Applications of Chemometrics to Building Mortar Formulations, a Case Study: Cementitious Tile Adhesives

Ezio Leone

Chemistry

Supervisors: Prof. Enrico Marcantoni, Prof. Maria Matilde Duarte Marques

Chairperson: Prof. Isabel M. Marrucho Supervisor: Prof. Enrico Marcantoni

Members of the Committee: Prof. Joao Lopes

October 2018

ii

iii

Contents Resumo ....................................................................................................................................... 9 Abstract .................................................................................................................................... 10 I. THEORY .......................................................................................................................... 11

Chemometrics ....................................................................................................................... 11 Introduction ...................................................................................................................... 11 A Brief History ................................................................................................................. 12 Multivariate Analysis ....................................................................................................... 13 Design of Experiment ....................................................................................................... 13

Factorial Design ........................................................................................................... 15 Principal Component Analysis ......................................................................................... 20

Derivation of PCs ......................................................................................................... 20 Geometrical Representation ......................................................................................... 20

Cementitious Adhesives ....................................................................................................... 25 Introduction to Adhesives ................................................................................................ 25

Mechanism of Adhesion ............................................................................................... 25 Mortars in History ........................................................................................................ 30

Dry-Mix Mortar Formulations ......................................................................................... 31 Uses of Mortars ................................................................................................................ 32 Cementitious Tile Adhesives ........................................................................................... 33

Standards and Classifications ....................................................................................... 34 Adhesive Strength Measurement According to EN 12004 .......................................... 40 Deformability According to EN 12004 ........................................................................ 41 Slip Resistance ............................................................................................................. 42 Adjustability ................................................................................................................. 42

iv

Ingredients ........................................................................................................................ 43 Binder ........................................................................................................................... 43 Cellulose Ether and Modifiers ...................................................................................... 49 Aggregates .................................................................................................................... 52 Redispersible Polymer .................................................................................................. 52 Fibers ............................................................................................................................ 54

Rheology .............................................................................................................................. 56 Importance of Rheology ................................................................................................... 56 Introduction to Rheology ................................................................................................. 56 Rheological Models .......................................................................................................... 58

Viscosity ....................................................................................................................... 58 Viscoelasticity .............................................................................................................. 59

Continuous Tests .............................................................................................................. 60 Shear Stress .................................................................................................................. 60 Shear Rate .................................................................................................................... 61 Yield Stress .................................................................................................................. 62 The Bingham Model ..................................................................................................... 62 Flow Test ...................................................................................................................... 63

Oscillatory Stress Sweep Tests ........................................................................................ 64 Creep Tests ....................................................................................................................... 64 Rheological Measurements .............................................................................................. 65 Dynamic Mechanical Analysis ......................................................................................... 66

II. EXPERIMENTAL PART ................................................................................................ 67 Materials ............................................................................................................................... 67

Tile Adhesives .................................................................................................................. 67 Concrete Slabs .................................................................................................................. 67 Ceramic Tiles ................................................................................................................... 67

v

Smooth Body Tiles ....................................................................................................... 67 Porous Body Tile .......................................................................................................... 68

Software ............................................................................................................................... 69 Instruments ........................................................................................................................... 70 Experimental Designs .......................................................................................................... 73

Tile Adhesive Model ........................................................................................................ 73 First Model ................................................................................................................... 73 Second Model ............................................................................................................... 75

Dustiness Model ............................................................................................................... 80 Procedures ............................................................................................................................ 82

Mixing Procedure ............................................................................................................. 82 Evaluation of Adhesion .................................................................................................... 82

Measurement of Tensile Adhesion Strength ................................................................ 85 Evaluation of Fresh Properties ......................................................................................... 85 Evaluation of Deformability ............................................................................................. 86 Evaluation of Dustiness .................................................................................................... 86 Evaluation of Rheological Properties ............................................................................... 87

Mixing Procedure ......................................................................................................... 87 Sample Loading ............................................................................................................ 87 Test Procedure .............................................................................................................. 88

Evaluation of Dynamic Mechanical Properties ................................................................ 89 III. RESULTS AND DISCUSSIONS ................................................................................ 90

Principal Component Analysis ............................................................................................. 90 Correlations in Tile Adhesives Testing ............................................................................ 90 Rheological Tests Correlations ........................................................................................ 93

General Overview ........................................................................................................ 93 Principal Component Analysis ................................................................................... 100

vi

Design of Experiment Results ............................................................................................ 103 Water Mix Ratio ............................................................................................................. 103

RSC and Discussion ................................................................................................... 105 Specific Gravity .............................................................................................................. 106

RSC and Discussion ................................................................................................... 107 EN Slip ........................................................................................................................... 109

RSC and Discussion ................................................................................................... 110 Mapei Slip ...................................................................................................................... 111

RSC and Discussion ................................................................................................... 112 Creep Tests ..................................................................................................................... 113

At 200 Pa .................................................................................................................... 113 At 300 Pa .................................................................................................................... 114 RSC and Discussion ................................................................................................... 115

Adjustability ................................................................................................................... 117 RSC and Discussion ................................................................................................... 118

24 h ................................................................................................................................. 120 RSC and Discussion ................................................................................................... 121

Open Time at 5’ .............................................................................................................. 123 RSC and Discussion ................................................................................................... 124

Open Time at 20’ ............................................................................................................ 125 RSC and Discussion ................................................................................................... 126

Open Time at 30’ ............................................................................................................ 127 RSC and Discussion ................................................................................................... 128

Initial Adhesion .............................................................................................................. 129 RSC and Discussion ................................................................................................... 130

Water Immersion ............................................................................................................ 132 At 5’ ............................................................................................................................ 132

vii

At 10’ .......................................................................................................................... 133 At 15’ .......................................................................................................................... 134 RSC and Discussion ................................................................................................... 136

Heat Aging at 5’ ............................................................................................................. 138 RSC and Discussion ................................................................................................... 139

Heat Aging at 10’ ........................................................................................................... 140 RSC and Discussion ................................................................................................... 141

Heat Aging at 15’ ........................................................................................................... 142 RSC and Discussion ................................................................................................... 143

Freeze/Thaw Cycles ....................................................................................................... 145 RSC and Discussion ................................................................................................... 146

Deformability ................................................................................................................. 147 RSC and Discussion ................................................................................................... 148

Deformability Load ........................................................................................................ 149 RSC and Discussion ................................................................................................... 150

DMA ΔE’ (5-50 °C) ....................................................................................................... 151 RSC and Discussion ................................................................................................... 152

Stress Sweep: G’ and G” at 200 and 300 Pa .................................................................. 154 G’ at 200 Pa ................................................................................................................ 154 G’ at 300 Pa ................................................................................................................ 155 G” at 200 Pa ............................................................................................................... 156 G” at 300 Pa ............................................................................................................... 157 RSC Discussion .......................................................................................................... 159

Stress Sweep: G’ and G” at Plateau ............................................................................... 162 G’ at Plateau ............................................................................................................... 162 G” at Plateau .............................................................................................................. 163 RSC and Discussion ................................................................................................... 164

viii

Stress Sweep: G’ ½ ........................................................................................................ 166 RSC and Discussion ................................................................................................... 167

Yield ............................................................................................................................... 168 Yield TA ..................................................................................................................... 168 Yield AP ..................................................................................................................... 169 RSC and Discussion ................................................................................................... 170

Dustiness ........................................................................................................................ 171 E Min .......................................................................................................................... 171 E Max ......................................................................................................................... 172 RSC and Discussion ................................................................................................... 172

IV. CONCLUSIONS ........................................................................................................ 175 References .............................................................................................................................. 176

9

Resumo Realizei meu projeto de tese no laboratório de P&D de adesivos de cimento da Mapei SpA, empresa líder no campo de adesivos, selantes e produtos químicos para construção. O objetivo do trabalho foi aplicar ferramentas quimiométricas para o estudo do comportamento mecânico e reológico de adesivos à base de cimento em relação à sua formulação. Em particular, dois Designs Experimentais foram definidos : um fatorial fracionário com de-sign composto central e outro idêntico, mas com a adição de quatro pontos de verificação. Foram considerando cinco ingredientes diferentes normalmente usados em formulações adesi-vas para azulejos. Todas as formulações derivadas do modelo foram testadas através do pull-off de tração de ladrilhos, de acordo com a ISO 13007, a fim de estudar seus desempenhos mecânicos. Propriedades frescas das mesmas formulações também foram estudadas através de medição reológica utilizando reómetro rotacional. Além disso, usei a Análise de Componentes Princi-pais para identificar possíveis correlações entre propriedades mecânicas e reológicas. Um ter-ceiro design experimental foi projetado a fim de estudar a geração de poeira das misturas secas. Este trabalho tem como confirma que a quimiometria tem a possibilidade de melhorar signifi-cativamente a informação gerada por experiências através de uma abordagem de análise mul-tivariada. Esta disciplina, aplicada à ciência do produto à base de cimento, demonstra também sua extrema flexibilidade e adaptabilidade a qualquer tipo de experiências e sua perfeita aplicabilidade na ciência da formulação se for configurada corretamente, superando clara-mente a abordagem clássica univariada.

10

Abstract I carried out my thesis project at the R&D laboratory of cementitious adhesives of Mapei S.p.A, leading company in the field of adhesives, sealants and chemical products for building. The aim of the work was to apply chemometric tools to the study of mechanical and rheological behavior of cement-based adhesives in relation to their formulation. In particular, two Design of Experiment were defined: a fractional factorial with central com-posite design and another identical but with the addition of four verification points. DoE were defined considering five different ingredients normally used in tile adhesive formulations. All the formulations derived from the model were tested through tile tensile pull-off, according to ISO 13007, in order to study their mechanical performances. Fresh property of the same formulations were also studied through rheological measurement using rotational rheometer. Furthermore, I used Principal Component Analysis in order to iden-tify possible correlations among mechanical and rheological properties. Furthermore, a third DoE was also designed in order to study the dustiness generation of dry blends. This work has the goal to confirm that chemometrics has the possibility to strongly enhance and improve the information generated by experimentations through the approach of multivar-iate analysis. This discipline, applied to cement based product science, also wants to demon-strate its extreme flexibility and adaptability to any kind of experimentation and its perfect ap-plicability in formulation science if it is setup correctly, clearly surpassing the classical univari-ate approach.

11

creativity intelligence

data design

I. THEORY

Chemometrics Introduction

Chemometrics has been defined as “A chemical discipline that uses statistical and mathematical methods, to design or select optimum procedures and experiments, and to provide maximum chemical information by analyzing chemical data.” In shorter words, it is focused as “Chemo-metrics concerns the extraction of relevant information from chemical data by mathematical and statistical tools.”[1] (Figure 1).

Figure 1. Chemometrics rule in the knowledge circle

Chemometrics is inherently interdisciplinary, using frequently employed in core data-analytic disciplines such as multivariate statistics, applied mathematics, and computer science, in order to address problems in chemistry, biochemistry, medicine, biology and chemical engineering. In this way, it mirrors other interdisciplinary fields, such as psychometrics and econometrics.

Knowledge

Hypotesis

Experiments

Information

12

Many in the field of analytical chemistry have found it difficult to apply chemometrics to their work. The mathematics can be intimidating, and many of the techniques use abstract vector spaces, which can seem counterintuitive. This has created a “barrier to entry” which has hin-dered a more rapid and general adoption of chemometric techniques[2]. Despite the broad definition of chemometrics, the most important part of it is still the applica-tion of multivariate data analysis to chemistry-relevant data. Chemical-physical systems of practical interest are often complicated and relationships between available (measurement) data and desired data (properties, origin) cannot be described by theory. Therefore, a typical chemo-metric approach is not based on "first principles" but is "data driven" and has the goal to create empirical models. A thorough evaluation of the performance of such models is essential for new cases. Multivariate statistical data analysis has been proven as a powerful tool for analyzing and structuring such data sets from chemistry and biochemistry[3].

A Brief History The start of chemometrics dates back to the 1960s, when multivariate data analysis methods - like for instance the "learning machine" - have been tried for solving rather complicated prob-lems in chemistry, such as the automatic interpretation of molecular spectra. The name chemo-metrics was first used by Svante Wold in 1972 (in Swedish, “kemometria”) and it was estab-lished in 1974 by Bruce Kowalski. The first years of chemometrics were characterized by rather uncritical use of machine learning methods for complex - often too complex - tasks in chemistry and consequently sometimes accompanied by ignorance and refusal of many chemists. However, in this time also falls the presentation of the Partial Least Square regression method by chemometricians, which is now the most used method for evaluation of multivariate data, not only in chemistry. During the next decades chemometricians learned to use multivariate data analysis in a proper and safe way for problems with a realistic chance for success, and also found back to the underlying statistical concepts. Chemometrics contributed with valuable method developments and provided many stimulants in the area. Furthermore, commercial software became available and nowadays sev-eral basic chemometric methods, like principal component analysis, multivariate classification, and multiple regression (by PLS and other approaches) are routinely used in chemical research and industry. Admittedly, sometimes without the necessary elementary knowledge about the used methods[3].

13

Multivariate Analysis The use of chemometrics also implies the use of multivariate data analysis, in which several related samples are analyzed simultaneously. A multivariate approach when handling and ex-ploring complex chemical data and designing experiments is certainly part of the foundation of chemometrics. Multivariate data analysis as opposed to using only one or a few variables in the data analysis is based on the fact that complex problems - by nature - need multiple variables to be described. Thus, by using and combining more variables, more information about the chemical system can be retrieved. In standard multivariate data analysis, data are arranged in a two-way structure, a table or a matrix. An example is a matrix in which each row corresponds to a sample and each column to a variable describing the complex system. This is the typical input for multivariate techniques: when these matrices are analyzed by means of chemometrics, all the variables are considered at the same time and consequently the extracted information represent a global overview of the system. Since chemometrics proved to be able to handle large amounts of data and to extract useful information, it has been successfully applied in different fields. During the last years, it has so increased in uses and applications that now modern analytical techniques are usually combined with chemometric methods.

Design of Experiment To explain satisfactorily this technique is better to introduce first what an experiment is. The term experiment is defined as the systematic procedure carried out under controlled conditions in order to discover an unknown effect, to test or establish a hypothesis, or to illustrate a known effect. (Figure 2)[4]. Experimental Design allows to optimize the amount of available resources while carrying out an experimental plan, having the possibility to predict the accuracy of the final model results before starting any battery of tests, with a determination of the leverage level (model accuracy) in every single point of the investigated space[5].

14

Figure 2. Process factors and responses

Experimental design can be used to: reduce design costs by minimizing the number of experiments; speeding up the design process; reducing late engineering design changes; reducing product material and labor complexity.

Designed Experiments are also powerful tools to achieve manufacturing cost savings by mini-mizing process variation and reducing rework, scrap, and the need for inspection (Figure 3). Components of Design of Experiment (DoE):

Factors (inputs): Include controllable and uncontrollable variables. The former re-fers to factors that we can control (e.g., a certain ingredient in dry blend formula-tions). The latter refers to factors we cannot control. Human beings are generally considered a noise factor, which is an uncontrollable factor that causes variability under normal operating conditions; yet we can control these factors during the ex-periment using blocking and randomization.

Levels (settings of each factor): One example would be the particular level of dosage of an ingredient.

Response (output): Consider testing a new tile adhesive. The output could be its me-chanical resistance against tensile test or its resistance against vertical slip, its vis-cosity, etc… Experiments should avoid optimizing the process for one response at the expense of another, and important outcomes are measured and analyzed to deter-mine the factors and their settings in a way that will provide the best overall outcome.

Controllable input factors

Uncontrollable input factors

Process Responses

15

Figure 3. DoE flowchart.

Factorial Design

Experimenters are often concerned with how changes of certain factors affect a process. If the experimenter has some control over the settings of these factors, then experimentation through changing these settings can aid in understanding the process. By using settings of the factors at a fixed number of values, referred to as levels, a factorial design is typically used as a plan to conduct such an experiment. Instead of focusing on one factor at a time, a factorial experiment varies the levels of these factors simultaneously. Depending on the different combinations of factors used in the experiment, not only can the experimenter study how the factors impact the response individually, but also their interaction.

Define problems

Objectives

Brainstorming

Design of Experi-ment

Conduct experiment, collect data

Analyse data

Brainstorming

Verify predicted results

16

Full Factorial Design A full factorial design is a basic design that carry out all possible experiments with two-level factors:

1 level =maximum level -1 level=minimum level

If there are k factors, each at two levels, a full factorial design has 2k experiments. This method allows to generate a linear model with interactions, for each variable. For a three factors full-factorial design:

= + ∙ 1 + ∙ 2 + ∙ 3 + ∙ 1 2 + ∙ 1 3 + ∙ 2 3 (1.1)

All experiments at the boundaries of the design space are planned, as illustrated for three factors in Figure 4. The corresponding experimental matrix with its encoding system is shown in Table 1.

Fractional Factorial Design Fractional factorial designs are experimental designs consisting of a carefully chosen subset (fraction) of the experimental runs of a full factorial design[6]. Fractional factorial designs are a good choice when the number of experiments has to be lowered, the resources are limited or the number of factors in the design is large. The alias principle allows selection of which ex-periments from the full factorial design must be run without losing significant information. The idea is to choose the experiments which lead to confound important effects, such as main and first-degree interaction effects, with smaller and less interpretable effects, such as second (and more) degree interaction effects. For example, with three factors, shows the alias generator: = , , (1.2)

I = 123 is called alias generator or design generator, a generating relation for this 23-1 design (the dark-shaded corners of Figure 4b). Since there is only one design generator for this design, it is also the defining relation for the design. Equally, I = -123 is the design generator (and defining relation) for the light-shaded corners of Figure 4b. We call I = 123 the defining relation for the 23-1 design because with it we can generate (by “multiplication”) the complete confound-ing pattern for the design. That is, given I=123, we can generate the set of {1=23, 2=13, 3=12,

17

I=123}, which is the complete set of aliases, as they are called, for this 23-1 fractional factorial design. With I = 123, we can easily generate all the columns of the half-fraction design 23-1. With three factors, only one alias generator is allowed, dividing the number of experiments by two for similar model accuracy.

Figure 4. (a) Full Factorial Design; (b) Fractional Factorial Design

Central Composite Design

The single order designs shown before can represent and predict only linear responses, which, in most cases, do not represent the reality. In these cases, it is indispensable a second-order design in order to generate curve responses in mathematical modeling. A second-order model contains all the terms in the first-order model, plus all quadratic terms and all cross product terms. Specifically, it takes the form:

yij = βk xki +

q

k=1βkk xki2 +

q

k=1βkl xki xli + εij

q

l=k+1

q-1

k=1 ,

yij = β0+xi' β +xi' β xi +εij. (1.3)

+1

+1

+1

-1 -1

-1

X1

X2

X3 +1

+1

+1

-1 -1

-1

X1

X2

X3

a b

18

Where xi' = ( , , … , )′, β = (β1, β2, … , βq)′, and β is a × matrix with β = βkk and β = β = βkl/2 for < . Note that the model only includes the cross product for < ; the matrix form with β includes both and , so the coefficients are halved to take this into account. Second-order models describes quadratic surfaces, and quadratic surfaces can take several shapes. Figure 5 shows four of the shapes that a quadratic surface can take. First, we have a simple minimum and maximum. Then we have a ridge; the surface is curved (here a maximum) in one direction, but is fairy constant in another direction. Finally, we see a saddle point; the surface curves up in one direction and curves down in another[7].

Figure 5. Sample second-order surfaces: (a) minimum, (b) maximum, (c) ridge, and (d) saddle.

(a)

(d) (c)

(b)

X1 X1

X1 X1

X2 X2

X2 X2

Y1 Y2

Y3 Y4

19

There are several choices for second-order designs. One of the most popular is the central com-posite design (CCD). A CDD is composed of factorial points, axial points and center points. Summarizing, for a CDD there are three levels:

1 level= Maximum level 0 level= Average level -1 level=Minimum level

The resulting experimental plan, described subsequently in details, is a rational combination of these levels for each variables, which provides a full inspection of the multi-dimensional space determinate by the total number of components investigated. For a 3 factors central composite design:

Response = a + b ∙ V1 + c ∙ V2 + d ∙ V3 + e ∙ V1 V2 + f ∙ V1 V3 + g ∙ V2 V3 + h ∙ V1 + i ∙ V2 + j ∙ V3 (1.4)

Constant and linear terms: pla-nar response-surface curve

Interaction terms: distorted re-sponse-surface curve

Quadratic terms: curve response

Table 1. Example of a 3 factors Full Factorial design with central composite addition.

Runs X1 X2 X3 Notes 1 -1 -1 -1 Full Factorial 2 -1 -1 1 Full Factorial 3 -1 1 -1 Full Factorial 4 -1 1 1 Full Factorial 5 1 -1 -1 Full Factorial 6 1 -1 1 Full Factorial 7 1 1 -1 Full Factorial 8 1 1 1 Full Factorial 9 0 0 0 Full Factorial

10 0 0 0 Central point 11 0 0 0 Central point 12 -1 0 0 Central point 13 1 0 0 Central Composite 14 0 -1 0 Central Composite 15 0 1 0 Central Composite 16 0 0 -1 Central Composite 17 0 0 1 Central Composite

20

Principal Component Analysis Principal component analysis (PCA) is probably the oldest and best known of the techniques of multivariate analysis. It was first introduced by Pearson (1901), and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now well entrenched in virtually every statistical computer package[8]. The central idea of PCA is to reduce the dimensionality of a data set consisting of a large number of interrelated variables, while retaining as much as possible of the variation pre-sent in the data set. This is achieved by using an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated varia-bles called principal components (PCs). The resulting vectors are an uncorrelated orthogonal basis set.

