00043a019,

1986.

Subrahmanyam, V. and Murty, P.: Vapour–liquid equilibria: Systems:(1)

Acetone-ethyl acetate;(2) acetone–n–propyl acetate, J. Appl. Chem., 14,

500–502, doi:10.1002/jctb.5010141106, 1964.

Suska, J.: Phase-Equilibria in binary-systems containing acetaldehyde, Col-

lect. Czech. Chem. Commun., 44, 1852–1856, 1979.

Bibliography 227

Swanson, B.: How Well Does Water Activity Determine Homogeneous Ice

Nucleation Temperature in Aqueous Sulfuric Acid and Ammonium Sul-

fate Droplets?, J. Atmos. Sci., 66, 741–754, doi:http://dx.doi.org/10.1175/

2008JAS2542.1, 2009.

Takahashi, S., Otake, K., Takahashi, T., and Iguchi, A.: Liquid-liquid equilib-

ria of phenol-water-solvent systems, Kagaku Kogaku Ronbunshu, 14, 531–

535, 1988.

Tang, I. and Munkelwitz, H.: Composition and temperature dependence of

the deliquescence properties of hygroscopic aerosols, Atmos. Environ. Part

A, 27, 467–473, 1993.

Tang, I. and Munkelwitz, H.: Water activities, densities, and refractive indices

of aqueous sulfates and sodium nitrate droplets of atmospheric importance,

J. Geophys. Res., 99, 18 801–18 808, 1994.

Tapper, M., Engelhardt, C., Schecter, B.A.;Fried, V., and Zudkevitch, D.:

Vapor-Liquid Equilibrium and Liquid-Liquid Equilibrium of The n-Butanal

- Water System, AIChE Symp. Ser., 81, 136–141, 1985.

Tarasenkov, D.: Freezing Temperature of Mixtures of Benzene with Toluene,

Alcohol and Gasoline, Zh. Prikl.Khim., 3, 153–155, 1930.

Thorat, R. and Nageshwar, G.: Excess Thermodynamic Properties and Iso-

baric Vapor-Liquid Equilibrium Data for the Binary System: Ethyl Acetate

- 2-Ethoxy Ethanol, J. Inst. Eng. India Chem. Eng. Div., 68, 59–63, 1988.

Tikhonova, N., Timofeev, V., Serafimov, L., and Kupriyanova, Z.: Vapor-

Liquid Equilibrium in the Ternary System Acetaldehyde - Acetone - Vinyl

Acetate at Atmospheric Pressure, Izv. Vyssh. Uchebn. Zaved. Khim. Khim.

Tekhnol, 13, 349–351, 1970.

Tiryaki, A., Guruz, G., and Orbey, H.: Liquid-liquid equilibria of ternary

systems of water+ acetone and C5 and C8alcohols at 298, 303 and 308 K,

Fluid phase equilib., 94, 267–280, 1994.

228 Bibliography

Tochigi, K., Goto, T., Kurita, S., and Kojima, K.: Activity coefficients for

water plus phenol system, J. Chem. Eng. Jpn, 30, 1116–1119, doi:10.1252/

jcej.30.1116, 1997.

Tong, C., Clegg, S., and Seinfeld, J.: Comparison of activity coefficient models

for atmospheric aerosols containing mixtures of electrolytes, organics, and

water, Atmospheric Environment, 42, 5459–5482, 2008.

Topping, D., McFiggans, G., and Coe, H.: A curved multi-component aerosol

hygroscopicity model framework: Part 2 Including organic compounds, At-

mos. Chem. Phys., 5, 1223–1242, 2005.

Tsapakis, M., Lagoudaki, E., Stephanou, E., Kavouras, I., Koutrakis, P.,

Oyola, P., and von Baer, D.: The composition and sources of PM2.5 organic

aerosol in two urban areas of Chile, Atmospheric Environment, 36, 3851–

3863, 2002.

