Towards a NanTowards a Nan“process-sp

BerendD t t f Ch iDepartment of Chemica

University of CaLawrence Berkeley

an

CECAM Ly

no Technologyno Technology simulator”d Smitl E i i /Ch i tl Engineering/Chemistrylifornia BerkeleyNational Laboratory

nd

yon France

Out

• Prehistoric nano-mof computational p

• Scientific Case stuSimulatorSimulator

tline

materials: an example process design

udy: The Helios

materiazeolitezeoliteknow zeolite structure

00 000 hypothetical st00,000 hypothetical st

als:esesestructurestructures

Chemistry: methyl shifts and crackin

Product d

dsorption: reactants pDiffusion: reactants ha

ctive siteChemical Reaction: coChemical Reaction: co

teDiffusion: products hav

om the active siteD ti f th dDesorption: of the prod

istribution

have to adsorbave to diffuse to the

onversion at the activonversion at the activ

ve to diffuse away

d tducts

pditi l l l t baditional: molecule too b

ot be a product

on-traditional (every zeo( yInverse shape selectivityDiffusion selectivity (WindoDiffusion selectivity (WindoReactant selectivityPore mouth catalysisPore mouth catalysis….

ery limited knowledge ond i i thermodynamics in the ze

ybi t f i thbig to form in the pores w

lite its own mechanism))

ow effect)ow effect)

n diffusion and litolite

Forms of Shape Selectivity

ited atom modelAlkane-zeo

ited atom modelAlkane-alkane:

Fi d b d l thFixed bond lengthBond-bendingT i

CH

TorsionNon-bonded: Lennard-Jones

Alk litAlkane-zeoliteRigid zeolite structureLennard-Jones interactions

Parameters:Alkane-alkane:

• fitted to vapor-liquid equilibriaAlkane-zeolite:

• fitted to heat of adsorption an(at zero loading)

olite model

CH2CH2

H3

CH2CH2

a Nature 1993, 3

nd Henry coefficients

Methane in MMFI (silicalite)

tants Reacti

interme

produion pediates

(single coi

tantsReactio

tantsintermed

Formation of reaction in– probability to find a

free energy of form– free energy of form“Fate” of a reaction inter

– Leave the zeolite → proC ti t t– Continue to react:

• (hydro)cracking if cracking p

mponent)

produon

produdiates

termediates:reaction intermediate: ation in the zeoliteation in the zeolitermediate:oduct

precursor

contribution of the energ

zeolite to the free igies

T 415 K

C10 hydrocoprimary product slat

0MFI

0

0

0

0

0

C3

C4

C5

C6

C7

(Black = branch1] Weitkamp et.al., Appl. Catal. 1983, 8, p 12

nversion, te : MFI vs. MEL

50

MEL

10

40

C

30

/ 100

mol

C

20

mol

/10

0C

3C

4C

5C

6C

hed, white = linear)23

Brønsted-Evans-P

phasephase

e a zeolite

Polanyi- principle

MFI MELMFI MEL

Reactio

4Me-C9

2,4diMe

-C10

5Me-C994,4diMe

on pathsp

MELi-C4 + n-C6

e-C8

i-C7 + C3

e-C8

MFIi-C6 + n-C4

MEL MFI

MEL MFI

screening b

Hydrodewaxing: remoHydrodewaxing: remohydrocarbons but leaunharmed

Screen zeolites with tScreen zeolites with tdifference between n-

by computer

oving the largeroving the larger ving the shorter

the largest free energthe largest free energ-C25 and n-C10;25 10

US P t t li ti (Ch

Hel• Aim: to demonstrat

l fl lyears a solar fluel abundant and scalprocesses, that haffi i f > 1%efficiency of > 1%

yields a chemicallyy yenergy density at lof methanolof methanol

lios te that within ten

t th tgenerator that uses lable manufacturing gs an overall power f li ht dfrom sunlight, and

y pure fuel having an y p geast as large as that

Nanotechnolo

• Nano-scalNano-scalphotovoltaelectroche

Almost bAlmost b

ogy in Helios

lablelable aic and emical system

blackblack

O2•n•p

M1

•p•n

M1

hh•m

PVhhe- M

M2 •p•w

H2•c

2

PV (Photo voltaic system)system)

nanorod and tubepolymer hybridpolymer hybridnanodot/nanotube

l l PVmolecular PV

M1 2 (Catalytic centersproton reduction to hydrowater oxidationcarbon dioxide reduction

Nano-technol• Develop nano-tech

have to be integrathave to be integratdevice

• Key questions:

- how do material caffect the overall

- how to optimize t

• D l H li• Develop a Helios paddress these que

ogy in Helioshnology “units” that ted into a workingted into a working

choices for the units activity?

the design?

i l t tprocess simulator to stions

O2

M1M1

hh PVhhe-

M2

H22

Cat 1Cat 1

PVPV

Cat 2Cat 2

Reactor/separator

Chem. Eng: procReactor/separator– Heat and mass balances

Reaction kinetics thermodynam– Reaction kinetics, thermodynam– Models for the types of reactorsM d lModels:– Empirical models that interpolat– Predictive models for missing e

correlations D i f h i l l tDesign of a chemical plantOptimization: – changes of feedstock, – changes of product specificatiog p pRecent development: use mompute the essential mate

cess simulation

tmics reactormicss/equipment

te experimental dataexperimental data; based on

nsmolecular simulations to

rials properties “in-silica”