Derivation of PCs PCA can be thought of as fitting an n-dimensional ellipsoid to the data, where each axis of the ellipsoid represents a principal component. If some axis of the ellipsoid is small, then the vari-ance along that axis is also small, and by omitting that axis and its corresponding principal component from our representation of the dataset, we lose only a commensurately small amount of information. To find the axes of the ellipsoid, we must first subtract the mean of each variable from the dataset to center the data around the origin. Then, we compute the covariance matrix of the data, and calculate the eigenvalues and corresponding eigenvectors of this covariance matrix. Then we must normalize each of the orthogonal eigenvectors to become unit vectors. Once this is done, each of the mutually orthogonal, unit eigenvectors can be interpreted as an axis of the ellipsoid fitted to the data. This choice of basis will transform our covariance matrix into a diagonalised form with the diagonal elements representing the variance of each axis. The pro-portion of the variance that each eigenvector represents can be calculated by dividing the ei-genvalue corresponding to that eigenvector by the sum of all eigenvalues.

Geometrical Representation In order to understand PCA geometrically, let us consider a two dimensional data set I × J, where I is the number of samples and J is the number of variables. In the present case, for convenience, we have set the number of variables to two: J1 and J2. As shown in Figure 6, these

21

samples can be presented in a two dimensional space spanned by J1 and J2. The two axes J1 and J2 are orthogonal to each other. The data set acquired for the samples have considerable amount of variation along J1 and J2 axes. In other words, both the dimensions are significantly important to have the complete information about the sample set. A counterclockwise rotation of the J1 and J2 axes by an angle θ (45° in the present case) gener-ates another pair of orthogonal axes T1 and T2. Mathematically, it could be shown using (1.5): TT = cosθ sinθ −sinθ cosθ

JJ

The new variables (or dimensions) T1 and T2 are the linear combinations of J1 and J2 variables with sine and cosine as coefficients T = J cosθ + J sinθ (1.5) T = −J sinθ + J cosθ (1.6)

Projection of the data set in space, spanned by the new variables T1 and T2 is shown in Figure 7. The data set has most the variations along T1 axis and is literally invariant along T2 axis.

Figure 6. Representation of a data set in the space spanned by J1 and J2. Data has significant variation

along the axes.

22

Figure 7. (a) Rotation of axes J1 and J2 by 45° to generate another pair of orthogonal axes T1 and T2. (b) Representation of data set in the new space spanned by T1 and T2. The data set has variations along T1 and T2. (c) Reducion of dimesnions. T2 is unimportant and hence could be removed, and T1 can be

taken as the approximation of data spanned in the two dimensional space spanned by J1 and J2. In principle, variation along T1 axis can be taken as a good approximation of the two-dimen-sional data set, and one can easily ignore the T1 and T2 axis. Thus, by projecting the data set in a suitable space, it is possible to reduce the dimensions of the data set while retaining all the information.

Loading and Score Plots The results of a PCA are usually displayed in terms of component scores (the transformed variable values corresponding to a particular data point), and loadings (the weight by which each standardized original variable should be multiplied to get the component score) (Figure 8). In principle, loading plot represents the correlations among the investigated variables: close labels indicate strongly correlated variables; opposed loadings in reference to the graph origin (in correspondence of [0; 0] coordinates) indicate negative correlations; perpendicular variables

23

are not correlated to each other. Loadings which have little contribution to both components are arranged at the origin. On the other hand, score plot indicates the position of each single experiment in the same ref-erence graph of loadings, indicating that tests located close to certain variables position have higher values of these same variables and, with the same logic, experiments opposed to certain variables will have lower values of these variables. Points close the average appear at the origin of the score plot.

Figure 8. Loading and score plots.

Side-by-side, these two plots took as example, very helpfully characterize all the observations in the data set. In the loading plot we can assume that the variables arranged at the top (V6, V7, V8) are correlated because they are close to each other but they are not correlated with the group variables on the left (V2, V3, V4, V5) and on the right (V1, V9, V10, V11, V12) because the groups are perpendicular to each other. On the other hand, these last two group of variables are inversely correlated. In the score plot, we can easily figure out a cluster of scores whose values do not differ greatly from each other. Sample 11 as sample 3 are very distant from the cluster and this indicates that the value of the score regarding the variable to which it refers is much higher than the other scores.

-0.4 -0.2 0.0 0.2 0.4

-0.4-0.2

0.00.2

0.4

Loading Plot (68.4% of total variance)

Component 1 (45.5% of variance)

Comp

onent 2

(22.9%

of var

iance)

V1

V2V3 V4V5

V6V7

V8

V9V10

V11V12

V13V14

+

-8 -6 -4 -2 0 2 4

-6-4

-20

24

6

Score Plot (68.4% of total variance)

Component 1 (45.5% of variance)

Comp

onent 2

(22.9%

of var

iance)

12

3

4

5 67 8

9

1011

12

13

14

15

16

17

18

1920

2122

23 24

2526 2728

2930 31

3233+

24

It is no coincidence that we can mentally superimpose these two plots and come to exactly the same conclusions, using only the plots (Figure 8). This result comes from the fact that the scores (right) are just a linear combination of the raw data, with weighting given by the loadings (left). With this powerful mathematical instrument, it is possible to identify clearly any kind of corre-lation among different tests. Furthermore, combining these information with experiments ob-tained from a rational Experimental Design, it will be possible to correlate formulation variables (input values in Design) to test results (output variables, elaborated with Principal Components Analysis).

25

Cementitious Adhesives Introduction to Adhesives

Adhesive, also known as glue, cement, mucilage, or paste, is any substance applied to one sur-face, or both surfaces, of two separate items that binds them together and resists their separa-tion[9]. Adhesives may be found naturally or produced synthetically. The earliest human use of adhesive-like substances was approximately 200,000 years ago[10]. Adhesives are typically or-ganized by the method of adhesion. These are then organized into reactive and non-reactive adhesives, which refers to whether the adhesive chemically reacts in order to harden. Alterna-tively they can be organized by whether the raw stock is of natural or synthetic origin, or by their starting physical phase. Based on their chemical composition and mechanical properties, the first important classification of gluing materials for construction, is as follows:

cementitious adhesives; dispersion adhesives or ready to use; organic adhesives;

this work will be focused on the cementitious adhesives category.

Mechanism of Adhesion The mechanism of adhesion has been studied for years. In order to provide an explanation for adhesion phenomena, several theories have been proposed. Because there is no unifying theory that describes all adhesive bonds in a comprehensive manner, the categorization of adhesion mechanisms often overlaps. The bonding of an adhesive to a substrate includes numerous me-chanical, physical, and chemical forces that influence each other. As it is impossible to separate these forces from each other, it can be divided into five different adhesion mechanisms, includ-ing mechanical, electrostatic, adsorption, chemisorptions and diffusion theory[11].

Physical Absorption The adhesion results from the molecular contact between two materials and these two materi-als are held together by the van der Waals forces (Figure 9). These are weakest forces that contribute to the adhesive bonding, but are quite sufficient to make strong joints[12].

26

Figure 9. Surface forces in physical absorption.

Chemical Bonding The chemical bonding adhesion is attributed to the formation of either covalent, ionic or hydro-gen bonds across the interface. Two materials form a compound at the joint by swapping elec-tron (ionic bonding), sharing electron (covalent bonding) or the hydrogen atoms are attracted to an atom of nitrogen, oxygen or fluorine (hydrogen bonding). Chemical bonds are strong and have significantly contribution to the interior adhesion.

Figure 10. Chemical bonding scheme.

Diffusion Adhesion

Adhesion of polymeric materials is attributed to interpenetration of chains at the interface. This theory requires both the adhesive and the substrate are polymers, which are both mobile and can be soluble in each other. Figure 11 illustrates the interface between an adhesive and the substrate before and after merged by diffusion. When a polymer adhesive and the substrate are pressed together and heated, atoms diffuse from one particle to the neighbors. This creates the

27

adhesion. The diffusion adhesion is affected by the contact time, the temperature, molecular weights of polymers and their physical form (liquid, solid).

Figure 11. Diffusion adhesion scheme.

Electrostatic Theory

Electrostatic adhesion theory invokes the forming of a difference in electrical charge at the interface between two materials, in which electrons transfer from one to another. That gives a force of attraction between these materials, which contribute to the resistance to the separation of the adhesive and the substrate. Figure 12 illustrates an electrical double layer appeared when an adhesive is brought into contact with a substrate. This theory cannot be applied if either one or both materials are insulators.

Figure 12. Positive and negative electrical charge at the material joints.

Mechanical Adhesion

The mechanical interlocking theory of adhesion states that good adhesion occurs only when an adhesive penetrates into the pores, holes and crevices and other irregularities of the adhered surface of a substrate, and locks mechanically to the substrate. As illustrated in Figure 13, one surface is never completely smooth. It always consists of a numerous of peaks and valleys.

28

According to this theory, when an adhesive is brought in contact with the substrate, it must penetrate the cavities on the surface, displace the trapped air at the interface, and establish a mechanical interlocking with the interface. It means that the adhesive must not only wet the surfaces, but also have the right rheological properties to fill in the cavities and to be opened in a reasonable time.

Figure 13. Mechanical interlocking between the adhesive and the substrate.

The surface roughness helps to increase the total contact area that the adhesion force can de-velop. That will increase the total energy of surface interaction, which leads to a higher re-sistance to separation of the joint. However, the adhesive must wet the substrate well in order to have an efficiently joint.

Mechanical Adhesion in Mortars Mechanical bonding occurs by the interlocking calcium silicate hydrate that develops in binder hydration (wheter lime or cement). The filaments or needles (shown in Figure 14) act at short range and provide the mortar with cohesion by connecting and agglomerating the aggregates. They are responsible for the physical characteristics of the hardened mortar, anchoring it to the surface texture and pores with which filaments enter into contact.

29

Figure 14. Microscopic detail of cement hydration (Mapei).

The following pictures sequence shows how these filaments develop, from mortar applications to mortar hardening. This process is known as hydration and filament interlocking occurs in the final mortar-hardening phase, when most hydration has ended. This process provides the hard-ened mortar with its mechanical properties and stiffness.

a b c d

Figure 15. (a) Tricalcium silicate hydration has not occurred. (a) After three days, the particles are covered with hydration ‘filaments’. (a) Close-up of particle fibers or filaments. (a). Fully developed

interlocking between fibers and plate formation of Ca(OH)2 after 28 days. Note that, though the lime mortar microstructure does not attain such complete interlocking as that found in cement mortars, lime mortars achieve good mechanical adhesion and, in particu-lar, greater deformability.

30

Mortars in History Historically, the use of mortars dates back to thousands of years ago, when the architecture and construction of the buildings began to be associated with the use of mineral mortars: the gyp-sum-based mortars were already used by the Babylonians around 6,000 years ago and those based on pozzolanic cement (volcanic ash powder) have existed for probably over 3,000 years and were used to a large extent by the ancient Phoenicians and Greeks. In the Roman building technique, cementitious adhesive was invented and used for the first time, i.e. a mixture of mortar (lime mixed with sand or pozzolana) and stones or stone chips. Mortars were applied up to the 1950s exclusively as job-site-mixed mortars. For these mortars, the mineral binders (mostly cement) and aggregates (mostly quartz sand) are transported sepa-rately to the job-site and are then mixed together by hand in the appropriate ratio. After mixing with water, the wet mortar is ready for application. However, the on-site mixing leads to enor-mous disadvantages, starting from the transport of raw materials and mixing them in the right proportion directly to the workplace, up to the main disadvantage, which is the impossibility of automating the entire process. During the 1950s and 1960s in Western Europe and in the USA, but especially in Germany, there was a fast-growing demand in the construction industry for new building materials and technologies[13]. Job-site mortar technology was and is notable to adequately meet all these requirements. As a practical consequence, the development of the modern construction and building chemical industry in the countries of the Western world from the 1960s onwards was influenced mainly by three important trends, which can be seen nowa-days worldwide:

Replacement of the job-site-mixed mortars by premixed and prepacked dry-mix mor-tars.

Mechanization of mortar application, including bulk transportation systems (e.g., silos), mechanical systems for automatic mixing of dry-mix mortar with water, and machine application (spraying) of wet mortar.

Modification of mortars with polymer binders (redispersible powders) and special addi-tives (e.g., cellulose ethers) and admixes to improve product quality and to meet the requirements of the modern building industry.

The introduction of dry-mix mortar technology and the use of silo transport and machine appli-cation of mortars made it possible that from 1960 to 1995 the volume of render and plaster mortar application in Germany increased by 600 %, while the number of employees in this

31

sector decreased by 25 %, that means, productivity increased by 800 % 14. In Western Europe, the consequences of this development were phenomenal. Since the 1960s, a huge number of modern dry mortar plants have been established with millions of tons of capacity. In Germany, for example, nowadays approximately 100 dry mortar plants exist, producing 10×106 tons per year of dry-mix mortars. There was a tremendous boom in dry-mix mortar technology after reunification of Germany after 1990, which now continues in the countries of Eastern Europe. The average growth rate for dry-mix mortar applications in Europe is approximately 12% per annum, based on a production of about 35–40×106 tons per year in 2000[15].

Dry-Mix Mortar Formulations Over the years, even though the first formal patent relating to the production and application of dry mixtures published in Europe dates back to 1893, the technology based on the mixing of mortars on site, was progressively replaced by dry-mortars mixed in plant, which today repre-sent the only form on the market. This technology gives more value to the material: in fact, it is possible to control the mortar formula in the laboratory and considerably increase the quality of the product by introducing specific additives that modify its behavior and performance. The main advantage achieved is the possibility of producing different types of dry mortars, charac-terized by well-defined properties and suitable for the requirements for each specific applica-tion. In view of a production carried out in industrial plants and using an adequate technological and management structure, it was possible to arrive at a further decisive evolution with the passage from a quantitative criterion of the characterization of the mixtures (substantially the cement content) to a criterion performance based on very specific tests (e.g. breaking tests) of the ma-terial obtained, and on a preliminary qualification of the doughs. The raw materials used for the production of pre-packaged dry-mix mortars are classified as 1) Mineral binders

Portland cement (OPC, ordinary Portland cement); cement with high alumina content (HAC, high-alumina cement); special cements; hydrated lime; sulfates.

32

2) Fillers

silica sand; calcareous sand; dolomite sand; marble sand; light fillers; special and functional fillers.

3) Additives

cellulose ether; pigments; anti-foaming agents; aerators; retardants; thickeners; water repellents; plasticizing; superplasticisers; mineral oils for dustiness abatement.

Uses of Mortars

The main applications of premixed mortars and their volume of use are shown as follows 1) Common products (about 70% of the total volume of the dry mixed mortar produced):

masonry mortars; mortars for bricks laying; adhesives for brick laying; cement-based guides; gypsum-based guides; dry concrete;

33

concrete to be applied by spry; mineral plasters.

2) Specialized products (about 30% of the total volume of dry mixed mortar produced):

ceramic tile adhesives; building adhesives; liquid mortars for tiles; liquid mortars; decorative mineral plasters; powder paints; outdoor thermal insulation composite system; compounds to be applied with a trowel; flooring compounds; repair mortars.

These highly specialized mortars have characteristics that meet the requirements of the modern building industry and have replaced the other building materials, such as, for example, ready-to-use paste compounds and liquid mixtures used in combination with mineral mortars. Among the commercially available specialist mortars on the market, there are cement-based adhesives, which for this reason represent one of the most important Mapei sectors, context of development of the present thesis work.

Cementitious Tile Adhesives The use of ceramic wall claddings, both for interiors and exteriors, is common in architecture, both modern and ancient, for aesthetic and decorative reasons. The presence of tiles also pro-vides a water-resistant, robust, long-lasting protection, hygienic and easy to clean. The wide use of these coatings justifies the high rate of development over time of one important branch of the construction sector, represented by auxiliary materials for gluing the tiles on floors and walls. Mainly used for floors, they improve the resistance to compression and abrasion and have good chemical resistance and excellent durability.

34

Figure 16. Example of tile adhesive application.

The research related to bonding materials has led to their commercialization as premix mortars, characterized by properties dictated by the presence of special dehydrated polymers (redispersi-ble powders) and applied with the thin-bed technique: this is the only way to satisfy the tech-nical requirements regarding the functionality, efficiency and durability of the adhesive, regard-less of the types of tiles used and the substrate on which they are glued. Traditionally tile adhe-sives are a simple composition of cement and sand. Nowadays cement-based adhesives are composed of cement, aggregates and organic additives (see the next chapters). They need to be mixed with water or aqueous solutions just before use. This combination of two mineral mate-rials can be applied using the thin bed spreading technique, especially if they are modified with cellulose ethers, which significantly improve the workability and retention of water. In general, the most important characteristics to be sought in an adhesive are the flexibility, the deformability, the ease of application, the reliability and the good adhesion of all types of tiles on all types of substrates. The tile adhesives are then classified according to their mechanical properties and technical performances by the standard EN 12004, which defines and establishes the criteria for the characterization of the different classes of products.

Standards and Classifications Cementitious tile adhesives are classified according to the mechanical requirements that they are asked to satisfy. The EN 12004 standard first of all, distinguishes two main classes based on the pull-off force, measured with a dynamometer:

C1: greater than 0.5 N/mm2; C2: greater than 1 N/mm2.

35

ISO 13007-2:2010 describes methods for determining characteristics for adhesives used in the installation of ceramic tiles. The test methods described are determination of open time, deter-mination of slip, determination of shear adhesion strength, determination of tensile adhesion strength, determination of transverse deformation and determination of chemical resistance. According to ISO 13007-1:2010 (E), cementitious adhesives shall comply with all of the C1 fundamental characteristics reported in Table 2. The additional characteristics for C2 (improved performance) products are also contained in Table 2. Further subclasses are defined as a function of the setting time (F), the extended open time (E), the slippage resistance (T) and exterior glue plywood adhesion (P). These classification tests, in accordance with the EN 1348 standard, are carried out by applying the adhesive on concrete standardized slabs and gluing tiles with a low water absorption (less than 0.2%) and standard dimensions (50x50 mm), on which pull-off tests are conducted in four different storage condi-tions described below.

Open Time The open time test is designed to study the behavior of the adhesive when, once applied to the substrate, it remains exposed to the air for a long time: this happens when the operator tiles a large surface and thus spends more time to apply all the tiles. In this test, as we will see later, the fundamental ingredient is cellulose ether, which thanks to its property of incorporating wa-ter into its molecular structure, prevents drying of the adhesive paste due to the hygroscopicity of cement and filler and the formation of the “skin” due to evaporation which hinders a good wettability. Storage conditions: 7 days in standard conditions (T = 23 ± 2°C and 50% ± 5% of humidity).

Heat Aging In hot countries, temperatures of façades exposed to the sunlight can easily reach 70 °C in summer, leading to fast drying, accelerated setting of the cementitious tile adhesive and cement dilation. These application conditions have a significant effect on the long-term secure bonding of tiling. The aim of this test is to determine the critical factors for tiling under such harsh conditions, and explore ways to increase the durability of the tile work. Storage conditions: 14 days in standard conditions plus 14 days at 70°C plus one day in standard conditions. Tiles are pulled off when the temperature of the slabs drops to 23 °C.

36

Water Immersion

Adhesives used to fix tiles externally and in wet duty installations such as swimming pools and showers obviously have to have a high level of water resistance. In this context water resistance defines the ability of the adhesive to withstand contact with water without deterioration and should not be confused with the term “waterproof” which requires that the product should be impervious to water in addition to it being water-resistant. Storage conditions: 7 days in standard conditions plus 21 days of immersion in water. The test is carried out immediately after the slab is removed from the water, when it is still wet, in order to simulate the real conditions at best.

24 hours Sometimes it is required that the tiled surface is walkable within a day. This test aims to simulate the behavior of the adhesive after one day from its application. As already introduced, the ce-ment starts its curing phase within few hours, but the hydration reactions are not completed yet and therefore the adhesive does not yet have the characteristics of a completely hardened adhe-sive which is obtained after many days. Storage conditions: 24 hours in standard conditions.

Initial Adhesion The test aims to study the performance of an adhesive, which, during the course of its working life, does not undergo particular stress i.e. indoor applications. Storage conditions: 28 days in standard conditions.

Freeze/Thaw Cycles When water penetrates a tile or any part of the installation and freezes, it solidifies. As it solid-ifies, it increases in volume. This volumetric change creates mechanical stresses that increase and become more damaging as the freezing and thawing cycles continue. The stress can damage the body of the tile, its surface (especially if it is glazed), the adhesive (in some cases creating a loss of bond) and the grout. It is not constant freezing which is the condition that is most searched but the action of freezing itself.

37

The ability to withstand these cycles without serious deterioration in adhesive strength are con-sidered to be important requirements, particularly for Northern European countries. For this test only, the adhesive is also applied to the back of the tiles, as well as to the slab (“back buttering technique”). Storage conditions: 7 days in standard conditions plus 21 days of immersion in water plus 25 cycles of freezing-thawing cycles: 2h at -15 ° C / 2 hours at 15 ° C. The test is carried out on dry slabs. Based on this classification, four main types of cementitious adhesives can be distinguished: basic or C1 (1), improved (2), flexible (3) and highly flexible (4). The distinction between these categories of adhesive depends on the amount of redispersible polymer powder, on the type and amount of cellulose ether and cement.

1) The low-quality adhesives contain, in addition to the sand used as inert, about 30 - 35% of Portland cement and about 0.3% of cellulose ether as a water retention agent: they do not meet the requirements of the new European standard EN 12004. They are based on a purely mechanical fixing mechanism, therefore suitable only for porous tiles on ma-sonry substrates so that they do not give shrinkage or movement phenomena. Further-more, there is not a high risk of failure due to exposure to very high temperatures or to frost. Since the most commonly used tiles are not very porous and must be fixed on different types of substrates, this adhesive must contain a higher polymer concentration to reach the minimum standards required by the law. As a result, basic adhesives have been overcome to significantly better quality products for practical applications that meet particular needs of the market. These adhesives generally meet the requirements of C1 class.