Tsonopoulos, C. and Prausnitz, J. M.: Fugacity Coefficients in Vapor-Phase

Mixtures of Water and Carboxylic Acids, Chem. Eng. J., 1, 273–278, doi:

10.1016/0300-9467(70)85014-6, 1970.

Udovenko, V.V.and Aleksandrova, L.: Vapor Pressure in Three-Component

Systems. IV. Formic Acid - Benzene - Water System, Russ. J. Phys. Chem,

37, 25–27, 1963.

Vaden, T., Imre, D., Beranek, J., Shrivastava, M., and Zelenyuk, A.: Evap-

oration kinetics and phase of laboratory and ambient secondary organic

aerosol, Proceedings of the National Academy of Sciences, 108, 2190–2195,

doi:10.1073/pnas.1013391108, 2011.

Viala, M.: Etude thermique et cryoscopique des melanges de benzene et

dalcool ethylique, Bull. Soc. Chim.Fr., 15, 5–11, 1914.

Virtanen, A., Joutsensaari, J., Koop, T., Kannosto, J., Yli-Pirila, P., Lesk-

inen, J., Makela, J., Holopainen, J., Poschl, U., Kulmala, M., et al.: An

amorphous solid state of biogenic secondary organic aerosol particles, Na-

ture, 467, 824–827, doi:10.1038/nature09455, 2010.

Bibliography 229

Volkamer, R., Jimenez, J., San Martini, F., Dzepina, K., Zhang, Q., Salcedo,

D., Molina, L., Worsnop, D., and Molina, M.: Secondary organic aerosol for-

mation from anthropogenic air pollution: Rapid and higher than expected,

Geophys. Res. Lett., 33, L17 811, doi:10.1029/2006GL026899, 2006.

Wallace, J., Wallace, J., and Hobbs, P.: Atmospheric science: an introductory

survey, Academic press, 2006.

Wang, J., Jacob, J., and Martin, S.: Sensitivity of sulfate direct climate

forcing to the hysteresis of particle phase transitions, J. Geophys. Res.,

113, D11 207, 2008.

Wang, L., Cheng, Y., Xiao, X., and Li, X.: Liquid-liquid equilibria for the

ternary systems acetic acid+ water+ butyl acetate and acetic acid+ water+

2-methyl propyl acetate at 304.15 K, 332.15 K, and 366.15 K, J. Chem. Eng.

Data, 52, 1255–1257, doi:10.1021/je6005746, 2007.

Waradzin, W. and Surovy, J.: Equilibrium Data of the Liquid-Vapor Systems

Containing Acetone, Vinyl acetate, Crotonaldehyde, and Acetic acid. I.

Experimental Data for Isothermal Binary Systems Processed by Means of

the Wilson Equation, Chem. Zvesti, 29, 783–794, 1975.

Webb, T. and Lindsley, C.: The cryoscopic study of certain aliphatic alcohols,

J. Am. Chem. Soc., 56, 874–878, 1934.

Weidlich, U. and Gmehling, J.: A modified UNIFAC model. 1. Prediction

of VLE, hE, and. gamma.. infin., Ind. Eng. Chem. Res., 26, 1372–1381,

doi:10.1021/ie00067a018, 1987.

Wen, C. and Tu, C.: Vapor–liquid equilibria for binary and ternary mixtures

of ethanol, 2-butanone, and 2, 2, 4-trimethylpentane at 101.3 kPa, Fluid

phase equilib., 258, 131–139, 2007.

Whitby, K. T. and Cantrell, B.: Atmospheric aerosols – Characteristics and

measurement, 1976.

Williams, R. and Carnahan, D.: Fracture faces and other interfaces as ice

nucleation sites, Cryobiology, 27, 479–482, 1990.

230 Bibliography

Wright, E. and Akhtar, B.: Soluble surface films of short-chain monocar-

boxylic acids on organic and aqueous substrates, J. Chem. Soc. B, pp.