Helios SiIntegral theoret

ptimize process conditionsnalyze interactions of the vata base for all experimentata base for all experimentonnect to economic forecacientific “de-bottlenecking”Where should improvements be madeWhere should improvements be madeWhich crucial knowledge is lacking?What if questions?What if questions?Connection to molecular understandin

imulator:tical description

svarious componentstal datatal datasting

e?e?

ng

Towards a “He

Reverse coarse graig.Start with an “engineeri

a. Correlation of experimentb. Empirical modelsc. Analyze the importance o

model

.Replace these by moreIntroducing molecular (.Introducing molecular (

elios Simulator”

ining approach:g pping model” al data

f the various components of the

e “science” based modelpredictive) toolspredictive) tools

Key factors

Excellent cyber-infrastru–Databases; all experimen–Software development; thSoftware development; th

• electronic structure cal• quantum chemistry• classical simulations

–computational resourcesC ll b i f llCollaboration of all grouBypassing knowledge gaBypassing knowledge ga–not all computational will

th i

for success

ucture:ntal data need to be storedhe Helios simulator requirehe Helios simulator requirelculations

i l dps involvedaps:aps: work at the same time at

… and if th

elios is a prototype op ypactory; many others w

fIt is therefore timely to hto build simulators of thematerials.

hings fail…

of a chemical nano-will follow. have the expertise how ese new class of

asic Research Neeatal sis for Energatalysis for Energ

eds: y

Priority resea

Ad d t l t fAdvanced catalysts fheavy fossil energy feheavy fossil energy feUnderstanding the chignocellulosic biomaand conversion to fueand conversion to fuePhoto- and electro-drPhoto and electro drcarbon dioxide and w

ss-cutting research: advanc

rch directions

f th i ffor the conversion of eedstockseedstockshemistry of ss deconstruction elsels riven conversions ofriven conversions of

water

ces in theory and computat

Some obscomputationcomputation

desdesAn airplane is designthere are only a few cthere are only a few cmaterial is designed Experimental data on

t i l timaterial properties wbottleneck in the desbottleneck in the desSuccessful materials–careful analyses of the

servations: nal materialsnal materials signs gned by computer, butcases in which acases in which a by computer n basic data of ill bwill become a

signsigns designge key molecular property

GoGo

1. Identify expectedy pkey scientific domains

2 Identify expected2. Identify expectedutilizing those systemsthree OASCR facilitiesmonthsmonths.3. Identify the impact

alsals

d science outcomes in

d science outcomesd science outcomes to be deployed at the s in the next 12-18

of having all scientists

ystem catalysis

Step 1: Correlations oa) Data base for all experib) Empirical correlations tob) Empirical correlations toc) Empirical models to des

systemssystems® Response of a unit dep

conditionsconditionsEmpirical description

a) Archiving experimental b) Understanding of the efb) Understanding of the ef

various components on

“reverse multi-sc

of the experimental datamental datao interpolate the datao interpolate the datascribe the operation of the

ending on the operational

of a device:dataffects of changes of theffects of changes of the overall performance

Step 2: Replacing “co“knowledge”knowledge

• Use expertise of the thi i l l tiempirical correlations

descriptionM k li bl d• => Make reliable pred

window

Step 3: Replacing unkp p gproperties by atomic/mcomputationscomputations

• Use modern simulatiomaterials propertiesmaterials properties.

orrelations” by

heory groups in Helios to replaceb “ ” th ti lby a “proper” theoretical

i ti t id th i t lictions outside the experimental

known materials molecular based

n techniques to estimate essenti

Relation to pres

Theory in Helios is aimespecific components of Haimed to “integrate” the Synergies:Synergies:–Step 1:

I t lli t d t b f ll• Intelligent data base for all e• Allows to address issues of

components that are not incomponents that are not in –Step 2: state of the art th

used directlyused directly–Step 3: novel materials c

ffi iefficiency

ent Helios work

ed to “de-bottleneck” Helios: the simulator is components

i t l d texperimental dataf integration of individual the same state of developmentthe same state of development

heoretical insights can be

can be screened for their

convert H2O and t f l (h dnto fuels (hydrogen,

on monoxide, formic ,methanol) CO

CO+CO++

Catalysis

O2O2 e- e-e e

e-

H+e-

+HH+

+H2

+ H2O

Almost blaAlmost bla

M1M1

NanPV Nanintegrat

M2

gan

PVPV system

ackack Metal catalys•proton reducproton reduc•water oxidat•CO2 reductio

noscale photovoltaics:noscale photovoltaics:tion with electrochemind catalytic aspects

t l icatalysis

Chem Eng:Chem. Eng:Reactor/separatorHeat and mass balanceHeat and mass balanceReaction kinetics, thermModels for the types of yModels:Empirical models that inP di ti d l fPredictive models for mbased on correlations Design of a chemical plDesign of a chemical plOptimization: changes of feedstock, g ,changes of product speRecent development: uscompute the essential mprocess desing”

: process simulation: process simulation

esesmodynamicsreactors/equipment

nterpolate experimental datai i i t l d tmissing experimental data;

antant

cificationsse molecular simulations to

materials properties “in-silico”

Alkane-zeoited atom modelited atom modelkane-alkane:Fi d b d l thFixed bond lengthBond-bendingT i

CH

TorsionNon-bonded: Lennard-Jonesk litkane-zeoliteRigid zeolite structureLennard-Jones interactionsarameters:Alkane-alkane:

– fitted to vapor-liquid equilibriaAlkane-zeolite:

– fitted to heat of adsorption an(at zero loading)

olite model

CH2CH2

H3

CH2CH2

a Nature 1993, 3

nd Henry coefficients