2) The improved adhesives show a much better bond strength than the previous one cate-gory because of the use of a quantity equal to 1-3% of redispersible polymer in the premixed powder. These adhesives generally meet the requirements of C2 class.

38

3) Flexible adhesives have been successful in Europe in the last decades. They are modi-fied with a greater amount of polymer powder (3-8%) and respond promptly to the re-quirements for class C2, according to EN 12004, and ensure excellent adherence to all types of substrates and show a greater deformability. In general, they offer long-term durability and reliability almost independently of the nature of the substrate.

4) The highly flexible cementitious adhesives contain up to 15% of polymer powder and are produced for special applications16.

39

Table 3 gives the special characteristics that can be reported for cementitious adhesives.

Table 2. Specifications for cementitious adhesives. Classification Property Requirement

C1 – Normal cementitious adhesives

Tensile adhesion strength ≥ 0.5 N/mm2 Tensile adhesion strength after water

immersion ≥ 0.5 N/mm2 Tensile adhesion strength after heat

ageing ≥ 0.5 N/mm2 Tensile adhesion strength after

freeze-thaw cycle ≥ 0.5 N/mm2

Open time: tensile adhesion strength ≥ 0.5 N/mm2 after not less than 20 min

C2 – Improved cementitious adhesives

Tensile adhesion strength ≥ 1 N/mm2 Tensile adhesion strength after water

immersion ≥ 1 N/mm2 Tensile adhesion strength after eat

ageing ≥ 1 N/mm2 Tensile adhesion strength after

freeze-thaw cycle ≥ 1 N/mm2

40

Table 3. Specifications for cementitious adhesives - special characteristics. Special characteristic Property Requirement

T – Slip resistance Slip ≤ 0.5 mm

F – Fast-setting adhesives

Tensile adhesion strength ≥ 0.5 N/mm2 after no more than 6 h

Open time: tensile adhesion strength ≥ 0.5 N/mm2 after not less than 10 min

All other requirements at least equal to or better than those listed for C1

adhesives as in Table 2 See Table 2

S – Transverse deformation

Deformable adhesive (S1) ≥ 2.5 mm, < 5 mm Highly deformable adhesive (S2) ≥ 5 mm

E – Extended open time Extended open time: tensile adhesion strength

≥ 0.5 N/mm2 after not less than 30 min

P – Exterior glue plywood adhesion

(optional substrates)

Normal exterior glue plywood adhesion (P1) ≥ 0.5 N/mm2

Improved exterior glue plywood adhesion (P2) ≥ 1 N/mm2

Adhesive Strength Measurement According to EN 12004

Adhesive strength is measured, first, by the tensile or pull-off adhesion strength of a test tile bonded with an adhesive to a substrate with set characteristics. Standard EN 1323 (2007) defines the characteristics of the concrete test slab, provides instruc-tions for slab fabrication, and describes a method of determining its water absorption. The standard also lays down the environmental conditions for the performance of the test and the characteristics of the pull-off head. Standard EN 1348 (2007) describes the test method for measuring adhesive strength through the tensile adhesion strength of a cement-based adhesive. Tensile adhesion strength is expressed in terms of a unit of pressure: the force needed to pull off a tile per unit surface area. In the International System of Measurements (SI), the pressure unit is the pascal, which is the pressure exerted by the force of a newton on a square meter. In

41

the tensile adhesion strength measurement of cementitious adhesives, however, is used the new-ton per square millimeter (N/mm2) or its SI counterpart, mega pascal (MPa). The equivalence between these units being as follows:

1 N/mm2 = 1 N/0.000001 m2 = 1,000,000 N/m2 = 106 N/m2 = 106 Pa = 1 MPa According to standard EN 12004 (2017), adhesive strength is measured through tensile adhe-sion strength or shear adhesion strength, expressed in terms of N/mm2 or MPa.

Figure 17. Schematic pull-off test.

Adhesive Bond Failure Model

Standard EN 12004 also describes the failure patterns that can occur in the tensile adhesion strength test, including the assignment of their corresponding codes.

Adhesive failure: failure occurs at the interface between the adhesive and the substrate (AF-S) or between the tile and the adhesive (AF-T). The test value is equal to the bond strength. Failure sometimes occurs in the adhesive layer between the tile and the pull-off head (pull stub or dolly). In this case, adhesive bond strength exceeds the test value.

Adhesive cohesive failure: failure occurs in the adhesive layer (CF‑A). Cohesive failure in the substrate or in the tile: failure occurs in the substrate (CF-S)

or in the tile (CF-T). In this case, the bond strength is greater than the test value.

Deformability According to EN 12004 The transverse deformation has been implemented as EN 12002 first and has been adopted by the international norm ISO 13007. The intention to set up requirements for the deformation of tile adhesives reflects the fact that tile adhesives are submitted to different stresses as a result of changing mechanical or climate conditions (floor heating system, outdoor exposure – heat, frost-thaw – etc.) which the adhesive has to endure. Taking this into consideration, the test

42

determines the deformability of a mortar which is the capacity of the hardened adhesive to be deformed by stresses between the tile and the fixing surface without damaging the surface. The deformation is recorded as deflection at the centre when a layer of the hardened adhesive is subjected to a three-point loading. According to the European standard EN 12002, an adhesive strip, 3 mm thick and 45 mm wide, is deformed between supports, 200 mm apart, by an application of a progressive force in the center of the strip. The deformation is measured according to the internal Mapei method (MAC 04): the deformation is measured at the maximum load as breaking criterion.

Figure 18. Example: cementitious tile adhesive deformability.

Slip Resistance

One performance requirement of tile adhesives is often that they resist “slip” i.e. once applied they should retain their position and shape under their own weight. Slip behaviour can be eval-uated according to EN 12004 where a 100 mm x 100 mm glassed-tile is applied on the adhesive after two minutes from its layering, then its distance is measured from a reference bar, then it is kept vertical for 20 minutes and finally its flow from the initial position is measured.

Adjustability Adjustability-time is the time that the operator has available to adjust tiles once they are placed into position on the adhesive bed without loss of adhesion strength. It is also dependent on water

43

retention. If the adhesive loses its water by evaporation, or by absorption into the substrate too quickly, then the mobility of the adhesive is destroyed and attempts to adjust the position of the tile will either not be possible or, if forced, will reduce the cohesive strength attained.

Ingredients As we have seen in the previous chapter, cementitious adhesives can be quite different from each other depending on the type and the proportions of the components, the mixing, the im-plementation and the cure. We focus herein only on the ingredients that will be used in this project.

Binder Cement is the hydraulic part of a cementitious system. It is an hydraulic binder, a substance used for construction that sets, hardens and adheres to other materials, binding them together. Cement is seldom used on its own, but rather to bind sand and gravel together. Cement is used with fine aggregate to produce mortar for masonry, or with sand and gravel aggregates to pro-duce concrete. There are different types of cement, different for the composition, for the prop-erties of strength and durability and therefore for the intended use. The most used cement is the Portland cement, it is mostly used as a binder in the preparation of concrete and is considered as the fundamental cement on which almost all modern hydraulic binders are based. Due to the importance of Portland cements, and its complex chemical nature, has been intro-duced the Cement Chemist Notation (CCN), in which the most common compound formulas in cement chemistry are indicated with single letters to simplify the formulas. The most com-mon abbreviations are listed in Table 4.

44

Table 4. Abbreviations of the most common compounds in cement chemistry.

Compound Formula CCN

Calcium oxide (lime) CaO C

Silicon dioxide (silica) SiO2 S

Aluminum oxide (alumina) Al2O3 A

Iron oxide Fe2O3 F

Sulfur trioxide SO3 —S

Magnesium oxide MgO M

Water H2O H

Composition ASTM C150[17] defines Portland cement as “hydraulic cement (cement that not only hardens by reacting with water but also forms a water-resistant product) produced by pulverizing clink-ers which consist essentially of hydraulic calcium silicates, usually containing one or more of the forms of calcium sulfate as an inter ground addition”. The composition of the clinker is listed in Table 5[18].

45

Table 5. Final composition of the clinker. Compound Formula Shorthand form % by weight

Alite or tricalcium silicate Ca3SiO4 C3S 55 Belite or dicalcium silicate Ca2SiO5 C2S 20

Tricalcium aluminate Ca3Al2O6 C3A 10 Tetracalcium aluminoferrite Ca4Al2Fe2O10 C4AF 8

Sodium oxide Na2O N Up to 2

Potassium oxide K2O K

Gypsum CaSO4·2H2O C—S H2 5 Note that the gypsum is added in order to regulate the setting time that, otherwise, it will be too fast.

Mechanism of Hydration Reaction The hydration of cement is a complex system of chemical reactions between cement and water, thanks to which the cement is transformed from an initially plastic, and therefore easily mold-able mass into a rigid and mechanically resistant material. The progression of these reactions manifests itself through two distinct physical-mechanical variations.

Setting: a first gradual loss of workability of the cement until the moment when the mixture is no longer workable.

Curing: the successive and progressive increase of mechanical resistance. Between these two processes there is no discontinuity: the consistency of the material increases progressively, going from the typical one of a mud to that of a compact rock. Distinguishing the two processes responds above all to a practical need of the production process; with the beginning of the setting phase, the time available to make use of the dough ends. When water is added, the reactions which occur are mostly exothermic. We can get an indica-tion of the rate at which the minerals are reacting by monitoring the rate at which heat is evolved

46

using a technique called conduction calorimetry. The heat generated is shown below as a func-tion of time.

Figure 19. Rate of heat evolution during the hydration of Portland cement.

The stage I hydrolysis of the cement compounds occurs rapidly with a temperature increase of several degrees. Stage II is known as the dormancy period. The evolution of heat slows dramat-ically in this stage. The dormancy period can last from one to three hours. During this period, the concrete is in a plastic state which allows the concrete to be transported and placed without any major difficulty. This is particularly important for the construction trade who must transport concrete to the job site. It is at the end of this stage that initial setting begins. In stages III and IV, the concrete starts to harden and the heat evolution increases due primarily to the hydration of tricalcium silicate. Stage V is reached after 36 hours. The slow formation of hydrate products occurs and continues as long as water and unhydrated silicates are present. A more detailed explanation of hydration reactions is given below. When water is added to cement, the following series of reactions occur:

The tricalcium aluminate reacts with the gypsum in the presence of water to produce ettringite and heat:

C3A + 3C—S H2 + 26H C6AS3H32, H = 207 cal/g Ettringite consists of long crystals that are only stable in a solution with gypsum (Figure 20). The compound does not contribute to the strength of the cement glue.

47

Figure 20. Crystal structure of ettringite. (A) Structure of ettringite column, one-half unit cell. Struc-

ture is parallel to the c crystallographic axis. View of A – B plane. Circles represent ettringite col-umns; regions between columns are channels containing water and sulfate molecules[19].

The tricalcium silicate (alite) is hydrated to produce calcium silicate hydrates, lime and

heat: 2C3S + 6H C3—S 2H3 + 3CH, H = 120 cal/g

The CSH has a short-networked fiber structure which contributes greatly to the initial strength of the cement glue.

Once all the gypsum is used up as in the first reaction, the ettringite becomes unstable and reacts with any remaining tricalcium aluminate to form monosulfate aluminate hy-drate crystals:

2C3A + 3C6A—S 3H32 + 22H 3C4ASH18, The monosulfate crystals are only stable in a sulfate deficient solution. In the presence of sul-fates, the crystals resort back into ettringite, whose crystals are two-and-a-half times the size of

48

the monosulfate. It is this increase in size that causes cracking when cement is subjected to sulfate attack.

The belite (dicalcium silicate) also hydrates to form calcium silicate hydrates and heat: C2S + 4H C3—S 2H3 + CH, H = 62 cal/g

Like in the second reaction, the calcium silicate hydrates contribute to the strength of the cement paste. This reaction generates less heat and proceeds at a slower rate, meaning that the contri-bution of C2S to the strength of the cement paste will be slow initially. This compound is how-ever responsible for the long-term strength of Portland cement concrete.

The ferrite undergoes two progressive reactions with the gypsum: - in the first of the reactions, the ettringite reacts with the gypsum and water to form

ettringite, lime and alumina hydroxides, i.e. C4AF + 3C—S H2 + 3H C6(A,F) —S 3H32 + (A,F)H3 + CH

- the ferrite further reacts with the ettringite formed above to produce garnets, i.e. C4AF + C6(A,F) —S 3H32 + 2CH +23H 3C4(A,F) —S H18 + (A,F)H3

The garnets only take up space and do not contribute in any way to the strength of the cement paste. Finally, the hardened cement paste consists of the following[18][20][21]:

Ettringite- 15 to 20% Calcium silicate hydrates, CSH- 50 to 60% Calcium hydroxide (lime) - 20 to 25% Voids- 5 to 6% (in the form of capillary voids and entrapped and entrained air)

49

Figure 21. SEM image of the complex structure of the hydration reaction products that coat the grains

of cement.

Cellulose Ether and Modifiers Introduction

Cellulose is an organic compound with the formula (C6H10O5)n, a polysaccharide consisting of a linear chain of several hundred to many thousands of β(1→4) linked D-glucose units[22][23].

Figure 22. Cellulose, a linear polymer of D-glucose units (two are shown) linked by β(1→4)-glyco-

sidic bonds. The cellulose fibers consist of bundles of parallel, non-branched chains joined by hydrogen bonds between hydroxyl groups on adjacent chains, forming fibrils (Figure 23). This arrange-ment of parallel chains in bundles due to the hydrogen bonds gives the cellulose fibers a high mechanical strength. Cellulose can assume both an amorphous and crystalline conformation (Figure 24).

Figure 23. Structure of the bundle of polymer chains.

50

Figure 24. Schematic illustration of amorphous and crystalline domains.

The crystalline part is hydrophobic. In order to obtain a hydrophilic product (such as the com-mon cotton) it is necessary to subject the cellulose to a set of treatments called mercerization. Cellulose ethers are characterized by the degree of substitution (DS), the average number of substituents (MS) and the degree of polymerization (DP).

Uses and Effects in Mortars Cellulose ethers is key performance additive in cement based tile adhesives. Cellulose ethers have a notable influence on adhesive’s fresh characteristics: they provide water retention for open time, tile correction time, and proper hydration development of the cement paste. Cellu-lose ethers also provide rheological properties for enhanced application characteristics. In ad-dition, polysaccharides such as starch derivatives are used to improve the consistency of the fresh material. The cellulose ethers most commonly used in cement mixtures are:[24]

Hydroxyethyl Cellulose (HEC); Hydroxyethyl Methyl Cellulose (HEMC); Hydroxypropyl Methyl Cellulose (HPMC).

Several treatments in the literature show how cellulose ethers (CE) have a significant effect on the physical properties of cement. In this treatment we restrict the investigation of this interac-tion exclusively on a Portland system. Already in 1985 Hayakawa and Soshiroda[25][26] demon-strated the ability of cellulose ethers to interact with cement, being able to bind the cement matrix and the aggregates and thus prevent their gravimetric separation in the fluid paste. Tanaka et al.[27], have patented an additive which contains CEs capable of increasing the fluidity and workability and which, above all, confer a higher resistance to compression.

51

Yamamuro et al.[28] have shown that the polysaccharide derivatives contain ionic and hydro-phobic functional groups that increase the viscosity of the cementitious suspension. CE also improves the adhesion of cement to the substrate, which increases the cement's ability to bond with many surfaces. Ghio et al.[29][30] have shown that in cements formulated with CEs there is an increase in thixotropy that has effects on rheological properties and, therefore, de-termines an improvement in workability. Some celluloses, in particular HEMC and HPMC[31], are also able to stabilize air bubbles created during the mixing phase. The stabilization of the air bubbles is important for a good workability: if, in fact, the air bubbles collapse during the modeling processes, it would be impossible to use the mixture properly. On the other hand, however, the air bubbles deteriorate the cohesive strength of the hardened mortar: the strength of cohesion and the resistance to flexibility and compression are in fact exponential functions of density. It is therefore necessary to optimize the system, looking for the best compromise between good workability and compressive strength. From a chemical point of view, cellulose ethers and water molecules have high affinity: each EC molecule is surrounded by a hydration sphere. This immobilizes the fluid phase and gives the cement the ability to "retain" water. Indeed cellulose ethers are used in tile adhesives in order to confer sufficient water retentivity on them. This is necessary to ensure that the mixing water is available to the tile adhesive long enough to hydrate the cement. High water retentivity also ensures prolonged wetting capability of the adhesive once it has been combed onto the substrate (tilers still refer to this period as the "open time"). Especially at high temperatures or in windy conditions, the reliability of the bond is increased if the adhesive has prolonged wet-ting capability, because once the adhesive starts to form a skin (because of evaporation), the surface area available to the bonding will be less[28]. Another feature often required in cementitious adhesives is slip resistance. Indeed, a good slip resistance is necessary when installing tiles on non-horizontal areas and, of course, on vertical surfaces. This characteristic is achieved from certain rheological modifier present in the so-called modified cellulose ethers that enhance the viscosity and the thickness of the fresh mortar paste in order to give a greater consistency to the mixture. The modified cellulose ether consists in cellulose ethers with specific molecular weight plus the addition of thickening substances as starch and polyacrylic amide (PAA). Also the cellulose itself has a thickening effect on the fresh mortar paste: the higher degree of polymerization and thus molecular weight, the higher the solution viscosity and thickness[28].

52

Aggregates Coarse Aggregates

Aggregates serve as reinforcement and structural component to add strength to the tile mortar. They provide packing density by filling voids, flexural strength and durability. Aggregate may be based on quartz, limestone and silica. Generally, silica sand (quartz) used for thin-bed adhe-sives has a grain size distribution in the range of 0.05 to 0.5 mm. Low-density aggregates like expanded glass granulates (granular density < 0.5 kg/l) are used in tile adhesives instead of high-density silica sand (≈ 2.6 kg/l) for special applications[32].

Fine Aggregates Another filler often used in cement-based tile adhesive is the calcium carbonate. The granu-lometry can vary from 0.01 to 0.1 according to the use to which they are put. A fine calcium carbonate gives a creamy consistency and an exceptionally smooth workability to the fresh mixture. On the other hand, limestone powder tends to absorb water due to its hygroscopic nature and, used in large quantities, gives brittleness to the hardened tile adhesive. Redispersible Polymer

Introduction The second generation of thin-bed tile adhesives, so-called flexible, polymer-modified tile ad-hesives were polymer-modified tile adhesives were placed on the market in the beginning of 1980s. The main benefits of redispersible polymer powder additives are improved workability, higher flexibility, and better adhesion. They made possible the safe laying of tiles on floor heat-ing systems, terraces and balconies. Even fully vitrified and glass tiles could be laid without problems due to excellent physical bonding of elasticized mortar to the reverse side of tiles. Redispersible, elasticizing polymer powder invented by Wacker Chemie in 1953 was the key for this quantum leap in thin-bed technology[32].

Production Redispersible polymer powders are organic polymer materials produced from latex dispersion by spry-drying. Latex dispersion are manufactured by emulsion polymerization. The starting emulsion consists of water-insoluble monomers (i.e. vinyl acetate, vinyl versatate, ethylene, styrene, methacrylic acid esters etc.) and surfactant (emulgator) in a continuous phase of water.

53

Water-soluble, so-called protective colloids (i.e. polyvinyl alcohol or cellulose ethers) are added to stabilize the emulsion micelles. The polymerization is started by addition of persul-fates or peroxides (initiators) to the emulsion. At the end of the polymerization process a milky polymer dispersion containing 40 to 70 % of latex particles of diameter 0.1 to 20 μm is obtained.

Figure 25. (a): poly(ethylene-co-vinyl acetate) (EVA). (b): poly(styrene-co-butyl acrylate).

Lattices with particles above 1 μm are of milky-withe appearance. Latex particles in the range of 0.1 to 1 μm give blue or brownish emulsions. Afterwards the emulsion is spray dried to obtain polymer powder. For that purpose, the latex emulsion is atomized into fine drops by a nozzle into the spray tower. An anti-caking agent is added during the spray-drying process in order to avoid the agglutination of the still sticky redispersible powder. The dried polymer pow-der is pneumatically carried out of the spray-tower and separated via a cyclone from the humid air. Redispersibility means that the dried polymer particles of 100 to 500 μm size disperse into the primary emulsion latex particles (0.1 to 20 μm) when stirred with water[32].

Uses The polymer domains which work as an organic binder in the tile adhesive matrix develop after evaporation of water of the mortar by coalescence of the individual latex particles. The polymer domains provide better adhesion at the mortar/substrate and mortar/tile interface. The main benefits of redispersible polymer powders in thin-bed tile adhesive are:

better interlocking of adhesive and substrate; higher flexibility reduces shear stress in the composite substrate, tile adhesive and tile

and allows the use of large tiles;

a b

54

improved rheological properties result in easier mixing, smother workability and good wetting of the tiles.

Typical polymer emulsion used for tile adhesive are copolymers of ethylene and vinyl acetate (EVA) or styrene and butyl acrylate. Dosage of redispersible polymer powder in cementitious thin-bed tile adhesives range from 0 to 5 %[32].

Glass Transition Temperature The mechanical properties of polymers radically change at the glass transition temperature (Tg); molecular motion is the underlying cause of the change. Below Tg there is no translational or rotational motion of the atoms that make up the polymer backbone, but these motions are pre-sent above Tg. Below Tg, polymers are relatively hard, inflexible and brittle, whilst above it they are soft and flexible. The terms glassy, and rubbery or leathery are used to describe properties in the two temperature regions.

Fibers Cementitious materials are brittle in nature. Due to this behavior, short, randomly and distrib-uted fibers are mostly being used to reinforce cementitious materials in the hardened state and to avoid creeping in the fresh state. Added fibers enhance tensile strength and flexural toughness and reduce crack creation and propagation in cement matrix. The major effect of fibers is to act as bridging at crack tips to resist crack propagation. Fiber bonding to cement paste is an im-portant factor that affects performance of the fiber reinforced cementitious composite (FRCC). Bonding energy (adhesion) between these materials is composed of interfacial interactions (chemical bonding) and mechanical interactions (interlocking)[33].