151–157, doi:10.1039/J29700000151, 1970.

Wylie, D., Jackson, D., Menzel, W., and Bates, J.: Trends in global cloud

cover in two decades of HIRS observations, Journal of climate, 18, 3021–

3031, doi:http://dx.doi.org/10.1175/JCLI3461.1, 2005.

Xie, Q., Wan, H., Han, M., and Guan, G.: Investigation on isobaric vapor–

liquid equilibrium for acetic acid+ water+ methyl ethyl ketone+ isopropyl

acetate, Fluid Phase Equilib., 280, 120–128, 2009.

Yamasoe, M., Artaxo, P., Miguel, A., and Allen, A.: Chemical composition

of aerosol particles from direct emissions of vegetation fires in the Amazon

Basin: water-soluble species and trace elements, Atmos. Environ., 34, 1641–

1653, 2000.

Yan, W. D., Topphoff, M., Rose, C., and Gmehling, J.: Prediction of vapor–

liquid equilibria in mixed-solvent electrolyte systems using the group con-

tribution concept, Fluid Phase Equilib., 162, 97–113, 1999.

Yaws, C. L., Narasimhan, P. K., and Gabbula, C.: Yaws’ Handbook of An-

toine coefficients for vapor pressure [electronic resource], Knovel, 2005.

Young, F.: D-glucose-water phase diagram, J. Phys. Chem., 61, 616–619,

doi:10.1021/j150551a023, 1957.

Zardini, A. A.: The effects of organic compounds on the hygroscopic prop-

erties of inorganic aerosols, Phd thesis, ETH Zurich, Zurich, Switzerland,

2007.

Zardini, A. A., Sjogren, S., Marcolli, C., Krieger, U. K., Gysel, M., Wein-

gartner, E., Baltensperger, U., and Peter, T.: A combined particle

trap/HTDMA hygroscopicity study of mixed inorganic/organic aerosol par-

ticles, Atmos. Chem. Phys., 8, 5589–5601, doi:10.5194/acp-8-5589-2008,

URL http://www.atmos-chem-phys.net/8/5589/2008/, 2008.


Bibliography 231

Zhang, Q., Worsnop, D., Canagaratna, M., and Jimenez, J.: Hydrocarbon-

like and oxygenated organic aerosols in Pittsburgh: insights into sources

and processes of organic aerosols, Atmos. Chem..Phys., 5, 3289–3311, 2005.

Zhang, Q., Jimenez, J., Canagaratna, M., Allan, J., Coe, H., Ulbrich, I.,

Alfarra, M., Takami, A., Middlebrook, A., Sun, Y., Dzepina, K., Dun-

lea, E., Docherty, K., DeCarlo, P., Salcedo, D., Onasch, T., Jayne,

J.T.and Miyoshi, T., Shimono, A., Hatakeyama, S., Takegawa, N., Kondo,

Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S.and Demerjian,

K., Williams, P., Bower, K., Bahreini, R., Cottrell, L., Griffin, R., Rauti-

ainen, J., Sun, J., Zhang, Y., and Worsnop, D.: Ubiquity and dominance

of oxygenated species in organic aerosols in anthropogenically-influenced

Northern Hemisphere midlatitudes, Geophys. Res. Lett., 34, L13 801, doi:

10.1029/2007GL029979, 2007.

Zhang, X., Zwiers, F., and Stott, P.: Multimodel multisignal climate change

detection at regional scale, J. Clim., 19, 4294–4307, 2006.

Zieger, P., Fierz-Schmidhauser, R., Weingartner, E., and Baltensperger, U.:

Effects of relative humidity on aerosol light scattering: results from different

European sites, Atmos. Chem. Phys, 13, 10 609–10 631, 2013.

Zobrist, B., Weers, U., and Koop, T.: Ice nucleation in aqueous solutions

of poly [ethylene glycol] with different molar mass, J. Chem. Phys., 118,

10 254, doi:/10.1063/1.1571818, 2003.