Figure 26. From the left: polypropylene mesh fiber; polypropylene fiber; cellulose fiber loose.

Source: asiafiberhk.com)

55

Fibers can be classified into two groups depend on their average length: long fibers with the higher aspect ratio among 200 to 500, are mainly used for reinforcement of mortars; short fibers, which have a general aspect ratio among 20 to 60, are used in influence wet-mortar properties and water demand. Long fibers, typically over 40 mm’s length are also called macro fibers. A typical dosage of macro fiber is 3-8 kg/m³. Whereas micro fibers are normally 6-12 mm’s length with a typical dosage is 0.6-1.0 kg/m³. Macro fibers are primarily used to enhance the toughness of a render or screed. Figure 26 shows some types of fibers, which can be used for reinforce-ment of mortars, including polypropylene fiber, cellulose fiber, etc.[34]

56

Rheology Importance of Rheology

In this section, we wish to recall the some basic definitions of the rheological science. For this thesis it would be inappropriate to omit a chapter focused on rheology and therefore, on the rheology that gravitates around the cementitious adhesives. This is because cementitious adhe-sives must have suitable properties as:

workability; flow and frictional resistance against surfaces; adhesion; resistance to sagging under self weight on a wall or inclined surface.

Thanks to an increasingly scientific approach in recent years, it is becoming possible to predict fresh properties, to design and select materials and to model processes to achieve the required performance. As a result, rheology has become a term recognized by technologists34. The situ-ation mirrors that in the coatings industry described by Eley who stated that: “to fully realize the hoped for benefit of rheological analysis, a robust link must be established between rheology and technology.”[35]. As will become evident, that link is not fully established for cement-based materials. This chapter aims to review our current understanding of the rheology of cement-based mortars in the fresh state.

Introduction to Rheology The term rheology was coined by Eugene C. Bingham, a professor at Lafayette College, in 1920, from a suggestion by a colleague, Markus Reiner[36]. The term was inspired by the apho-rism of Simplicius (often attributed to Heraclitus), πάντα ῥεῖ, "everything flows"[37]. Rheology is defined as “the science of the deformation and flow of matter”[38], It is a branch of physics and physical chemistry since the most important variables come from the field of me-chanics: forces, deflections and velocities. This definition is very wide and formally would in-clude studies such as those of hydrodynamics and aerodynamics, which in fact are not normally regarded as coming within its scope. In practice, rheology is principally concerned with extending continuum mechanics to charac-terize flow of materials, that exhibits a combination of elastic, viscous and plastic behavior by

57

properly combining elasticity and (Newtonian) fluid mechanics. It is also concerned with es-tablishing predictions for mechanical behavior (on the continuum mechanical scale) based on the micro- or nanostructure of the material, e.g. the molecular size and architecture of polymers in solution or the particle size distribution in a solid suspension. Materials with the characteris-tics of a fluid will flow when subjected to a stress which is defined as the force per area. There are different sorts of stress (e.g. shear, torsional, etc.) and materials can respond differently under different stresses. Much of theoretical rheology is concerned with associating external forces and torques with internal stresses and internal strain gradients and flow velocities[39][40].

Continuum mechanics The study of the physics of continuous materials

Solid mechanics The study of the physics of continuous materials with

a defined rest shape.

Fluid mechanics The study of the physics of continuous materials

which deform when subjected to a force.

Elasticity Describes materials that return to their rest shape after applied stresses are

removed.

Plasticity Describes materials that

permanently deform after a sufficient applied

stress.

Non-Newtonian fluids do not undergo strain

rates proportional to the applied shear stress.

Newtonian fluids undergo strain rates proportional to the

applied shear stress. Rheology

The study of materials with both solid and viscoelastic behaviour.

Rheology unites the seemingly unrelated fields of viscosity and non-Newtonian fluid dynamics by recognizing that materials undergoing these types of deformation are unable to support a stress (particularly a shear stress, since it is easier to analyze shear deformation) in static equi-librium. In this sense, a solid undergoing viscous deformation is a fluid, although no viscosity coefficient is associated with this flow[41][42].

58

One of the major tasks of rheology is to empirically establish the relationships between defor-mations (or rates of deformation) and stresses, by adequate measurements, although a number of theoretical developments (such as assuring frame invariants) are also required before using the empirical data. These experimental techniques are known as rheometry and are concerned with the determination with well-defined rheological material functions. Such relationships are then amenable to mathematical treatment by the established methods of continuum mechanics. The characterization of flow or deformation originating from a simple shear stress field is called shear rheometry (or shear rheology). The study of extensional flows is called extensional rhe-ology. Shear flows are much easier to study and thus much more experimental data are available for shear flows than for extensional flows.

Rheological Models Viscosity

Viscosity is a fundamental characteristic property of all liquids. When a liquid flows, it has an internal resistance to flow. Viscosity is a measure of this resistance to flow. Viscosity can also be determined as a drag force and is a measure of the frictional properties of the fluid. Viscosity is a function of temperature and pressure. Although the viscosities of both liquids and gases change with temperature and pressure, they affect the viscosity in a different manner. The fluid’s resistance to flow is caused by intermolecular friction exerted when layers of fluids attempt to slide by one another[43]. When the viscosity of a liquid remains constant and is independent of the applied shear stress, such a liquid is termed a Newtonian liquid. In the case of the non-Newtonian liquids, viscosity depends on the applied shear force and time. The shear viscosity of a fluid expresses its resistance to shearing flows, where adjacent layers move parallel to each other with different speeds. If the speed of the top plate is low enough, the fluid particles will move parallel to it, and their speed will vary linearly from zero at the bottom to v at the top. Each layer of fluid will move faster than the one just below it, and friction between them will give rise to a force resisting their relative motion. In particular, the fluid will apply on the top plate a force F in the direction opposite to its motion, and an equal but opposite one to the bottom plate. An external force is therefore required in order to keep the top plate moving at constant speed. It can be expressed as following:

59

= (1.7) where, τ = F/A is the shearing stress (explained later), η is the viscosity. In conclusion, viscosity (sometimes referred to as absolute viscosity) is obtained by dividing the shear stress by the rate of shear strain. The unit is Force/Area × Time = Pa·s.

Viscoelasticity Viscoelasticity is the property of materials that exhibit both viscous and elastic characteristics when undergoing deformation. A viscous material exhibits time-dependent behavior when a stress is applied while under constant stress and deforms at a constant rate, and when the load is removed, the material has ‘forgotten’ its original configuration, remaining in the deformed state. On the other hand, an elastic material deforms instantaneously when stretched and ‘re-members’ its original configuration, returning instantaneously to its original state once the stress is removed. Viscoelastic materials have elements of both of these properties and, as such, exhibit time-dependent strain showing a ‘fading memory’. Such a behavior may be linear (stress and strain are proportional) or nonlinear. The physical reasons for elastic behavior can be quite different for different materials. In metals, the atomic lattice distances and its shape change when forces are applied (energy is added to the system). When forces are removed, the lattice goes back to the original lower energy state. For rubbers and other polymers, elasticity is caused by the stretching of polymer chains when forces are applied[44]. In purely elastic materials, the stress and strain occur in phase, so that the response of

one occurs simultaneously with the other. In purely viscous materials, there is a phase difference between stress and strain, where

strain lags stress by a 90 degree (π/2 radian) phase lag (Figure 27). Viscoelastic materials exhibit behavior somewhere in between that of purely viscous

and purely elastic materials, exhibiting some phase lag in strain[44].

60

Figure 27. Elastic and viscous response.

Stress and strain in a viscoelastic material can be represented using the following expressions.

Strain: = ( ) Stress: = ( + )

where ω = 2 π f, where f is frequency of strain oscillation; t is time; δ is phase lag between stress and strain. The elastic and viscous modulus in viscoelastic materials measure the stored energy, represent-ing the elastic portion, and the energy dissipated as heat, representing the viscous portion[44]. The tensile storage and loss moduli are defined as follows.

Storage: = Loss: =

Continuous Tests

In this section, we wish to recall some basic definitions of rheology, without particular reference to mortar, including shear stress, shear rate, and yield stress and then we will focus on continu-ous rotational tests.

Shear Stress A shear stress, denoted τ, is defined as a stress, which is applied parallel or tangential to a face of a material, as opposed to a normal stress, which is applied perpendicularly.

Purely Elastic Response

Stress

Strain

= 0° = 90° Purely Viscous Response

Stress Strain

61

Figure 28. Simple schematic of shear stress.

In particular as shown in Figure 28Figure 29, it will result in a strain, or deformation, changing the square into parallelogram. The formula to calculate average shear stress is:

= (1.8) where τ is the shear stress, F is the force applied and A is the cross sectional area.

Shear Rate Consider a volume material as a set of parallel molecular layers kept between two parallel planes with the distance h between them as described in the Figure 29. The lower plane is fixed, and the upper plane is displaced by a distance dh at a constant speed v.

Figure 29. Simple schematic of shear rate. Under the effect of tangential forces, the upper molecular layers move at the same speed of the adjacent moving plane. The lower layers will move in the same direction but with smaller and smaller velocities. They create a gradient of velocity between the two planes. The displacement between two planes is defined as the deformation of the volume material, or the strain, denoted γ, follows the relation:

s

s = 0

62

= ℎ (1.9) The standard constant velocity gradient across the sample is defined as the shear rate . Also called strain rate, it is the strain rate between two adjacent layers of the sheared fluid. It is often presented as the derivative versus time of the deformation: = = ℎ = ℎ = ℎ (1.10)

Yield Stress The yield stress is defined as the minimum applied shear stress that we observed a fluid flow in the materials. When the applied shear stress is lower than this value, the material shows the solid-like behavior (no flow, no permanent deformation). Pass through this threshold, there will be a transition from solid-like to liquid-like behavior. The material will be sheared. The yield is the intercept between the ordinate axis (shear stress) and the shear rate. An approach is to start with the sample in its at-rest state (no permanent deformation) and in-crementally increase the shear stress until we identify the value at which it starts to flow. It means that the fluid sample goes from solid-like behavior to liquid-like behavior.

The Bingham Model The rheological behavior of fluids flow can be classified into Newtonian and non-Newtonian fluids based on the relationship between the shear stress and shear rate. If this relationship is linear, the fluid is Newtonian. Otherwise, it is non-Newtonian. At the fresh state, tile adhesives consists of a mixture of water with solid particles ranging in size typically from about 0.4 mm down to less than one μm. The solids concentration is very high and the smaller particles are taking part in chemical reactions, so it is reasonable to expect that the rheology of such a system will be very complicated. Fortunately, there is now a con-siderable body of evidence to show that the behavior of fresh cementitious adhesives can in practice be represented by a very simple model (the Bingham model) because the average shear rates of practical processes are low and they can be approximated as Bingham plastics[34]: ma-terials that behave as a rigid body at low stresses but flows as a viscous fluid at high stress. These materials present a yield stress limit which must be exceeded before a significant irre-versible deformation can occur. The proposed model has the form:

63

= + (1.11) where τ is the shear stress applied to the material, τ0 is the Bingham yield stress, describing the stress needed to initiate flow, η0 is the Bingham plastic viscosity, which is the resistance of the material to flow, and is the shear strain rate. Bingham model is used to characterize the fluids which have a constant viscosity value. Shear thinning materials show a decrease in viscosity under increasing shear loads, but they do not show a time dependent recovery of viscosity unless they are also thixotropic. Hence a thix-otropic material is shear thinning, but a shear thinning material is not necessarily thixotropic. In adhesive use, thixotropy can be a benefit. The lowering of viscosity as the adhesive is strained will aid spreading but the subsequent recovery in structure and thickness will help retain adhe-sive where it is desired. Ordinary cement-based adhesive is one example of a shear-thinning fluid, while oobleck provides one realization of a shear-thickening fluid. In the equation 4.8, when n = 1 and τ0 ≠ 0, the fluid behavior is Bingham. When n = 1 and τ0 = 0 the fluid is Newtonian. By variation of n and the yield stress τ0, we can express the shear thinning as well as the shear-thickening fluids. Typical flow curves of shear stress versus shear rate for different rheological behavior models are shown in the following figure.

Figure 30. Classification of fluids with shear stress as a function of shear rate.

Flow Test

In order to determine the general flow behaviour of a sample, the viscosity is measured as a function of the shear rate in a rotational rheometer. For the presentation of the data, either the

64

viscosity or the shear stress is plotted against the shear rate. The thus obtained graph is called flow curve. In many cases the flow curve is the first and usually also the most important rheo-logical measurement. It shows the flow behaviour for low shear rates (slow motions) as well as high shear rates (fast motion). Low shear rate appear e.g. at the levelling or sagging of paints, sedimentation or slow flowing. High shear rates (> 1000 1/s) occur at the majority of technical processing like coating, spraying or flowing though pipes. By means of the flow curve it is possible to read the viscosity for the shear rates of interest. If someone is interested in the flow behaviour during the use of cementitious adhesives the vis-cosity at shear rates below 1 1/s are important.

Oscillatory Stress Sweep Tests Oscillatory test are important when testing materials may undergo macro or micro-structural rearrangement with time. These rearrangements directly influence rheological behaviour. Os-cillatory stress sweeps directly provide the necessary information about how a material changes with the increase of the stress. In the oscillation stress sweep the sample is subjected to small amplitude oscillatory (i.e. clock-wise then counterclockwise) shear. In the early stages of the test stress is sufficiently low to preserve structure but as the test progresses the incrementing applied stress causes the ultimate disruption of structure – the yield process – manifested as a decrease in elasticity (phase angle rises) and an accompanying decrease in rigidity.

Creep Tests The creep test forms a simple and quick method used to find the viscoelastic properties of the material under investigation. The mobile part of the measuring arrangement is loaded with a constant shear stress (τx) for a certain period of time. The sample reacts on this force with a deformation, i.e. the material starts to creep. For this work, this test has been used to evaluate more accurately the slip resistance of cementi-tious adhesives. In fact, one performance requirement of cement-based adhesives is often that they resist ‘slump’ or ‘slip’ (i.e. once the tile is applied on a wall, it should retain its position under its own weight). Slip behaviour can be studied through creep measurements of the long-term flow of the paste under small but constant stress.

65

Slump resistant adhesives often take advantage of thixotropy: “a decrease of viscosity under constant shear stress or shear rate, followed by a time dependent recovery when the shear is removed”. Thixotropy is often confused with shear thinning or pseudo-viscosity.

Rheological Measurements Cementitious adhesives are widely used for many rendering functions, both outdoor and indoor. The rheological requirements associated with the various uses and processing combinations are, therefore, expected to be diverse as well[45]. In cement adhesive laboratories, for each product under investigation, some properties of the fresh adhesive paste are evaluated.

1. Viscosity: it is measured with the single point test using the Brookfield viscometer. This is a very fast method because it provides an almost instantaneous response which must fall within a certain range of viscosity in order to find the correct w/c rate and obtain a good workability of the fresh paste.

2. Slip test according to ISO 13007 / EN 1308: it is a practical method used to measure the vertical slip of the tile.

It is obvious that these tests were chosen to have rapid responses without the use of complex and sensitive tools. However, these measurements are rough, has a low reproducibility and are affected by the op-erator’s error and thus, to fully understand the rheological properties of cement adhesives, this method is not suitable. Therefore, proper rheological evaluation is needed to provide a useful information for the opti-mization of fresh cementitious adhesives performance. Rotational rheometry is used to measure the viscosity of cement based adhesives by varying shear stress. In this work, two geometries were used: the cross-hatched parallel-plate geometry and ball-measuring system geometry. Cross-hatched parallel-plate geometry (Figure 31a) is normally used either in rotational flow or in oscillatory modes for the rheological evaluation of cementitious pastes. The cross-hatched plate is needed because it guarantees a necessary grip for the reproducibility of the measure and avoid slip. The gap between the two plate must be much larger than the particle size (at least

66

ten times) (sand grains in our case), otherwise the continuous medium hypothesis always adopted in the rheometer theories is violated. The ball-measuring system (BMS) (Figure 31b) is normally used for large-particulated fluids. It consists of a sphere that is dragged through a sample volume of approximately 0.5 l. Imple-mented in a rotational rheometer, this ball performs a rotational motion through the sample on a circular path. Torques exerted on the sphere and the corresponding rotational speeds are rec-orded within a wide measuring range. (a)

Figure 31. Schematic of measurement geometries: (a) parallel plate (not to

scale); (b) ball-measuring system.

(b)

Because of its suitable features, the parallel-plate and the ball-measuring system geometries have been extensively used in this work for the evaluation of cement adhesive pastes in creep, stress sweep (parallel-plate only), and flow tests.

Dynamic Mechanical Analysis Dynamic mechanical analysis (DMA) is an effective approach to evaluate the viscoelastic be-haviour of solid state materials. A sinusoidal stress is applied and the strain in the material is measured, allowing one to determine the complex modulus. The temperature of the sample (in our case) or the frequency of the stress are often varied, leading to variations in the complex modulus; this approach can be used to locate the glass transition temperature of the polymer contained in the cement adhesive, as well as to identify transitions corresponding to other mo-lecular motions.

Cross-hatched plate

67

II. EXPERIMENTAL PART

Materials Tile Adhesives

In this work we analyzed several components of typical tile adhesive formulation, we indicate the range in which each component dosage has been varied in the tests.

Portland cement “Colacem 52.5R GREY”; batch: July 2017 (20% - 40%). Cellulose ether (0.25% - 0.45%), modified or non-modified. Redispersible polymeric powder (0% - 5%). Fine calcium carbonate (<100 μm) (0% - 20%). Coarse calcium carbonate (<600 μm) (0% - 20%). Cellulose fibers (0% – 2%). Silica sand (<450 μm). Preconditioned water at 23 °C.

All the materials were conditioned at least for 8 hours under standard conditions (23 ± 1°C, relative humidity of 50 ± 5%).

Concrete Slabs All the concrete slabs used for tile adhesive testing were in compliance with ISO 13007 / EN 1323 in terms of water absorption, residual humidity and superficial resistance. They were all coming from the same producer, with random production batches used during the experimental campaign.

Ceramic Tiles Smooth Body Tiles

All the tiles used for water immersion, heat aging, f/t cycles were, according to ISO 13006:2012, ceramic tiles, group BIII, belonging to non-porous tile, complying with ISO

68

13006:2012 of water absorption, cut to facial dimensions of (50 ± 1) mm × (50 ± 1) mm, with a thickness in the range of 5 mm to 6 mm.

Porous Body Tile According to ISO 13006:2012; ceramic tiles, group BIII, belonging to porous body tile, com-plying with ISO 13006 of water absorption (15 ± 3) % mass fraction, cut to facial dimensions of (50 ± 1) mm × (50 ± 1) mm, with a thickness in the range of 7 mm to 10 mm and a profile back pattern less than 0,25 mm deep.

69

Software An R-language based free Chemometrics software distributed from Italian Chemometrics Group[46] was used to make Principal Component Analyses and to obtain data analysis, in order to use the proper Design of Experiment and to obtain all the equations coefficients necessary to describe the behavior of adhesives in the whole investigated domain. An internal software based on Microsoft Excel and Microsoft Visual Basic has been devel-oped[47] in order to obtain an intuitive graphical evaluation of the obtained results, making them intuitively described by:

A fully rotatable tri-dimensional graph, representing one of the chosen response in func-tion of two variables.

A bi-dimensional graph, with a plain representation of the response curves obtained through the mathematical equation.

The possibility of graphs animation, in order to visually investigate the behavior of the desired response in function of a third variable

Single point calculation of all the investigated responses in function of up to five input composition variables.

70

Instruments

Pull-off tests were performed with the dynamometer “M053” of LBG. Cali-bration class 0.5 with maintenance of 250 N/s.

Samples were mixed with the plane-

tary mixer “Automix - 65-L0006/AM” of Controls Group using the “slow” speed: 140±5 rpm. Bowl capacity: 5 liters. Distance between beater and bowl: 3±1 mm during all cycle.

Evaluations of viscosity were carried out using the viscometer “Brookfield DV-I”. Torque measurement accu-racy: 1% of full scale range. Repeata-bility: 0.2% of full scale range.

71

Deformability tests were performed using “Uniframe-Mini” dynamome-ter of Controls Group. Class 1 accu-racy (load and deformation).

Dustiness of cementitious tile adhe-sives was evaluated with “DustMon L” from AnaTech. Sample amount : 50 g. Measurement time: typically 30 s. Measurement principle: light ex-tinction.

DMA tests were performed using

“DMA 242 C” of Netzsch. Tempera-ture range: -170°C to 600°C. Heating rate: 0.01 to 20 K/min. Frequency range: 0.01 to 100 Hz. High force range: 24 N (12 N static and 12 N dynamic). Modulus range: 10-3 to 106 MPa. Static defor-mation: Up to 20 mm.

72

Rheological properties were evaluated using “AR-G2” of TA Instrument and “MCR 302” of Anton Paar. For the TA rheometer the parallel-plate, cross-hatched geometry was used. For the Anton Paar rheometer the ball-measuring system was used.

73

Experimental Designs

Tile Adhesive Model In this thesis two Fractional-Factorial Design with Central Composite points were realized in order to understand and predict the behavior of cementitious adhesives from the mechanical and rheological point of view.

First Model In this work a Full-Factorial design, without Central Composite points, would have required 25=32 experiments which are considered excessive both for the high quantity of raw material needed and also for the large amount of time required to conduct all the experiments. For this reason a Fractional-Factorial with Central Composite Design model, was ideated in order to understand the influence of five different ingredients in the performance of cementitious tile adhesives. The model includes the following five formulation variables:

Table 6. Formulation variables. Ingredient -1 0 1

Portland cement 20% 27.50% 35% Cellulose ether 0.25% 0.35% 0.45% CE Modification Non modified 50/50 mix with CE Highly modified Polymer 0% 2.50% 5% Calcium Carbonate 0% 10% 20%

In this work, the quantity of sand (coarse filler) was added until that the sum of the percentage values reach 100%. All the samples contains 0.6% of calcium formate, a common accelerant used in the formula-tions of cementitious adhesives. For this Fractional Factorial Design the fifth column is generated by the alias generator intro-duced in the theory part of this thesis. In this case the fifth column is generated from the multi-plication of the first four column.