Zobrist, B., Marcolli, C., Pedernera, D. A., and Koop, T.: Do atmo-

spheric aerosols form glasses?, Atmos. Chem. Phys., 8, 5221–5244, doi:10.

5194/acp-8-5221-2008, URL http://www.atmos-chem-phys.net/8/5221/

2008/, 2008.

Zobrist, B., Soonsin, V., Luo, B., Krieger, U., Marcolli, C., Peter, T., and

Koop, T.: Ultra-slow water diffusion in aqueous sucrose glasses, Phys.

Chem. Chem. Phys., 13, 3514–3526, doi:10.1039/c0cp01273d, 2011.

Zou, M. and Prausnitz, J.: Vapor-liquid and liquid-liquid equilibria in binary

aqueous systems, J. Chem. Eng. Data, 32, 34–37, doi:10.1021/je00047a009,

1987.



232 Bibliography

Zuberi, B.: Microphysics of atmospheric aerosols: phase transitions and cloud

formation mechanisms, Ph.D. thesis, Massachusetts Institute of Technology,

2003.

Zuend, A.: Modelling the Thermodynamics of Mixed Organic-Inorganic

Aerosols to Predict Water Activities and Phase Separations., Phd the-

sis, ETH Zurich, Zurich, Switzerland, doi:10.3929/ethz-a-005582922, URL

http://e-collection.ethbib.ethz.ch/view/eth:30457, 2007.

Zuend, A. and Seinfeld, J. H.: Modeling the gas-particle partitioning of sec-

ondary organic aerosol: the importance of liquid-liquid phase separation,

Atmos. Chem. Phys., 12, 3857–3882, doi:10.5194/acp-12-3857-2012, URL

http://www.atmos-chem-phys.net/12/3857/2012/, 2012.

Zuend, A. and Seinfeld, J. H.: A practical method for the calculation of liquid-

liquid equilibria in multicomponent organic-water-electrolyte systems using

physicochemical constraints, Fluid Phase Equilibria, 337, 201–213, 2013.

Zuend, A., Marcolli, C., Luo, B. P., and Peter, T.: A thermodynamic model

of mixed organic-inorganic aerosols to predict activity coefficients, Atmos.

Chem. Phys., 8, 4559–4593, doi:10.5194/acp-8-4559-2008, URL http://

www.atmos-chem-phys.net/8/4559/2008/, 2008.

Zuend, A., Marcolli, C., Peter, T., and Seinfeld, J. H.: Computation

of liquid-liquid equilibria and phase stabilities: implications for RH-

dependent gas/particle partitioning of organic-inorganic aerosols, Atmos.

Chem. Phys., 10, 7795–7820, doi:10.5194/acp-10-7795-2010, URL http:

//www.atmos-chem-phys.net/10/7795/2010/, 2010.

Zuend, A., Marcolli, C., Booth, A. M., Lienhard, D. M., Soonsin, V.,

Krieger, U. K., Topping, D. O., McFiggans, G., Peter, T., and Seinfeld,

J. H.: New and extended parameterization of the thermodynamic model

AIOMFAC: calculation of activity coefficients for organic-inorganic mix-

tures containing carboxyl, hydroxyl, carbonyl, ether, ester, alkenyl, alkyl,

and aromatic functional groups, Atmos. Chem. Phys., 11, 9155–9206, doi:

10.5194/acp-11-9155-2011, URL http://www.atmos-chem-phys.net/11/

9155/2011/, 2011.

http://e-collection.ethbib.ethz.ch/view/eth:30457








AcknowledgementsThe successful completion of this thesis has been facilitated by the co-

operation and support of many colleagues and friends. I express my sincere

gratitude towards

- Thomas Peter for offering me this opportunity to perform the work presented

here, for the excellent support and for asking challenging scientific questions

which helped me improve this work. I also thank you for being a rescue sup-

port in the couple of adventures that I did during the group retreats.