74

All the sample were prepared and analyzed following a random numeration, in order to mini-mize the systematic error caused by the operator. The experimental plan is showed in the following table:

Table 7. Tile adhesive experimental plan. Test Cement Cellulose ether CE Modification Polymer Filler

1 -1 -1 -1 -1 1 2 -1 -1 -1 1 -1 3 -1 -1 1 -1 -1 4 -1 -1 1 1 1 5 -1 1 -1 -1 -1 6 -1 1 -1 1 1 7 -1 1 1 -1 1 8 -1 1 1 1 -1 9 1 -1 -1 -1 -1 10 1 -1 -1 1 1 11 1 -1 1 -1 1 12 1 -1 1 1 -1 13 1 1 -1 -1 1 14 1 1 -1 1 -1 15 1 1 1 -1 -1 16 1 1 1 1 1 17 0 0 0 0 0 18 0 0 0 0 0 19 0 0 0 0 0 20 0 0 0 0 -1 21 0 0 0 0 1 22 0 0 0 -1 0 23 0 0 0 1 0 24 0 0 -1 0 0 25 0 0 1 0 0 26 0 -1 0 0 0 27 0 1 0 0 0 28 -1 0 0 0 0 29 1 0 0 0 0

75

Second Model The second model treated in this thesis is the Fractional-Factorial plus the Central Composite Design seen above, with the addition of four verification points, for a total of 33 runs. It was designed in order to check and verify the actual "operation" of the first model. Verification point include the levels -0.5 and 0.5 which represent the central points between -1 and 0 (-0.5) or between 0 and 1 (0.5) (tab).

Table 8. Verification points. Test Cement Cellulose ether CE Modification Polymer Filler 30 0 0 0 0 0.5 31 0 0 0 0.5 0 32 0 0 0.5 0 0 33 -0.5 0 0 0 0

76

The final model including the verification points is:

Table 9. Second model, including verification points. Test Cement Cellulose ether CE Modification Polymer Filler

1 -1 -1 -1 -1 1 2 -1 -1 -1 1 -1 3 -1 -1 1 -1 -1 4 -1 -1 1 1 1 5 -1 1 -1 -1 -1 6 -1 1 -1 1 1 7 -1 1 1 -1 1 8 -1 1 1 1 -1 9 1 -1 -1 -1 -1 10 1 -1 -1 1 1 11 1 -1 1 -1 1 12 1 -1 1 1 -1 13 1 1 -1 -1 1 14 1 1 -1 1 -1 15 1 1 1 -1 -1 16 1 1 1 1 1 17 0 0 0 0 0 18 0 0 0 0 0 19 0 0 0 0 0 20 0 0 0 0 -1 21 0 0 0 0 1 22 0 0 0 -1 0 23 0 0 0 1 0 24 0 0 -1 0 0 25 0 0 1 0 0 26 0 -1 0 0 0 27 0 1 0 0 0 28 -1 0 0 0 0 29 1 0 0 0 0 30 0 0 0 0 0.5 31 0 0 0 0.5 0 32 0 0 0.5 0 0 33 -0.5 0 0 0 0

Due to the large amount of sample needed to perform all the tests required, we decided to pre-pare dry sample of 9 kg each.

77

The formulation of the samples is showed in the next table:

Table 10. Tile adhesive experimental plan formulation. Sam-ple

Cement (g)

Cellulose (g)

CE Modification (g)

Polymer (g)

CaCO3 (g) Accelerant

(g) Sand

(g) 1 1800 22,50 0,00 0,0 1800 54 5323,5 2 1800 22,50 0,00 450,0 0 54 6673,5 3 1800 0,00 22,50 0,0 0 54 7123,5 4 1800 0,00 22,50 450,0 1800 54 4873,5 5 1800 40,50 0,00 0,0 0 54 7105,5 6 1800 40,50 0,00 450,0 1800 54 4855,5 7 1800 0,00 40,50 0,0 1800 54 5305,5 8 1800 0,00 40,50 450,0 0 54 6655,5 9 3150 22,50 0,00 0,0 0 54 5773,5 10 3150 22,50 0,00 450,0 1800 54 3523,5 11 3150 0,00 22,50 0,0 1800 54 3973,5 12 3150 0,00 22,50 450,0 0 54 5323,5 13 3150 40,50 0,00 0,0 1800 54 3955,5 14 3150 40,50 0,00 450,0 0 54 5305,5 15 3150 0,00 40,50 0,0 0 54 5755,5 16 3150 0,00 40,50 450,0 1800 54 3505,5 17 2475 15,75 15,75 225,0 900 54 5314,5 18 2475 15,75 15,75 225,0 900 54 5314,5 19 2475 15,75 15,75 225,0 900 54 5314,5 20 2475 15,75 15,75 225,0 0 54 6214,5 21 2475 15,75 15,75 225,0 1800 54 4414,5 22 2475 15,75 15,75 0,0 900 54 5539,5 23 2475 15,75 15,75 450,0 900 54 5089,5 24 2475 31,50 0,00 225,0 900 54 5314,5 25 2475 0,00 31,50 225,0 900 54 5314,5 26 2475 11,25 11,25 225,0 900 54 5323,5 27 2475 20,25 20,25 225,0 900 54 5305,5 28 1800 15,75 15,75 225,0 900 54 5989,5 29 3150 15,75 15,75 225,0 900 54 4639,5 30 2475 16,20 16,20 225,0 1350 54 4864,5 31 2475 16,20 16,20 337,5 900 54 5202,0 32 2475 23,40 8,10 225,0 900 54 5314,5 33 2138 16,20 16,20 225,0 900 54 5652,0

The raw results obtained are listed below:

Table 11. Tile adhesive experimental plan raw results. Test

Water ratio (%)

Sp. Grav.

(g/cm3) EN Slip (mm)

Mapei Slip

(mm) Adjust. (min)

24h (N/mm2)

O.T. 5' (N/mm2)

O.T. 20' (N/mm2)

O.T. 30' (N/mm2)

H.A. 5' (N/mm2)

H.A. 10' (N/mm2)

H.A. 15' (N/mm2)

W.I. 5' (N/mm2)

W.I. 10' (N/mm2)

W.I. 15' (N/mm2)

F/T Cy-cles

(N/mm2) Initial adhes.

(N/mm2) Deform.

(mm) Load (N) 1 21.5 1.6 0.3 0.4 10 0.65 1.18 0.00 0.00 0.74 0.00 0.00 0.57 0.44 0.31 1.32 0.39 1.51 7.60 2 19.0 1.32 2.1 10.0 40 0.89 2.57 1.30 0.56 2.11 1.35 1.31 0.79 0.57 0.54 1.15 2.79 3.50 8.44 3 23.5 1.5 0.1 0.2 5 0.30 0.89 0.00 0.00 0.31 0.00 0.00 0.50 0.47 0.32 1.09 0.51 1.27 3.73 4 24.0 1.6 0.3 0.5 5 0.74 2.81 0.55 0.14 2.51 0.20 0.14 0.66 0.52 0.39 0.60 2.78 3.03 9.86 5 25.0 1.3 5.0 10.0 50 0.60 1.35 0.90 0.59 0.54 0.49 0.41 0.67 0.59 0.39 0.93 0.97 1.72 4.51 6 25.5 1.37 5.0 10.0 50 0.89 2.34 0.85 0.60 2.20 1.89 0.90 0.68 0.51 0.46 1.24 2.61 3.24 8.35 7 27.5 1.54 0.2 0.4 10 0.28 0.64 0.48 0.18 0.24 0.12 0.00 0.58 0.27 0.00 1.03 0.75 1.38 4.43 8 24.5 1.35 0.5 0.9 15 0.40 2.08 1.71 1.06 1.78 1.54 1.35 0.61 0.50 0.45 1.02 1.65 3.65 6.62 9 21.5 1.59 0.8 1.2 15 1.10 1.10 0.27 0.00 1.26 0.55 0.17 1.44 0.81 0.60 2.13 1.86 1.54 12.87 10 22.0 1.65 3.0 10.0 25 0.99 1.55 0.18 0.00 2.46 0.18 0.00 1.12 0.47 0.00 2.27 2.71 2.53 16.47 11 25.0 1.84 0.1 0.2 2 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.80 0.37 0.00 1.87 0.45 1.57 16.22 12 23.5 1.69 0.3 0.6 5 1.08 2.27 0.18 0.00 3.68 1.36 1.08 1.50 0.95 0.61 1.98 3.48 2.62 14.41 13 27.0 1.56 5.0 10.0 25 0.83 1.80 0.29 0.00 1.62 1.08 0.23 1.29 0.80 0.52 1.66 2.17 1.92 10.47 14 26.0 1.43 5.0 10.0 50 1.36 3.48 1.33 0.81 3.34 2.94 2.36 1.32 1.17 0.74 1.82 3.45 2.84 12.08 15 29.0 1.52 0.4 0.7 15 0.41 1.00 0.26 0.18 0.68 0.58 0.27 1.16 0.97 0.71 1.80 1.04 1.61 10.41 16 29.0 1.61 0.4 0.8 18 1.14 2.54 0.52 0.22 3.61 1.89 0.00 0.93 0.72 0.44 2.39 2.32 2.86 10.31 17 25.0 1.48 0.8 1.3 18 1.11 1.69 0.46 0.17 2.45 1.23 0.87 1.08 0.71 0.52 1.75 2.68 2.27 9.23 18 24.5 1.47 0.7 1.2 20 1.06 1.87 0.72 0.32 1.87 1.43 1.06 1.19 0.66 0.51 1.85 1.90 2.24 9.28 19 24.5 1.53 0.6 1.4 22 0.88 1.87 0.81 0.22 2.50 1.47 1.15 1.00 0.77 0.52 1.54 2.17 2.39 9.02 20 23.5 1.45 1.0 2.5 20 1.00 2.06 0.88 0.56 1.59 1.45 0.73 1.02 0.73 0.50 1.33 2.10 2.29 9.82 21 25.0 1.56 0.6 1.0 15 1.11 1.66 0.53 0.04 2.21 0.90 0.00 0.68 0.53 0.60 1.86 2.38 2.23 12.09 22 25.0 1.56 0.6 1.0 12 0.57 0.85 0.27 0.00 0.54 0.21 0.00 0.60 0.44 0.41 1.81 1.09 1.81 7.40 23 24.0 1.49 1.6 3.2 25 1.29 3.07 0.81 0.47 3.43 2.12 1.70 1.19 0.68 0.52 1.44 3.55 2.99 11.43 24 23.5 1.48 5.0 10.0 30 1.14 2.27 0.80 0.41 2.34 1.80 1.11 1.11 0.56 0.50 1.50 2.91 2.18 10.48 25 26.0 1.56 0.2 0.5 12 1.10 2.41 0.94 0.51 2.42 1.18 0.92 1.09 0.60 0.47 1.87 2.56 2.19 12.51 26 22.5 1.59 0.3 0.7 10 1.17 1.55 0.20 0.00 2.15 0.68 0.21 0.96 0.68 0.44 2.02 2.23 2.17 11.47 27 26.5 1.49 2.0 7.5 40 0.89 2.07 1.06 0.64 2.11 1.79 0.98 1.01 0.58 0.45 1.26 2.10 2.50 8.48 28 24.0 1.48 0.8 1.4 20 0.89 1.86 0.89 0.46 1.77 1.39 1.11 0.79 0.63 0.29 1.22 1.95 2.28 7.06 29 25.0 1.61 0.6 1.1 15 1.33 1.49 0.57 0.17 2.04 1.26 0.00 1.24 0.74 0.43 2.08 2.54 2.11 11.47 30 25.0 1.51 1.0 2.6 25 1.14 2.22 0.65 0.37 2.13 1.59 0.77 1.08 0.69 0.45 1.49 2.33 2.30 8.66 31 23.5 1.46 1.8 3.4 30 1.22 2.36 0.75 0.39 2.58 1.33 1.36 0.97 0.70 0.52 1.53 2.49 2.62 7.79 32 23.0 1.48 2.2 6.0 35 1.11 2.15 0.78 0.48 2.10 1.34 0.86 1.08 0.69 0.49 1.38 2.22 2.29 8.84 33 23.0 1.49 0.9 1.5 20 0.88 2.05 0.84 0.29 1.75 1. 17 0.86 0.83 0.57 0.74 1.16 2.14 2.22 7.46

Table 12. Tile adhesive experimental plan raw results.

Test Creep 200 Pa

(%) Creep 300 Pa

(%) G'

Plat. (kPa)

G' 200 Pa (kPa)

G' 300 Pa (kPa)

G" Plat. (kPa)

G" 200 Pa (kPa)

G" 300 Pa (kPa)

G' ½ (kPa)

Yield TA

(Pa·s) 1s-1TA (Pa·s)

Yield AP

(Pa·s) 1s-1 AP (Pa·s)

ph 1s-1 (Pa·s)

ph 5s-1 (Pa·s)

ph 10s-1 (Pa·s)

E'5 (MPa)

E'23 (MPa)

E'50 (MPa)

Delta E' (MPa)

1 23.5 52.8 45 16 8.9 23 12 6.3 30 14400 614 5140 328 335 103 61 11880 11620 11150 730 2 46.7 161.3 22 4.0 1.7 12 2.4 11 25 18110 579 5934 328 323 106 65 8740 7855 6050 2690 3 0.2 0.6 1408 1235 979 291 215 228 400 513800 654 26390 330 330 88 52 7080 6805 6510 570 4 2.2 5.7 279 32 20 118 19 17 60 92470 591 18800 330 310 94 55 11720 10830 8445 3275 5 89.2 246.0 30 7.1 3.6 17 4.4 2.2 25 21280 808 5925 401 409 106 57 5990 5800 5620 370 6 184.7 428.6 9.0 2.1 1.2 5.0 1.2 0.9 25 12550 609 3660 370 374 113 67 7730 7000 5530 2200 7 1.2 2.3 358 70 38 146 45 40 125 115700 777 45430 607 414 105 60 7271 7110 6843 428 8 4.9 7.5 1.33 19 11 56 11 11 50 55860 708 13180 330 383 115 69 7070 6385 4415 2655 9 9.1 19.9 62 20 13 30 21 12 30 35960 741 14360 505 442 132 75 21310 21030 20000 1310

10 13.7 23.7 22 22 12 12 16 7.9 315 308300 768 6570 445 380 120 80 18800 17715 15255 3545 11 0.2 0.4 1624 1103 339 360 308 159 250 194000 1141 55940 651 450 123 68 20575 20110 19430 1145 12 2.4 4.4 266 53 26 118 41 30 100 81460 630 19125 392 394 110 65 15750 14690 12115 3635 13 31.6 80.6 32 14 6.0 16 8.0 2.9 60 13950 781 3950 406 433 126 74 15285 14940 14380 905 14 118.6 205.0 6.5 2.2 1.6 4.1 1.2 1.1 8 11240 609 3300 350 360 110 67 11810 10790 9192 2618 15 1.7 4.2 242 41 18 104 25 23 100 112900 760 20635 413 338 90 50 13745 13390 13140 605 16 1.2 3.9 228 54 32 84 29 30 100 74770 815 21200 440 395 111 73 10360 9625 8225 2135 17 8.0 17.0 104 21 14 48 18 10 30 33600 632 9440 340 353 111 66 12235 11385 10070 2165 18 6.0 13.0 129 25 14 62 17 12 50 40310 741 9485 410 440 130 73 13255 12570 11170 2085 19 4.9 10.0 130 24 14 60 16 14 50 37010 666 9570 330 352 103 66 11215 10530 9305 1910 20 10.0 26.0 102 18 10 51 14 10 40 25710 656 10320 375 376 110 64 13660 12870 11060 2600 21 2.8 6.7 181 33 18 77 22 14 80 54960 786 11023 445 410 117 70 13172 12810 11270 1902 22 2.4 6.0 244 39 28 108 25 23 80 61950 757 16075 448 414 116 67 11615 11450 11055 560 23 9.1 22.1 51 7.3 3.4 24 3.9 2.1 40 22830 612 6230 345 350 110 68 11215 10530 8610 2605 24 91.9 359.3 14 2.9 1.1 7.5 1.5 0.8 20 12030 616 3936 337 356 109 66 14270 13700 11985 2285 25 2.6 5.3 22 22 12 12 16 8.1 100 117600 765 19820 418 342 96 57 15430 14645 13040 2390 26 3.9 8.6 216 35 17 96 32 23 80 36260 598 10690 400 352 104 62 13770 13000 11445 2325 27 11.2 20.6 71 14 9.1 32 10 5.5 40 19040 656 6885 330 348 100 59 9882 9600 8330 1552 28 7.4 14.2 13 21 13 64 16 13 60 30200 618 7175 335 348 105 61 8010 7710 6460 1550 29 4.9 13.4 14 21 14 5.9 13 11 80 33330 769 9895 390 405 115 67 15333 14690 13385 1948 30 7.2 17.6 76 14 6.8 34 8.8 5.5 40 31730 661 10625 402 389 112 66 10480 10140 9010 1470 31 12.2 28.8 55 13 6.3 25 9.6 5.2 40 39580 734 8412 350 364 110 65 11000 10500 8960 2040 32 41.5 107.0 34 7.3 3.7 18 4.4 2.3 15 22190 759 4680 364 364 114 68 11950 11600 10345 1605 33 8.8 22.5 73 15 7.5 32 12 6.7 50 30070 690 7190 390 391 118 69 10135 9695 8605 1530

80

Dustiness Model Dustiness of cementitious tile adhesives is a real-world practical issue, generated every time a bag of product is poured into a bucket (Figure 32). In this work, a second Fractional-Factorial with Central Composite Design model was ideated in order to investigate dust generation in cementitious tile adhesives and understand how the ingredients contribute to generate dust. This model includes the following formulation variables:

Table 13. Formulation variables of dustiness model. Ingredient -1 0 1

Portland cement 20% 30% 40% Fine calcium carbonate 0% 10% 20% Coarse calcium carbonate 0% 10% 20% Polymer 0% 5% 10% Cellulose fibers 0% 1% 2%

Figure 32. Dusty and not dusty product.

81

In the following table is listed the experimental plan and its raw results:

Table 14. Dustiness experimental plan and results. Exp. Cement Fine C. Coarse C. Polymer Fiber D’ (at 0s) D” (at 30s) Dust Index

1 -1 -1 -1 -1 1 18,8 35,7 54,5 2 -1 -1 -1 1 -1 13,0 30,1 43,2 3 -1 -1 1 -1 -1 7,3 17,4 24,7 4 -1 -1 1 1 1 20,6 44,0 64,6 5 -1 1 -1 -1 -1 6,8 18,2 25,0 6 -1 1 -1 1 1 26,1 59,7 85,9 7 -1 1 1 -1 1 16,3 44,1 60,4 8 -1 1 1 1 -1 14,7 46,1 60,8 9 1 -1 -1 -1 -1 6,9 23,5 30,3 10 1 -1 -1 1 1 17,9 44,0 62,0 11 1 -1 1 -1 1 11,8 31,1 42,8 12 1 -1 1 1 -1 20,3 59,0 79,3 13 1 1 -1 -1 1 10,8 40,0 50,8 14 1 1 -1 1 -1 16,2 51,5 67,7 15 1 1 1 -1 -1 8,0 30,4 38,4 16 1 1 1 1 1 18,4 50,1 68,5 17 0 0 0 0 0 14,5 37,7 52,2 18 0 0 0 0 0 15,5 36,6 52,1 19 0 0 0 0 0 13,5 36,2 49,7 20 0 0 0 0 -1 10,9 44,2 55,1 21 0 0 0 0 1 16,5 45,7 62,2 22 0 0 0 -1 0 9,8 31,0 40,8 23 0 0 0 1 0 16,7 46,0 62,7 24 0 0 -1 0 0 13,7 33,6 47,3 25 0 0 1 0 0 13,1 43,3 56,3 26 0 -1 0 0 0 13,0 33,8 46,7 27 0 1 0 0 0 13,2 38,6 51,9 28 -1 0 0 0 0 15,5 37,2 52,7 29 1 0 0 0 0 11,6 53,2 64,8

This model does not include verification points that will be seen in future projects.

82

Procedures All of the procedures were carried out by my with the exception of the pull-off tests which were carried out by an operator.

Mixing Procedure For each sample we found the volumes of water in such a way that the fresh paste has a Brookfield viscosity between 450 and 550 mPa·s, calculated using the Brookfield viscometer. Then we proceeded following the internal Mapei mixing procedure “MAC 01-E”. The water is poured into the five-liter capacity planetary mixer (Automix); the mixer is started at “slow” speed and 3 kg of the dry blend is added in a constant rate followed by mixing until 30 seconds; the bowl is scraped down with the paddle within 1 minute; the paddle is replaced and the mixing is started again for 1 minute; the mixing is stopped to let the adhesive mature for 10’; the mixing is started again for 15 seconds at speed 1.

Evaluation of Adhesion All the investigated formulations were checked following the same preparation procedure and pulled off with identical dynamometers (LBG M053, properly calibrated according to ISO 7500-1). The laboratory was maintained by a proper system fully conditioned, keeping temper-ature, humidity and air speed within the range dictated by the norm: 23±1°C, relative humidity of 50±5% and air speed lower than 0.2 m/s. After the standardized mixing procedure, the adhesive is spreaded on a concrete slab in a single pass, with a 6 mm-toothed square notched trowel. The trowel shall be held at an angle of ap-proximately 60° to the substrate at a right angle to one edge of the slab and drawn across the slab parallel to that edge (in a straight line). After 5’, 10’ and 15’ or 5’, 20’, 30’ (for open time testing only), 50 mm x 50 mm tiles are applied in the adhesive bed. Each tile is loaded with a 20 N weight for 30 sec (Figure 33). The slabs prepared with each adhesive were submitted to all the curing procedures (Figure 34) according to ISO13007 and listed in Table 15.