- Claudia Marcolli for being my primary supervisor, for guiding me through

this thesis, for the scientific discussions, for the understanding and excellent

support.

- David Topping for agreeing to co-examine my Ph.D thesis, and coming to

Zurich for my Ph.D defence.

- Andreas Zuend for guiding me through the model work, for the good dis-

cussions and suggestions, for all the prompt help you provide, and all the fun

in Pasadena. I have learned a lot from you.

- Many thanks to Ulrich Krieger for introducing me to the experimental mea-

surements with EDB and total pressure measurements, for helpful discussions

and suggestions related to the experimental work.

- Uwe Weers and Sandro Tiegermann for repair and maintenance of the ex-

perimental apparatus.

- Best thanks to Eve Choffat and Petra Forney for supporting in the admin-

istrative formalities.

- the entire Institute for Atmospheric and Climate Science (IACETH) and

the Atmospheric Chemistry for a great working atmosphere. Special thanks

to my officemates, Julien Anet and Daniel Lienhard for being my german to

english translators, for all the fun time, and for the birthday cakes and cook-

ies.

- Teachers, supervisors and friends from India, special thanks to Dr. P.Pradeep

Kummar from University of Pune, for motivating me towards my thesis. My

friends Prasad Arlulkar, Roschelle Martis, Vidya Varma, Dhanraj Warjurkar,

Praveen Pandey and Pranjali Potdar for all the scientific and non-scientific

discussions, and for all the time that we spend together.

233

- My friends in Zurich, many thanks to Bhavana Rachuri, Mahesh Rachuri,

Neelam Nirantar, Ajinkya Gaikaiwari, Awanti Gaikaiwari, Arghya Sen,

Sharmila Subramaniam, Sarita Jain, Ashwinkumar Iyer, Ipsita Maharana,

Ambili Nair, Sonali Patil, Vidya Dongre, Fredrick Moses, Ramananda Siri-

gireddy and Arti Kulkarni for your friendship, for sharing my worries, weekend

parties, road trips around Europe, swimming and badminton games, and for

all the care and understanding. Special thanks to Trupti Dubley, Shalaka

Patki and Dr. P.N.Kulkarni for your friendship and motivation all through

these years.

- My family, special thanks to my sisters and my brother for being my strong

support system, and keeping me balanced all these years.

234

Curriculum VitaeGouri Ganbavale

Date of Birth: 14.09.1983

Place of Birth: Kolhapur, India

Citizen: India

EducationNov 2009 - July 2013 PhD student at the Swiss Fedral Institute of

Technology Zurich (ETH Zurich), Institute of

Atmospheric and Climate Science (IACETH),

Atmospheric Chemistry group. Thesis title:

Temperature Dependence of Activity Coeffi-

cients in Organic Aerosols.

July 2004 - July 2008 Student at the Atmospheric and Space Sci-

ences,University of Pune, India.

June 2001 - March 2004 Bachlors of Science, Shivaji University, Kolha-

pur, India.

June 1999 - Feb 2001 Higher Secondary School, Swami Vivekanand

College, Kolhapur, India.

March 1999 Secondary School, Holy Cross Convent high

School, Kolhapur, India.

Work Experience:

- M.Sc. Project Work: Internal Structure of Moon and Moonquakes

- Variability of aerosol optical depth and its relation to drop size effective

radius over pune and surroundings.

- Luminescence dating of rock samples.

- Geomagnetic and paleomagnetic studies of the rock samples.

- Experimental and model studies of temperature dependence of activity co-

efficients in organic mixtures.

International Conferences and Workshops:

- Goldschmidt Conference, 2013, Florence, Italy

- European Aerosol Conference (EAC), 2012, Granada, Spain.

- European Geosciences Union (EGU) General Assembly, 2011, Vienna, Aus-

tria.

- International Conference for Advanced Oxidation Processes, 2010, Kot-

tayam, India.

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