83

Figure 33. Laying of adhesive and tiles.

Figure 34. Slabs aging in different conditions and subsequent pull-off test.

Table 15. Sample curing for pull-off test.

Days in air (23°C - 50%

R.H.) Days in

oven (70°C)

Days in wa-ter

Freeze / Thaw Cycles

24 hours 1 - - - Open time (5’, 20’, 30’) 7 - - - Initial adhesion 28 - - - Heat (5’, 10’, 15’) 14 14 - - Water (5’, 10’, 15’) 7 - 21 - Freeze / Thaw 7 - 21 25

Storage at room temperature (open time).

Porous tiles were used. The slabs are stored under standard conditions (23 °C, 50% R. H.) for 7 days and not 28 as the norm says, in order to have a more critic measurement. Then the pull-head plates are bonded to the tiles with an epoxy adhesive and then the bond strength measurement is carried out.

84

Storage at room temperature (initial adhesion). Porous tiles were used. The slabs are stored under standard conditions for 27 days. Then the pull-head plates are bonded to the tiles and the slabs are stored for another day before the strength measurement.

Short-time storage (24 h). Smooth tiles were used. The pull-head plates are bonded to the tiles before the applica-tion on the adhesive. After the application, the slabs are stored under standard condition for 24 hours and then the bond strength measurement is carried out.

Storage with water immersion.

Smooth tiles were used. The slab is stored for 7 days under standard conditions and afterwards immersed in water for 20 days. The pull-head plates are bonded to the tiles and the tiles are immersed for further 7 hours. Then the test units are removed from water and the bond strength measurement is immediately carried out.

Heat aging. Smooth tiles were used. The slabs are stored for 14 days under standard conditions and then at 70°C in air-circulating oven for a further 14 days. After removal of the tests units from the oven the pull-head plates are bonded to the tiles and the test units are stored at standard condition for further 24 hours.

Freeze-thaw cycles. Smooth tiles were used. The tiles are covered with a 1 mm thick layer of adhesive before placed on the adhesive bed (buttering-floating technique). After the slabs have been stored for 7 days under standard conditions and immersed in water for 21 days, 25 freeze-thaw cycles are carried out. One freeze-thaw cycle consists of storing the slab test unit for 5 hours in a cold chamber at -15°C and afterwards for 2 hours in a water quench. After the last cycle the test unit is removed from the water quench, wiped with a cloth and the pull-head plates bonded to the tiles. The test units are stored another 24 hours at standard conditions before testing.

85

Measurement of Tensile Adhesion Strength The tensile testing dynamometer is applied to the pull-head plates and the pull-off test is per-formed with a constant load rate of 250 N/s. The pull-off dynamometers must satisfy the mini-mum requirements for Class 1 according to ISO 7500-1, with a minimum sampling frequency of 50Hz. These tests allow to dictate the adhesive mortar classes according to ISO 13007.

Evaluation of Fresh Properties We proceeded following the Mapei internal mixing procedure “MAC 17” according to ISO 13007. After the mixing procedure the viscosity cup is filled with the product taking care not to intro-duce any air bubbles and the cup is tapped 3-5 times to release air. The viscosity cup is posi-tioned below the spindle and the latter is immersed in the fresh adhesive; the Brookfield is started up and the viscosity reading recorded in mPa·s. Brookfield viscosity has been chosen as an indicator of adhesive consistence: in this way, the water ratio used for mixing could be an output variable of this experimental campaign. Specific gravity of the fresh material has been evaluated with internal methods, as well as ad-justability time of the adhesive, checked as the maximum time at which 100 mm x 100 mm P1 tile can be “adjusted” by the operator when applied on a non-absorbent substrate. Slip tests were performed according to ISO 13007 / EN 1308, applying a layer of adhesive, leaving in on a slab for two minutes, then applying a 100 mm x 100 mm V1 tile, measuring its distance from a reference bar, then keeping it vertical for 20 minutes and finally measuring its slippage from the initial position.

Figure 35. Evaluation of fresh properties (from left to right): Brookfield viscosity, EN slip, Mapei

slip.

86

Evaluation of Deformability All the investigated samples were tested for their deformability, according to ISO 13007 / EN 12002 norms. After a standardized mixing procedure, six samples were prepared for each for-mulation: the fresh adhesive is poured into the templates on a flat polyethylene film, and a flow table is used to form a test strip of 45 mm width, 3 mm thickness and 280 mm length. The tests specimens are placed in an air-tight plastic container for two days under their molds and then conditioned at standard conditions for another 14 days. After that, the thickness of the tests specimens is determined using a caliper with 0.01 mm precision. Samples which deviate more than 0.1 mm from 3 mm are discarded. Testing phase (3 points flexural test) has been performed using a Uniframe-Mini dynamometer from Controls, with a sensitivity of 0.001N, a preload value of 0.1N and using deformability at maximum load as breaking criterion.

Figure 36. Deformability evaluation.

Evaluation of Dustiness

The Dustiness has been evaluated with an AnaTec DustMon L machine. A fixed amount of cementitious product drops in an 80 cm tube and then generates a cloud of dust inside a testing chamber: the measurement is taken with a laser light detector, that transforms the amount of non-passing light in a dustiness index. These values (%) are recorded immediately after the dust cloud generation and after 30 second from the impact. The so-called “Dust Index” is the sum of these two numbers and can be considered as an indi-cator of air quality during the use of cementitious products.

87

Figure 37. Dustiness evaluation.

Evaluation of Rheological Properties All the runs were carried out under controlled temperature (23°C).

Mixing Procedure All the paste batches were prepared using the same water ratio used in the evaluation of tile adhesion strength. Different mixing batch were used for each test and repetition. The parallel plate geometry requires a little amount of sample: 200 g of the dry blend were poured in a 250 ml polypropylene container and mixed with the appropriate quantity of water for 30” using a digital overhead stirrer with a dough hook bar. The paste was then maturated for 10 minutes, mixed again for 30” and was ready for the measurement. For the ball-measuring system measurements, 500 g of the dry blend were mixed with the ap-propriate quantity of water using the planetary mixer (Automix - 65-L0006/AM) using the in-ternal procedure “MAC 01-E”.

Sample Loading For the parallel plate geometry, a silicon ring with 40 mm diameter and 2.8 mm height was used for moulding the adhesive paste on the lower plate; the paste was placed with a spoon and the excess material removed with a spatula; the ring was then removed. For the ball-measuring system, the fresh sample was poured into a stainless steel bowl, with 10 cm diameter and 5 cm deep, until filling. The bowl was replaced in the appropriate compartment of the rheometer and the spherical geometry was replaced and immersed in the dough.

88

Test Procedure

In this work, for each sample, four different rheological tests were carried out using two geom-etry assembled on two rotational rheometer: TA rheometer used a cross-hatched parallel plate geometry; Anton Paar rheometer used a ball-measuring system (BMS). All tests were performed twice to confirm the repeatability of the measurement. After each run, the geometries were cleaned with water and then dried with compressed air. For tests requiring parallel-plate geometry, the upper cross-hatched plate was lowered until it gently touched the flat surface of the sample at a gap distance of 1500 μm. This preparation procedure ensured that initial geometry and volume of the samples were constant for all tests. The Peltier plate maintained the temperature of the system at 23 °C for all the tests. In addition, it is recommended to attempt disturbing the sample as little as possible when loading, in order to maintain its original structure. This mainly consists of descending the upper plate slowly to keep low shear rate and to avoid excessive increase of the normal force and possible modifica-tion/destruction of the sample structure during the test. For each test, the instrument performed an auto calibration and gap auto zero. This procedure is identical for each test.

Flow curves were performed using both the geometries. The shear was set in the range from 10-3 s-1 to 100 s-1 in 3 minutes of measurements. The instrument recorded 10 points in series for every order of magnitude.

Oscillatory stress sweep tests were performed using the cross-hatched parallel plate ge-ometry. The shear stress range was set from 1 to 1000 Pa in 3 minutes of measurement. The instrument recorded the values of the elastic (G’) and viscous modulus (G”) 10 times in series for every order of magnitude.

Peak-hold tests were performed using the BMS. Three peak hold tests were performed at 3 different shear: 1, 5 and 10 s-1. The rheometer performs more revolutions on the same sample under a constant shear stress.

Creep tests were performed with the cross-hatched parallel plate geometry. A constant shear (200 or 300 Pa) was set for 3 minutes after 3 minutes of waiting.

89

Evaluation of Dynamic Mechanical Properties The samples already used for the deformability tests were collected and cut in order to obtain samples measuring 60x10x4 mm. DMA measurements were performed using DMA 242 C using the temperature scan method at 2° C/min. It started from 0°C and ended at 50°C using scan frequency of 1 Hz.

90

III. RESULTS AND DISCUSSIONS

Principal Component Analysis Correlations in Tile Adhesives Testing

Using the Principal Component Analysis technique and the tile adhesive Experimental Design, it is possible to analyze and evaluate the correlations among the whole performance profile of cementitious tile adhesives, including pull-off, deformability and DMA tests. It is therefore possible, thanks to this technique, to extract useful information from the huge amount of data collected.

Figure 38. Cementitious tile adhesives loading plot.

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3

-0.3-0.2

-0.10.0

0.10.2

0.3

Loading Plot (67% of total variance)

Component 1 (39.1% of variance)

Comp

onent 2

(27.9%

of var

iance)

EN Slip

Adjust.

24h

O.T. 5'

O.T. 20'O.T. 30'

H.A. 5'

H.A. 10'

H.A. 15'

Water 5'

Water 10'

Water 15'

F/T Cycles

28 gg

E'5E'23E'50

Delta E'

Deform.

Load

Water ratio

Sp. grav.

Mapei Slip+

Cement

Cellulose ether

91

Figure 39. Cementitious tile adhesives score plot.

Variance explained by 5 components: 87.62%. Variance explained by each component: 39.06% 27.89% 9.07% 7.23% 4.37%. Multivariate analysis clearly highlights the correlations between tests.

1) All the elastic modulus from the DMA (E’5, E’23, E’50), 24 hours pull-off, water im-mersion, freeze-thaw cycles and pull-off after heat aging are strictly correlated to each other and they are all related to the cement amount and to the specific gravity of the adhesive. In this way, they all seem inversely correlated to the amount of cellulose ether in formulation. The cement, among other things, gives rigidity to the structure of the hardened adhesive, increasing its modulus. This observation can be confirmed by observing the score plot in which we can identify some samples that are positioned in the same area where, in the loading plot, the tests correlated to the cement amount are present. In fact, if we take into account the samples

-5 0 5

-6-4

-20

24

6

Score Plot (67% of total variance)

Component 1 (39.1% of variance)

Comp

onent 2

(27.9%

of var

iance)

1 2

3

4

5

6

78

910

11

12

13 14

1516

17181920

21

22

23242526

2728

29

30 3132

33

+

92

that are present in that area (i.e. 9, 11 and 12), they all have a high content of cement and little amount of cellulose ether.

2) Open time at 20’, at 30’, adjustability and slip resistance are strictly correlated and they are all related to the cellulose ether content. A high amount of cement and a high specific gravity penalize these results.

3) Water immersion and heat aging at 5’ are correlated to the cement amount and at 10’ and especially at 15’ they move into the cellulose group.

4) Polymer generally improves all the performances, with a lower effect on water immer-sion and freeze-thaw cycles and a very important contribution over heat, initial adhesion and deformability results.

5) The modified cellulose ether improves only the slip test (that, naturally, has better re-sults when the values are low) and strongly penalize all other results.

These considerations will be examined and studied later using Response Surface Curves (RSC).

93

Rheological Tests Correlations General Overview

Before exposing the results using the chemometrics approach, it is better to focus on the classic approach, in order to present, in a generic way, the individual graphs of the rheological tests carried out on the fresh and hardened samples. In order not to dwell too much on classic approach, only three samples with very different formulations will be exposed in this chapter. A more detailed exposition and explanation of the results of this work will be dealt using the chemometric approach, thanks to which it is possible to analyze and understand the rheological behavior of cement adhesives, taking into consider-ation all 33 samples simultaneously for each test. For each characterization, the results of three different samples have been chosen which differ considerably from the formulation and rheological point of view (see Table 16). All the de-picted graphs, except the creep ones, are set up using a logarithmic scale for the ordinate and the abscissae axis in order to have graphs that are more readable.

Table 16. Formulations of the chosen samples in percentages. Raw materials (w/w %) n°6 n°11 n°18

Cement 20.00 35.00 27.50 Cellulose 0.45 - 0.175 Modified Cellulose - 0.25 0.175 Sand 204 53.95 44.15 59.05 Polymer - - 2.50 Calcium Carbonate 20.00 20.00 10.00 Water 0.255 0.250 0.245

Flow Curves

The flow curves depicted in the next two figures, show the trend of the viscous evolution (vis-cosity) (Pa∙s) with increasing the shear rate [1/s]. In this way, it is possible to visualize how the viscosity of the adhesive changes in a range of shear rates unlike the Brookfield measurement which calculates the viscosity only at a determined shear rate The experiments were carried out using two different geometries and instruments: the graph in Figure 40 is obtained from the TA® rheometer with the plate-plate cross-hatched system. The

94

graph in Figure 41 is obtained from the Anton Paar rheometer using the ball-measuring system. Also in these test, three different sample were chosen: 6, 11 and 18.

Figure 40. Flow curve TA, cross-hatched parallel-plate geometry.

Figure 41. Flow Curve AP. Ball-measuring system.

10

100

1000

10000

100000

0,01 0,1 1 10 100

Viscosit

y (Pa·s)

Shear rate (1/s)

Flow Curve TA

Sample 6 Sample 11 Sample 18

1,0E+01

1,0E+02

1,0E+03

1,0E+04

1,0E+05

0,001 0,01 0,1 1 10 100

Viscosit

y (Pa·s)

Shear rate (1/s)

Flow Curve AP

Sample 6 Sample 11 Sample 18

95

As Bingham pseudoplastic fluid, the viscosity of the cementitious adhesives drops with the increasing of the stress. However, it is clear the difference between the three samples, both in the first and in the second graph: it is evident that sample 11 has a higher viscosity than the sample 18 and 6. In this experiment the parameters that influence most are: the quantity of cement, the water mix ratio and the quantity of cellulose. Indeed sample 11 has 35% of cement instead the 27.5% of the sample 18 and the 20% of the sample 6 (see table xx of the formulas). The flow curves obtained from the plate-plate cross-hatched geometry cross each other at 10 [1/s]. This happens because the sand could generate friction with the cross-hatched geometry. The two tests both express the same property of an adhesive but the curves obtained are not the same because different geometries were used.

Oscillatory Stress Sweep Below (Figure 42) is depicted a multiple line graph of the stress sweep carried out on samples 6, 11 and 18. In this graph is showed both the elastic and viscous modulus, G’ and G” respectively, on the Y-axis using a logarithmic scale and the oscillatory stress (Pa) on the X-axis. This test is specific to investigate the viscoelastic modulus of the fresh adhesives.

96

Figure 42. Stress sweep test.

In all the samples G’ > G” until a certain value of stress: the fresh adhesives show an elastic behavior when the oscillatory stress is low and it is almost linear until 100 Pascal. After a certain value (that differs between the three samples) the viscous behavior becomes predominant (G”>G’) because the structure of the adhesives begins to breakdown. The G’ and G” stress sweep curve of the sample 11 his shifted in two orders of magnitude relative to the sample 18 and in three orders relative to the sample 6 and this result enlightens the big difference, both in viscous and elastic modulus, between the three samples. This test shows that the fresh paste of the sample 11, unlike the sample 6, is very structured and thicker due to the high quantity of cement and modified cellulose ether, which are determinant in this test. The sample 18 shows an intermediate behavior. A more detailed analysis of the results will be discussed using the RSC in which we will be able to understand the influence of all the other ingredients.

1,00E+02

1,00E+03

1,00E+04

1,00E+05

1,00E+06

1,00E+02

1,00E+03

1,00E+04

1,00E+05

1,00E+06

1 10 100 1000

G'' (Pa

) ●■▲

G' (Pa)

●■▲

Osc. Stress (Pa)

Stress Sweep

Sample 6 Sample 11 Sample 18 Sample 6 Sample 11 Sample 18

97

Creep 200 – 300 Pa These tests are performed using a parallel plate geometry that rotate under a constant stress (200 and 300 Pa). This test is specifically used to investigate the slip resistance of adhesives and, in our case, to simulate the slippage test performed with the normed tile (200 Pa) and the Mapei tile (300 Pa) seen before. Below is depicted a multiple line graph obtained from the creep tests carried out on samples 6, 11 and 18 at 200 Pa (Figure 43) and 300 Pa (Figure 44) respectively. % Strain is displayed on the Y-axis (logarithmic) versus time on the X-axis.

Figure 43. Creep test at 200 Pa.

0,01

0,1

1

10

100

1000

0 0,5 1 1,5 2 2,5 3

% Strai

n

Time (min)

Creep 200 Pa

Sample 6 Sample 11 Sample 18

98

Figure 44. Creep test at 300 Pa.

As we can see, the curves follow the same trend but widely differ in strain percentage. The ordinate axis is logarithmic so the values after the plateau differ in three orders of magnitude. Both graphs show that the sample 6 has the highest value of strain, and thus means that it has the lowest slip resistance. Sample 11 on the contrary, has. The sample 6 has a large quantity of cellulose ether but without modifier and sample 11 has only an average quantity of modified cellulose. These two ingredients contribute most for the slip resistance test and give opposite results: the cellulose itself makes cement paste less thick and more fluent; the modified cellu-lose gives solidity to the fresh adhesive. These results are perfectly correlated with those obtained by slips tests: in fact, sample 6 pre-sents a EN slip of 5 mm and Mapei slip of 10 mm; sample 11, 0.1 mm and 0.2 respectively; sample 18, 0.7 mm and 1.2 mm respectively. A more detailed analysis of the results will be discussed using the RSC in which we will be able to understand the influence of all the other ingredients.

0,01

0,1

1

10

100

1000

0 0,5 1 1,5 2 2,5 3

% Strai

n

Time (min)

Creep 300 Pa

Sample 6 Sample 11 Sample 18

99

Dynamic Mechanical Analysis This test is specific to observe the glass transition temperature of the polymer present in the cement adhesive. It is also a test that can be compared to the deformability test seen above, but with much smaller forces involved and without causing the material to break. Among all the 33 samples analyzed, the results of 2, 18 and 11 were selected which have re-spectively a high, medium and no polymer content.

0 10 20 30 40 50Temperatura /°C

6000

8000

10000

12000

14000

16000

18000

20000

22000

E' /MPa

[1.2] M 848 PR17ITAC0210-02.dm2E' (1.0 Hz)

[2.2] M 843 PR17ITAC0210-18.dm2E' (1.0 Hz)

[3.2] M 840 PR17ITAC0210-11.dm2E' (1.0 Hz)

[1.2]

[2.2]

[3.2]

The results demonstrate that the storage modulus G’ gradually decreases with increasing tem-perature but it remains in the same order of magnitude. It is also clear the difference in elastic modulus G’ between the three samples, mostly caused by the amount of cement that generates a more rigid structure. Cellulose ether also contributes, even if in a minor way, to the lowering of the elastic modulus as it acts as a retarder in the cement curing phase as discussed in the theory part of this thesis[48]. One other important consideration concerns the glass transition temperature: it is well evident in the sample 2 around 30°C and less evident but still present in the sample 18, while it does not exist in the sample 11. This test will be evaluated with the deformability results using a PCA, in the next chapter.

100

Principal Component Analysis In situations like these, that is when you have a large amount of data, sometimes redundant, it is very useful to use a PCA in order to get a better view of the information and it is easier to interpret the results. For each test, the significant points of the graphs are chosen, necessary for the realization of the PCA: in the creep test are chosen the last point of the graph for each run, representing the total strain performed by the slip of the adhesive. In the oscillatory test, are chosen G’ and G” at their plateau (maximum value), G’ and G” at 200 and 300 Pa in in such a way as to have a correlation with the results obtained by the creep tests and G’1/2 which is the oscillation rate in which we have a halving of the elastic modulus. This value indicates the stress necessary to flake apart the adhesive. In the flow test are chosen the first point of the curve which represent the yield and the viscosity at shear rate of 1s-1 to compare it with that of Brookfield. In the peak-hold tests, all the runs shown constant values of viscosity therefore the mean values were taken. Using the Principal Component Analysis with the data obtained from the Experimental Design, it is possible to evaluate the correlations among the whole rheological performance profile of cementitious tile adhesives that exist between the various tests performed on the samples with the addition of the pull-off results of the open-time test, obtaining the following result:

101

Figure 45. PCA loading plot.

Figure 46. PCA score plot.

-0.4 -0.2 0.0 0.2 0.4

-0.4-0.2

0.00.2

0.4

Loading Plot (77.9% of total variance)

Component 1 (61.6% of variance)

Comp

onent 2

(16.3%

of var

iance)

EN SlipMapei SlipAdjust.200 Pa300 Pa

G'PlateauG'200 PaG'300 PaG"Plateau

G"200 PaG"300 Pa

G'1/2 (Pa)Yield TA

1s-1TA

Yield AP

1s-1 AP

ph 1s-1ph 5s-1ph 10s-1

O.T. 20'O.T. 30'

+

-5 0 5

-50

5

Score Plot (77.9% of total variance)

Component 1 (61.6% of variance)

Comp

onent 2

(16.3%

of var

iance)

1 2

3 4

5 678

910

11

12

13

14

15

16

17

18

1920

2122

23 24

2526 2728

293031

3233+

A

C B

102

Variance explained by 5 components: 92.19% % Variance explained by each component: 61.59; 16.34; 6.57; 4.17; 3.53. From this PCA we can distinguish three different groups of loadings:

A. peak hold tests and viscosity at 1s-1; B. elastic, viscous modules and yields; C. slip and creep tests, registrability, and open time adhesion tests.

Combining the loading and the score plots, we can deduce that the group A is totally uncorre-lated to the other groups because these tests only represent the viscosity of the adhesive under a specific stress. The viscosity values obtained from these tests are very similar to each other because we have chosen to obtain dough with the same viscosity of Brookfield (by varying water mix ratio) that can be compared to that obtained at 1s-1. The group B has well correlated results because they all depend on the viscous and elastic modulus which, in tile adhesives, follow the same trend. A high viscous or elastic modulus means that also the value of G’1/2 will be high, this is the case of very structured adhesives: a low amount of cellulose ether but with a high content of its modified formula. As we can see, this group is inversely correlated to the group C where are depicted the loadings of open time, slip, creep and registrability tests which are strictly correlated to each other: the adhesive with a high amount of cellulose ether subjected to the extended open time test, will have a high pull-off resistance (thanks to water retention and thus, good wettability), but it will slip more. The slip resistant adhesive, contrariwise, will give high values to the loading group C and low values to the group B, which is bad for open time test (low wettability) and good for slip test. The group C strictly depends to the amount of rheological modifiers. Indeed the score plot highlights that the samples with a high quantity of non-modified cellulose ether are on the left of the plot and those with a high quantity of modified cellulose, on the right.

103

Design of Experiment Results The experimental design based on a fractional factorial design explained in the previous chap-ter, helped us to understand the variations of the performance by varying the concentration of the five variables, which are the ingredient of our formulations. The three-dimensional graphs that we are going to visualize are obtained from the elaboration of the coefficients generated by the R-based chemometric program. Unlike how PCAs may appear, they are easy to read for everyone. We are going to see the response surface curves that express very interesting results and that inspect real problems and on which we can make correlations. Also the dustiness DoE will be analyzed. Before the single discussion, coefficients (from the first and second model) generated from the R-based program and their significance and predictions of verification points are illustrated.

Water Mix Ratio Significance of the coefficient and experimental vs. fitted plot (from R):

Table 17. Coefficients of water mix ratio. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0,75 2,083333 1,166667 -0,41667 0,611111 0,28125 0,09375 0,15625 -0,21875 -0,34375 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0,03125 -0,03125 -0,09375 0,03125 0,34375 0,009894 0,009894 0,259894 0,009894 -0,24011

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-0.50.0

0.51.0

1.52.0

***

***

******

**

**

*

20 22 24 26 28

2022

2426

28

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

34

56

7

8

910

11

12

13

14

1516

17181920

212223

24

25

26

27

28

2930

313233

104

Significance of coefficients: red: > 99,9%; blue: between 99 and 99.9%; green: between 95 and 99%.

Table of variables:

Variable Factor V1 Cement V2 Cellulose ether V3 Modified cellulose ether V4 Polymer V5 Calcium carbonate

Remembering the equation:

= + ∙ 1 + ∙ 2 + ∙ 3 + ∙ 4 + ∙ 5 + ∙ 1 2 + ℎ ∙ 1 3 + + ∙ 1 4 + ∙ 1 5 + ∙ 2 3 + ∙ 2 4 + ∙ 2 5 + ∙ 3 4 + ∙ 3 5 + + ∙ 4 5 + ∙ 1 + ∙ 2 + ∙ 3 + ∙ 4 + ∙ 5

Table 18. Predicted vs. observed values of verification points (first model). Test n° Predicted (%) Observed (%) Difference (%) Confidence interval (%)

30 24.8 24.5 0.3 ± 7.2 31 24.3 23.5 0.8

32 24.0 23.0 1.0 33 24.1 23.0 1.1

Table 19. Coefficients of water mix ratio. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0,775337 2,083333 1,188128 -0,43314 0,609214 0,28125 0,09375 0,15625 -0,21875 -0,34375

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0,03125 -0,03125 -0,09375 0,03125 0,34375 0,00576 0,119778 0,273202 0,045627 -0,13876

105

Table 20. Predicted vs. observed values of verification points (second model). Test n° Predicted Observed

30 24.6 % 24.5 % 31 24.1 % 23.5 % 32 23.8 % 23.0 % 33 23.9 % 23.0 %

RSC and Discussion

The water mix ratio was chosen so as to obtain the right viscosity range in order to have a good workability of the dough. The graphs show interesting results:

Cement, filler and modified cellulose increase the demand of water necessary to obtain the right viscosity. Cement uses water for its hydration reactions and thus; the fine car-bonate is high hygroscopic and requires water so as to give creaminess to the mixture; the modified cellulose, on the other hand, makes the mixture less workable and more viscous, so the adhesive requires more water necessary obtain a good workability.

Cellulose ether and polymer, on the contrary, lower the quantity of water. In particular, the cellulose, thanks to its capacity to “retain” water, incorporates water into its molec-ular structure but it does not withdraw it from the fresh mixture and thus improves its workability. Also the polymer, although less than cellulose, optimizes the workability of the dough because polymer particles act as bearings, and the adhesive is redispersible and able to occlude air.

106

Figure 47. Water mix ratio: filler amount vs. cement (top left); polymer amount vs. cellulose ether

(top right); CE modification vs. cement (bottom).

Specific Gravity Significance of the coefficient and experimental vs. fitted plot (from R):

Table 21. Coefficients of specific gravity. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0,08 -0,06722 0,050556 -0,02778 0,065556 -0,01188 0,001875 0,010625 -0,01313 -0,00688 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0,006875 -0,00687 0,008125 -0,00062 -0,01188 0,02095 0,01595 -0,00405 0,00095 -0,01905

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-0.05

0.00

0.05

***

***

***

***

***

1.3 1.4 1.5 1.6 1.7 1.8

1.31.4

1.51.6

1.71.8

Experimental vs. Fitted Values

Experimental Value

Fitted

Value 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

1718 19

20

2122

2324

2526

2728

29

30313233

107

Table 22. Predicted vs. observed values of verification points (first model). Test n° Predicted (N) Observed (N) Difference (N) Confidence interval (N)

30 1.54 1.51 0.03 ± 0.05 31 1.50 1,46 0.04

32 1.49 1.48 0.01 33 1.48 1.49 -0.01

Table 23. Coefficients of specific gravity. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.079628 -0.06722 0.050675 -0.02876 0.06479 -0.01188 0.001875 0.010625 -0.01312 -0.00688

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² 0.006875 -0.00688 0.008125 -0.00062 -0.01188 0.025151 0.018479 -0.00206 -0.00096 -0.01997

Table 24. Predicted vs. observed values (second model).

Test n° Predicted (N) Observed (N) 30 1.54 1.51 31 1.50 1.46 32 1.48 1.48 33 1.48 1.49

RSC and Discussion

The model highlights that: Cement, filler and CE modification increase the specific gravity: the cement and the

filler have a high density compared to the other ingredients, increasing in fact the spe-cific gravity of the fresh mix. The modified cellulose ether compacts the dough, increas-ing its specific gravity.

Cellulose ether reduces the specific gravity because it preserves the water inside the mixture thus decreasing its density.

108

Figure 48. Specific gravity RSC: cement amount vs. CE modification (left) and cellulose amount vs.

filler amount (right).

109

EN Slip Significance of the coefficient and experimental vs. fitted plot (from R):

Table 25. Coefficients of EN slip. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.036667 0.260611 -0.51194 0.177611 -0.0495 -0.02356 -0.02356 -0.04181 0.053938 -0.08544 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.12344 -0.00794 -0.00419 -0.00794 0.049063 -0.08467 -0.03617 0.074832 0.065832 -0.03617

Table 26. Predicted vs. observed values of verification points (first model). Test n°

Predicted (mm)

Observed (mm)

Difference (mm)

Confidence interval (mm)

30 0.7 1.0 -0.3 ±0.26 31 1.0 1.8 -0.8

32 1.5 2.2 -0.7 33 0.7 0.9 -0.2

Table 27. Coefficients of EN slip. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.035333 0.260562 -0.51525 0.183117 -0.04709 -0.02355 -0.02355 -0.04181 0.053927 -0.0854

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0.12346 -0.00793 -0.00418 -0.00793 0.049062 -0.09975 -0.05731 0.068481 0.069451 -0.04651

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.4

-0.20.0

0.2

***

***

***

***

-1.0 -0.5 0.0 0.5

-1.0-0.5

0.00.5

Experimental vs. Fitted Values

Experimental ValueFitt

ed Va

lue

1

2

3

4

56

7

8

9

10

11

12

1314

1516

171819 202122

23

24

2526

27

2829 30

3132

33

110

Table 28. Predicted vs. observed values (second model). Test n° Predicted (mm) Observed (mm)

30 0.8 1.0 31 1.1 1.8 32 1.7 2.2 33 0.8 0.9

RSC and Discussion

Slippage of cementitious tile adhesives is strongly influenced mainly by the amount of cellulose ether and by its modification, with very reproducible results obtained modeling (Figure 49) with 10% of filler and 30% of cement). The ability to retain water makes the adhesive softer and less thick and, as we will also see in rheological tests, this feature compromises a good slip result. Furthermore, it can be deducted that the amount of cement and filler have almost negligible effects on this kind of test, while a big amount of Polymer (> 4%) can determinate an increase of slippage even in highly modified system, where it becomes necessary to reduce the total amount of cellulose ether in order to respect the “T” classification of adhesives (slippage < 0.5 mm).

Figure 49 . Cellulose ether modification vs. cellulose ether. amount (left) and vs. polymer amount

(right) in slip testing.

111

Mapei Slip Coefficient plot and experimental vs. fitted plot (from R):

Table 29. Coefficients of Mapei slip. Model without verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.035722 0.254111 -0.53328 0.228278 -0.06933 -0.02275 -0.01288 -0.04325 0.115875 -0.06838

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0.174 0.016375 -0.06463 0.0065 0.036875 -0.16137 0.105132 0.094632 -0.00237 -0.05587

Table 30. Predicted vs. observed values of verification points (first model).

Test n°

Predicted (mm)

Observed (mm)

Difference (mm)

Confidence interval (mm)

30 1.5 2.6 -1.1 ± 0.2 31 2.1 3.4 -1.3

32 3.2 10.0 -6.8 33 1.4 1.5 -0.1

Table 31. Coefficients of Mapei slip. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.036911 0.254113 -0.54473 0.232003 -0.06442 -0.02278 -0.01288 -0.04326 0.115867 -0.06834

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0.17398 0.016382 -0.0646 0.006484 0.036864 -0.19839 0.073166 0.114202 -0.01761 -0.06575

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.6

-0.4-0.2

0.00.2

0.4

***

***

***

**

-0.5 0.0 0.5 1.0

-0.50.0

0.51.0

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

56

7

89

10

11

12

1314

1516

171819 202122

23

24

25

26

27

2829

3031

32

33

112

Table 32. Predicted vs. observed values (second model). Test n° Predicted (mm) Observed (mm)

30 0.8 2.6 31 1.1 3.4 32 1.7 10.0 33 0.8 1.5

RSC and Discussion

The slip test performed with the more porous, bigger and heavier tile is a more drastic test that wants to simulate a more critical situation. The results are totally comparable to the previous one but the values are obviously higher (Figure 50).

Figure 50. Cellulose ether modification vs. cellulose ether. amount (left) and vs. polymer amount

(right) in Mapei slip testing.

113

Creep Tests At 200 Pa

Coefficient plot and experimental vs. fitted plot (from R):

Table 33. Coefficients of creep at 200 Pa. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

-0.12151 0.270305 -0.78182 0.251342 -0.09629 -0.01234 0.072768 -0.03676 -0.1298 -0.05617 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.0778 -0.0538 0.075958 -0.02624 -0.04985 -0.0542 -0.01377 0.355235 -0.16428 -0.11033

Table 34. Predicted vs. observed values of verification points (first model). Test n° Predicted (%) Observed (%) Difference (%) Confidence interval (%)

30 5.6 7.2 -1.6 ± 3.9 31 8.0 12.2 -4.2

32 20.0 41.5 -21.5 33 7.4 8.8 -1.4

Table 35. Coefficients of creep at 200 Pa. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 -0,12281 0,270305 -0,78953 0,248151 -0,09404 -0,01234 0,072768 -0,03676 -0,1298 -0,05617

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0,0778 -0,0538 0,075958 -0,02624 -0,04985 -0,06195 -0,02737 0,376314 -0,19224 -0,1138

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-0.8-0.6

-0.4-0.2

0.00.2

0.4

***

***

******

** *** **

*

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5-1.0

-0.50.0

0.51.0

1.52.0

2.5

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

56

7

8

910

11

12

13

14

1516

171819 2021

22

23

24

25 26

2728

29 3031

32

33

114

Table 36. Predicted vs. observed values (second model). Test n° Predicted (%) Observed (%)

30 6.0 7.2 31 8.5 12.2 32 21.9 41.5 33 7.9 8.8

At 300 Pa

Coefficient plot and experimental vs. fitted plot (from R):

Table 37. Coefficients of creep at 300 Pa. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

-0.13321 0.260452 -0.80253 0.237441 -0.09842 0.044863 0.109411 -0.04542 -0.10148 -0.06035 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.09077 -0.01482 0.080802 -0.00224 -0.01161 -0.0759 -0.09141 0.424269 -0.15433 -0.09507

Table 38. Predicted vs. observed values of verification points (first model). Test n° Predicted (%) Observed (%) Difference (%) Confidence interval (%)

30 13 17.6 -4.6 ± 8,7 31 18.5 28.8 -10.3

32 49.6 107 -57.4 33 17.2 22.5 -5.3

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-0.50.0

0.5

***

***

***

**

**

**

*

-0.5 0.0 0.5 1.0 1.5 2.0 2.5

-0.50.0

0.51.0

1.52.0

2.5

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

56

7

8

910

11

12

13

14

1516

171819 2021

22

23

24

25 26

272829

3031

32

33

115

Table 39. Coefficients of creep at 300 Pa. Model with verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

-0.13548 0.260452 -0.81058 0.234011 -0.09578 0.044863 0.109411 -0.04542 -0.10148 -0.06035 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.09077 -0.01482 0.080802 -0.00224 -0.01161 -0.08176 -0.10748 0.444427 -0.18583 -0.09926

Table 40. Predicted vs. observed values (from second model). Test n° Predicted (%) Observed (%) 30 14.1 17.6 31 19.7 28.8 32 54.9 107 33 18.6 22.5

RSC and Discussion

Creep test are performed at 200 and 300 Pa in order to simulate the same stress at which the adhesive is subjected during the EN and Mapei slip tests respectively. We thus expect to see the same behavior of the ingredients saw before in the RSC of the EN and Mapei slip test (Figure 49 and Figure 50):

Figure 51. CE modification vs polymer (left) and vs. cellulose (right) in creep test at 200 Pa.

116

Figure 52. Cement vs. cellulose (left); filler vs. polymer (right) in creep test at 300 Pa.

In fact, the graphs show that:

As seen also in the slip test, cellulose ether strongly enhances the strain % and its mod-ification strongly diminishes it (Figure 51 on the right with 30% of cement 3% of pol-ymer and 10% of filler).

Cement and filler tend to decrease the strain %, especially when there is a high amount of cellulose ether and polymer (Figure 52 with, on the left, average CE modification, 3% of polymer and 10% of filler; on the right with 30% of cement, 0.3% of cellulose and 0.3% of cellulose).

Polymer strongly enhances the strain % only when there is a high quantity of CE mod-ifier (Figure 51 on the left).

117

Adjustability Coefficient plot and experimental vs. fitted plot (from R):

Table 41. Coefficients of adjustability. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

-1.94444 8.666667 -11.5556 4.944444 -3.05556 -0.25 2.5 0.375 1.25 -2.75 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.625 -0.25 -3.375 2.5 1.625 -2.09367 5.406332 1.406332 -1.09367 -2.09367

Table 42. Predicted vs. observed values of verification points (first model). Test n°

Predicted (min)

Observed (min)

Difference (min)

Confidence interval (min)

30 18 25 7 ± 5 31 22 30 8

32 26 35 9 33 20 20 0

Table 43. Coefficients of adjustability. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 -1.87291 8.666667 -12.1304 5.092175 -2.92776 -0.25 2.5 0.375 1.25 -2.75

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0.625 -0.25 -3.375 2.5 1.625 -3.72868 4.093238 2.679953 -1.74197 -2.83167

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-15

-10-5

05

10 ***

***

*

0 10 20 30 40 50

010

2030

4050

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

34

56

7

89

10

1112

13

14

151617 18 1920

2122

23

24

2526

27

2829

3031

32

33

118

Table 44. Predicted vs. observed values (second model). Test n° Predicted (min) Observed (min)

30 20 25 31 25 30 32 29 35 33 23 20

RSC and Discussion

From the graph, we can see that the ingredients that have the main role are cellulose and its modification.

Cellulose greatly increases the setting time, while its modification decreases it in all cases (Figure 53 on the left).

Cement and filler play the same role diminishing the setting time but only when there is a low amount of CE modification (Figure 53 on the right with 0.35% of cellulose, low amount of modified CE and 2% of polymer).

Figure 53. CE mod. vs. cellulose amount (left); filler vs. cement amount (right).

119

Figure 54. Polymer vs. cellulose amount.

Polymer always increases setting time but less than cellulose ether and especially when

there is a high amount of modified CE and low amount of cellulose ether (Figure 54 with high amount of modified CE, 0.25% of cellulose and 30% of cement).

120

24 h Coefficient plot and experimental vs. fitted plot (from R):

Table 45. Coefficients of 24h. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.166667 -0.02889 -0.14444 0.202222 -0.00611 0.03625 0.00375 0.04625 -0.06 -0.02125 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.02625 0.06 0.06375 0.06 0.0175 -0.00525 -0.08525 0.004749 -0.18525 -0.06025

Table 46. Predicted vs. observed values of verification points (first model). Test n°

Predicted (N/mm2)

Observed (N/mm2)

Difference (N/mm2)

Confidence interval (N/mm2)

30 1.07 1.14 -0.07 ± 0.17 31 1.14 1.22 -0.08

32 1.16 1.11 0.05 33 1.00 0.88 0.12

Table 47. Coefficients of 24h. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.169905 -0.02889 -0.14312 0.204314 -0.00421 0.03625 0.00375 0.04625 -0.06 -0.02125

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² 0.02625 0.06 0.06375 0.06 0.0175 -0.01886 -0.08429 -0.00026 -0.17488 -0.05072

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.3

-0.2-0.1

0.00.1

0.2 ***

***

***

0.2 0.4 0.6 0.8 1.0 1.2 1.4

0.20.4

0.60.8

1.01.2

1.4

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

5

6

7

8

910

11

12

13

14

15

16171819

20 21

22

23

24

2526

2728

29

303132

33

121

Table 48. Predicted vs. observed values (second model). Test n° Predicted (N/mm2) Observed (N/mm2)

30 1.07 1.14 31 1.14 1.22 32 1.16 1.11 33 1.00 0.88

RSC and Discussion

In this test we can see the influence of different ingredients (Figure 55 with 0.25% of cellulose, 10% of filler, 2.5% of polymer on the left; 0.30% of cellulose, 2.5% of polymer and low CE modification on the right) (Figure 56 with 2.5% of polymer, 10% of filler and average quantity of modified CE on the left; 20% of cement, 45% of cellulose ether and average CE modification on the right).

Cellulose ether improves the performance of this test only when there is a high amount of both cement and filler. Cellulose ether retains water and improves the wettability of the adhesive which, in its absence, would be dry after few minutes from its application;

Cement and polymer strongly increase the pull-off resistance: cement increases the me-chanical strength of the adhesive even in 24 hours and the polymer enhances the pull-off strength as a result of the cohesion attained in the polymer/hydrated cement co-ma-trix.

Figure 55. Cement vs. CE modification (left) and vs. filler (right) in 24h testing.

122

CE modification penalize the result because it lowers the wettability of the adhesive. Filler penalizes this pull-off test when it is present a high amount of cement: it with-

draws the water from the paste matrix penalizing the hydration of the cement and the wettability. It also enhances the pull-off values when there is a low amount of cement and a high amount of cellulose ether.

Figure 56. Cellulose vs. cement (left) and filler vs. polymer (right) in 24h testing.

123

Open Time at 5’

Table 49. Coefficients of O.T. at 5’. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

-0.02722 0.187778 -0.16667 0.772222 -0.12667 0.30875 -0.06875 0.0125 -0.1275 -0.1425 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.02375 0.04375 0.16625 0.08625 -0.0275 -0.29289 -0.15789 0.372111 -0.00789 -0.10789

Table 50. Predicted vs. observed values of verification points (first model). Test n°

Predicted (N/mm2)

Observed (N/mm2)

Difference (N/mm2)

Confidence interval (N/mm2)

30 1.83 2.22 -0.39 ± 0.34 31 2.31 2.36 -0.05

32 2.10 2.15 -0.05 33 1.87 2.05 -0.18

Table 51. Coefficients of O.T. at 5’. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 -0.03099 0.187778 -0.16682 0.772433 -0.11757 0.30875 -0.06875 0.0125 -0.1275 -0.1425

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0.02375 0.04375 0.16625 0.08625 -0.0275 -0.29825 -0.18019 0.350492 -0.02924 -0.08924

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-0.6-0.4

-0.20.0

0.20.4

0.60.8 ***

*****

* **

*

0 1 2 3

01

23

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17 1819 2021

22

2324

25

26

272829

30

3132

33

124

Table 52. Predicted vs. observed values (second model). Test n° Predicted (N/mm2) Observed (N/mm2)

30 1.89 2.22 31 2.35 2.36 32 2.14 2.15 33 1.91 2.05

RSC and Discussion

A tile adhesive exposed for five minutes to the air, does not lose enough water by evaporation such as to affect its performance. Indeed open time at 5 minutes is strongly dependent from cement and polymer amount (Figure 57 on the left: 2% of polymer, average content of CE modification, 5% of filler) (for the same reasons seen in 24h test). Cellulose ether improves the mechanical strength only when there is a large amount of cement (>30%) while it decreases when the latter is in low quantities (<23%). CE modification lowers the values when there is a low quantity of polymer and a high quantity of cellulose ether (Figure 57 on the right with 30% of cement, 0.4% of cellulose and 10% of filler).

Figure 57. O.T. 5': cement vs. cellulose amount (left); modified cellulose vs. polymer amount (right).

125

Open Time at 20’ Coefficient plot and experimental vs. fitted plot (from R):

Table 53. Coefficients of O.T. at 20’. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

-0.17111 0.262222 -0.07111 0.275556 -0.19056 -0.02 -0.05 -0.1025 0.06125 0.03875 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.03375 -0.065 0.00125 0.1175 -0.11 0.003879 -0.09612 0.143879 -0.18612 -0.02112

Table 54. Predicted vs. observed values of verification points (first model). Test n°

Predicted (N/mm2)

Observed (N/mm2)

Difference (N/mm2)

Confidence interval (N/mm2)

30 0.61 0.65 -0.04 ± 0.33 31 0.80 0.75 0.05

32 0.78 0.78 0 33 0.80 0.84 -0.04

Table 55. Coefficients of O.T. at 20’. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 -0.17223 0.262222 -0.07104 0.27416 -0.18951 -0.02 -0.05 -0.1025 0.06125 0.03875

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² 0.03375 -0.065 0.00125 0.1175 -0.11 0.007753 -0.0973 0.142371 -0.19358 -0.0176

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.3

-0.2-0.1

0.00.1

0.20.3

***

*** ***

*****

**

****

0.0 0.5 1.0 1.5

0.00.5

1.01.5

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

56

7

8

910

1112

13

14

15

1617 18 19

20

21

22

2324

25

26

2728

29 30

313233

126

Table 56. Predicted vs. observed values (second model). Test n° Predicted (N/mm2) Observed (N/mm2) Difference (N/mm2)

30 0.61 0.65 -0.04 31 0.80 0.75 0.05 32 0.78 0.78 0.01 33 0.80 0.84 -0.04

RSC and Discussion

In O.T. test at 20 minutes, we can see how the quantity of cellulose becomes important in this test (Figure 58 on the left: 4% of polymer, 5% of filler and average CE mod.; on the right: 25% of cement, 0.40% of cellulose, average CE mod.). 20 minutes are enough to form a dry “skin” on the surface of the adhesive when there is not enough cellulose ether and there is a lot of cement. In addition, cement and filler degrade performance in any formulation: they are respon-sible for drying water from the fresh paste due to their high hygroscopicity, decreasing consid-erably the wettability of the adhesive, which is fundamental for extended open time tests.

Figure 58. O.T. 20’: cement vs. cellulose amount (left); filler vs. polymer amount (right).

127

Open Time at 30’ Coefficient plot and experimental vs. fitted plot (from R):

Table 57. Coefficients of O.T. at 30’. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

-0.12278 0.198889 -0.03778 0.161667 -0.14333 -0.0325 -0.0025 -0.04625 0.0325 0.00375 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.065 -0.07625 -0.02 0.04125 -0.055 -0.01735 -0.01235 0.127652 -0.09735 -0.03235

Table 58. Predicted vs. observed values of verification points (first model). Test n°

Predicted (N/mm2)

Observed (N/mm2)

Difference (N/mm2)

Confidence interval (N/mm2)

30 0.23 0.37 -0.14 ± 0.30 31 0.36 0.39 -0.03

32 0.36 0.48 -0.12 33 0.36 0.29 0.07

Table 59. Coefficients of O.T. at 30’. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 -0.12045 0.198889 -0.04067 0.162014 -0.1399 -0.0325 -0.0025 -0.04625 0.0325 0.00375

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² 0.065 -0.07625 -0.02 0.04125 -0.055 -0.03515 -0.01968 0.133326 -0.10312 -0.02422

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.2

-0.10.0

0.10.2

***

*** ***

***

*

*

0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.00.2

0.40.6

0.81.0

1.2

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

56

7

8

910111213

14

151617 1819

20

2122

23

2425

26

2728

2930

31 3233

128

Table 60. Predicted vs. observed values (second model). Test n° Predicted (N/mm2) Observed (N/mm2) Difference (N/mm2)

30 0.25 0.37 -0.12 31 0.38 0.39 -0.01 32 0.38 0.48 -0.10 33 0.37 0.29 0.08

RSC and Discussion

At 30 minutes, a large amount of cellulose is essential to ensure a sufficient wettability of the adhesive. Both the cement and calcium carbonate drastically reduce the mechanical resistance of the adhesive for the same reasons described in the 20-minute test, but this time they are even more accentuated. In fact we can see a slight worsening in this test compared to that of 20’ (Figure 58: same quantities of 20’ test).

Figure 59. O.T. 30’: cement vs. cellulose amount (left); filler vs. polymer amount (right).

129

Initial Adhesion Coefficient plot and experimental vs. fitted plot (from R):

Table 61. Coefficients of initial adhesion. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.312222 -0.00778 -0.24 0.895 -0.07167 0.060625 -0.11438 -0.04813 -0.17438 -0.18188 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.21563 0.190625 0.081875 0.050625 -0.02063 -0.19873 -0.27873 0.291266 -0.12373 -0.20373

Table 62. Predicted vs. observed values of verification points (first model). Test n°

Predicted (N/mm2)

Observed (N/mm2)

Difference (N/mm2)

Confidence interval (N/mm2)

30 2.30 2.33 -0.03 ± 0.39 31 2.81 2.49 0.32

32 2.58 2.22 0.36 33 2.19 2.14 0.05

Table 63. Coefficients of initial adhesion. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.312223 -0.00778 -0.23153 0.88776 -0.06978 0.060625 -0.11438 -0.04813 -0.17438 -0.18188

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0.21563 0.190625 0.081875 0.050625 -0.02063 -0.17543 -0.25542 0.276464 -0.133 -0.17194

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.5

0.00.5

1.0

***

***

** * * *

*

0.5 1.0 1.5 2.0 2.5 3.0 3.5

0.51.0

1.52.0

2.53.0

3.5

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

5

6

7

8 9

10

11

12

13

14

15

16 1718 192021

22

2324

252627

28

2930

3132

33

130

Table 64. Predicted vs. observed values (second model). Test n° Predicted (N/mm2) Observed (N/mm2) Difference (N/mm2)

30 2.26 2.33 -0.07 31 2.75 2.49 0.26 32 2.52 2.22 0.30 33 2.14 2.14 0.00

RSC and Discussion

The depicted figures above show that: Cement and polymer always improve the pull-off resistance in all cases. (Figure 60 on

the left with 0.35% of cellulose, average amount of mod. CE and 10% of filler). Filler tends to lower performance when there is a little amount of cellulose ether (Figure

61).

Figure 60. Cement vs. polymer amount (left) and vs. cellulose amount (right).

131

Cellulose ether improve pull-off results only when there is a low amount of modified

CE, otherwise, it lowers the performance when it is present in more than 0.35% (Figure 49 on the right with low amount of modified CE, 2.5% of polymer and 20% of filler). It also enhances the values when there is a high amount of filler, increasing the wetta-bility of the adhesive.

Figure 61. Cellulose vs. filler amount.

132

Water Immersion At 5’

Coefficient plot and experimental vs. fitted plot (from R):

Table 65. Coefficients of water immersion at 5’. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.275 -0.005 -0.06444 0.066111 -0.09444 -0.01125 -0.02625 -0.015 -0.075 -0.01375 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.0575 0.05 0.045 -0.015 -0.01875 0.024551 -0.00545 0.109551 -0.09545 -0.14045

Table 66. Predicted vs. observed values of verification points (first model). Test n°

Predicted (N/mm2)

Observed (N/mm2)

Difference (N/mm2)

Confidence interval (N/mm2)

30 0.94 1.08 -0.14 ± 0.23 31 1.03 0.97 0.06

32 1.08 1.08 0 33 0.89 0.83 0.06

Table 67. Coefficients of water immersion at 5’. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.276554 -0.005 -0.06446 0.064543 -0.09066 -0.01125 -0.02625 -0.015 -0.075 -0.01375

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0.0575 0.05 0.045 -0.015 -0.01875 0.016412 -0.0066 0.108472 -0.10365 -0.12455

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.3

-0.2-0.1

0.00.1

0.20.3 ***

* *

0.4 0.6 0.8 1.0 1.2 1.4 1.6

0.40.6

0.81.0

1.21.4

1.6

Experimental vs. Fitted Values

Experimental ValueFitt

ed Va

lue

1

2

3

456

78

9

10

11

12

1314

15

1617 181920

2122

23

24

2526 27

28

29

3031

32

33

133

Table 68. Predicted vs. observed values (second model). Test n° Predicted (N/mm2) Observed (N/mm2) Difference (N/mm2)

30 0.94 1.08 -0.14 31 1.03 0.97 0.06 32 1.08 1.08 0.00 33 0.89 0.83 0.06

At 10’

Coefficient plot and experimental vs. fitted plot (from R):

Table 69. Coefficients of water immersion at 10’. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.138889 0.046111 -0.03056 0.051667 -0.11833 0.074375 0.006875 0.001875 -0.07188 -0.03938 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.00937 0.004375 0.033125 -0.00562 -0.00062 0.064881 0.009881 -0.04012 -0.06012 0.009881

Table 70. Predicted vs. observed values of verification points (first model). Test n°

Predicted (N/mm2)

Observed (N/mm2)

Difference (N/mm2)

Confidence interval (N/mm2)

30 0.59 0.69 -0.10 ± 0.24 31 0.66 0.70 -0.04

32 0.65 0.69 -0.04 33 0.59 0.57 0.02

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

***

***

**

**

* *

0.2 0.4 0.6 0.8 1.0 1.2

0.20.4

0.60.8

1.01.2

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

34 5

6

7

8

9

10

11

12

13

14

15

161718 19

20

2122

232425 26

27

28

29

303132

33

134

Table 71. Coefficients of water immersion at 10’. Model with verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.139759 0.046111 -0.03132 0.05255 -0.11592 0.074375 0.006875 0.001875 -0.07188 -0.03938 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.00938 0.004375 0.033125 -0.00562 -0.00062 0.055586 0.0045 -0.04205 -0.06152 0.015354

Table 72. Predicted vs. observed values (second model). Test n° Predicted (N/mm2) Observed (N/mm2) Difference (N/mm2)

30 0.60 0.69 -0.09 31 0.67 0.70 -0.03 32 0.66 0.69 -0.03 33 0.60 0.57 0.03

At 15’

Coefficient plot and experimental vs. fitted plot (from R):

Table 73. Coefficients of water immersion at 15’. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.05 0.052778 -0.03722 0.049444 -0.11889 0.09125 0.0275 -0.05375 -0.0725 -0.02375 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.01 0.03125 0.05875 -0.0175 0.00875 -0.11797 -0.03297 0.007032 -0.01297 0.072032

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-0.2-0.1

0.00.1

0.2

***

**

*

0.0 0.2 0.4 0.6 0.8

0.00.2

0.40.6

0.8

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

45

6

7

8

9

1011

1213

1415

16171819

20

2122

2324

2526

27

28

29303132

33

135

Table 74. Predicted vs. observed values of verification points (first model). Test n°

Predicted (N/mm2)

Observed (N/mm2)

Difference (N/mm2)

Confidence interval (N/mm2)

30 0.45 0.45 0 ± 0.19 31 0.51 0.52 -0.01

32 0.51 0.49 0.02 33 0.43 0.74 -0.31

Table 75. Coefficients of water immersion at 15’. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.042381 0.052778 -0.03621 0.049197 -0.11932 0.09125 0.0275 -0.05375 -0.0725 -0.02375

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² 0.01 0.03125 0.05875 -0.0175 0.00875 -0.09367 -0.04295 -0.00752 -0.02406 0.060088

Table 76. Predicted vs. observed values (second model).

Test n° Predicted (N/mm2) Observed (N/mm2) Difference (N/mm2) 30 0.47 0.69 0.02 31 0.53 0.70 0.01 32 0.53 0.69 0.04 33 0.47 0.57 -0.27

136

RSC and Discussion Also in this test, cement greatly increases pull-off resistance, especially at 5’. Increasing open time, the adhesive needs more cellulose in order to obtain good performances, especially at 15’ and when the quantity of filler and cement are high (Figure 62, Figure 63, Figure 64 on the left: average quantity of CE mod., 3% of polymer and 15% of filler). Filler penalize all the results at 5’, 10’ and 15’. Cellulose ether modification does not seem to have an important role in this experiment.

Figure 62. Water immersion at 5’: cellulose vs. cement amount (left); and CE mod. vs polymer

amount (right).

Figure 63. Water immersion at 10’: cellulose vs. cement amount (left); and CE mod. vs polymer

amount (right).

137

An interesting fact is that in this test the polymer seems to improve the performance of the adhesive only when there is a high amount of modified CE at 5’, 10’, and especially at 15’. On the other hand, it lowers the results when the quantity of modified CE is lower (Figure 62, Figure 63, Figure 64, on the right with 30% of cement, 0.30% of cellulose and 15% of filler).

Figure 64. Water immersion at 15’: cellulose vs. cement amount (left); and CE mod. vs polymer

amount (right).

138

Heat Aging at 5’ Coefficient plot and experimental vs. fitted plot (from R):

Table 77. Coefficients of heat aging at 5’. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.360556 0.05 -0.07667 1.066111 0.016667 0.1725 0.0025 0.1725 -0.13875 -0.0825 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.0375 0.18625 0.275 0.00875 0.00375 -0.25553 -0.03053 0.219472 -0.17553 -0.26053

Table 78. Predicted vs. observed values of verification points (first model). Test n°

Predicted (N/mm2)

Observed (N/mm2)

Difference (N/mm2)

Confidence interval (N/mm2)

30 2.13 2.13 0 ± 0.43 31 2.68 2.58 0.10

32 2.28 2.10 0.18 33 1.95 1.75 0.20

Table 79. Coefficients of heat aging at 5’. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.364963 0.05 -0.0726 1.064273 0.017379 0.1725 0.0025 0.1725 -0.13875 -0.0825

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0.0375 0.18625 0.275 0.00875 0.00375 -0.25917 -0.01433 0.217344 -0.16761 -0.24113

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.5

0.00.5

1.0

***

***

*** * *

0 1 2 3 4

01

23

4

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

1718 1920 21

22

23

2425

2627

28

2930

31

3233

139

Table 80. Predicted vs. observed values (second model). Test n° Predicted (N/mm2) Observed (N/mm2) Difference (N/mm2)

30 2.10 2.13 -0.03 31 2.65 2.58 0.07 32 2.25 2.10 0.15 33 1.91 1.75 0.16

RSC and Discussion

From the graph shown in Figure 65, we can see that: Filler slightly improves the overall performance of the adhesive, but excessive amounts

(> 7% ÷ 10%) strongly penalize the obtained results. Cement and polymer amount are fundamental, as they strongly increase the perfor-

mances as seen before in the 24h and O.T. 5’ tests. Cellulose does not seem to improve performance in this test, indeed, with little amount

of cement and calcium carbonate, it tends to worsen the mechanical strength due to its retarding action against cement hardening48.

Cellulose ether modification is always negative in any formulation.

Figure 65. Heat aging at 5’: cellulose vs. cement amount (left); filler vs. polymer amount (right).

140

Heat Aging at 10’ Coefficient plot and experimental vs. fitted plot (from R):

Table 81. Coefficients of heat aging at 10’. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.158889 0.444444 -0.18944 0.58 -0.22222 0.119375 0.059375 -0.01313 -0.06938 -0.10938 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.215625 0.144375 0.003125 0.056875 -0.16313 -0.05113 -0.14113 0.113865 -0.21113 -0.20113

Table 82. Predicted vs. observed values of verification points (first model). Test n°

Predicted (N/mm2)

Observed (N/mm2)

Difference (N/mm2)

Confidence interval (N/mm2)

30 1.21 1.59 -0.38 ± 0.45 31 1.61 1.33 0.28

32 1.50 1.34 0.16 33 1.28 1.17 0.11

Table 83. Coefficients of heat aging at 10’. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.161609 0.444444 -0.18552 0.572776 -0.21194 0.119375 0.059375 -0.01313 -0.06938 -0.10938

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² 0.215625 0.144375 0.003125 0.056875 -0.16313 -0.05732 -0.13508 0.102246 -0.23759 -0.14882

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.4

-0.20.0

0.20.4

0.6

******

** **

*** *

*

0.0 0.5 1.0 1.5 2.0 2.5 3.0

0.00.5

1.01.5

2.02.5

3.0

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

34

5

6

7

8

910

11

12

13

14

15

16

17 181920

21

22

2324

25

26

27

28

2930

313233

141

Table 84. Predicted vs. observed values (second model). Test n° Predicted (N/mm2) Observed (N/mm2) Difference (N/mm2)

30 1.22 1.59 -0.37 31 1.59 1.33 0.26 32 1.48 1.34 0.14 33 1.27 1.17 0.10

RSC and Discussion

In this test we can clearly see the differences between this test and that one at 5 minutes of open time:

Cellulose ether plays an important role and becomes necessary for the adhesive to have good mechanical strength.

Cement, also in this test, improves the performance but only when a sufficient amount of cellulose is present, in order to maintain a good wettability of the adhesive.

Calcium carbonate, this time, worsens the resistance immediately, even with few quan-tities.

Polymer always increases pull-off resistance. Also here, cellulose ether modification is always negative in any formulation.

Figure 66. Heat aging at 10’: cellulose vs. cement amount (left); filler vs. polymer amount (right).

142

Heat Aging at 15’ Coefficient plot and experimental vs. fitted plot (from R):

Table 85. Coefficients of heat aging at 15’. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

-0.06167 0.199444 -0.15167 0.431111 -0.35611 0.025 -0.0175 -0.0325 -0.10125 -0.12625 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.08375 -0.0525 -0.09125 0.035 -0.2775 -0.18365 -0.14365 0.276346 0.111346 -0.37365

Table 86. Predicted vs. observed values of verification points (first model). Test n°

Predicted (N/mm2)

Observed (N/mm2)

Difference (N/mm2)

Confidence interval (N/mm2)

30 0.55 0.77 -0.22 ± 0.49 31 1.06 1.36 -0.30

32 0.96 0.86 0.10 33 0.80 0.86 -0.06

Table 87. Coefficients of heat aging at 15’. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 -0.06239 0.199444 -0.14813 0.438249 -0.35097 0.025 -0.0175 -0.0325 -0.10125 -0.12625

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² 0.08375 -0.0525 -0.09125 0.035 -0.2775 -0.19638 -0.15961 0.244488 0.127511 -0.36648

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.8

-0.6-0.4

-0.20.0

0.20.4

0.6

***

*** **

*

0.0 0.5 1.0 1.5 2.0 2.5

0.00.5

1.01.5

2.02.5

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

45

6

7

8

91011

12

13

14

1516

17 181920

21

22

2324

25

26

2728

29 30

313233

143

Table 88. Predicted vs. observed values (second model). Test n° Predicted (N/mm2) Observed (N/mm2) Difference (N/mm2)

30 0.58 0.77 -0.19 31 1.10 1.36 -0.26 32 0.99 0.86 0.13 33 0.83 0.86 -0.03

RSC and Discussion

As observed many times during past experimental campaigns carried on by the R&D laborato-ries of Mapei Group, heat aging can be a very critical test for cementitious tile adhesives, espe-cially if this test is combined with open time[47]. (Figure 67)

The amount of cellulose ether improves the overall performance in this test, guarantee-ing a good wettability of tiles.

Cement improves the characteristics of the adhesive only up to a certain quantity (up to 25%) after which it gets worse due to its high hygroscopicity.

Filler, as seen in the previous test, penalize the result anyway.

Figure 67. Heat aging at 15’: cellulose vs. cement amount (left); filler vs. polymer amount (right).

144

Also in this test, polymer improves the overall performance. Cellulose ether modification is always negative in any formulation (Figure 68).

Figure 68. Cement vs. CE modification.

145

Freeze/Thaw Cycles Coefficient plot and experimental vs. fitted plot (from R):

Table 89. Coefficients of freeze/thaw cycles. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.466667 -0.07111 -0.02056 0.015 0.055 -0.04 0.06625 0.085 0.02875 0.12 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.09125 0.065 -0.015 -0.02875 0.0375 -0.02222 -0.03222 0.012784 -0.04722 -0.07722

Table 90. Predicted vs. observed values of verification points (first model). Test n°

Predicted (N/mm2)

Observed (N/mm2)

Difference (N/mm2)

Confidence interval (N/mm2)

30 1.69 1.49 0.20 ± 0.39 31 1.68 1.53 0.15

32 1.70 1.38 0.32 33 1.44 1.16 0.28

Table 91. Coefficients of freeze/thaw cycles. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.472601 -0.07111 -0.01376 0.012665 0.051269 -0.04 0.06625 0.085 0.02875 0.12

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² 0.09125 0.065 -0.015 -0.02875 0.0375 -0.01723 -0.00053 0.013897 -0.02604 -0.06232

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.4

-0.20.0

0.20.4

***

0.5 1.0 1.5 2.0 2.5

0.51.0

1.52.0

2.5

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

23

4

5

678

910

11 12

13 1415

16

17 181920

21222324 2526

27

28

29

303132

33

146

Table 92. Predicted vs. observed values (second model). Test n° Predicted (N/mm2) Observed (N/mm2) Difference (N/mm2)

30 1.62 1.49 0.13 31 1.61 1.53 0.08 32 1.62 1.38 0.24 33 1.37 1.16 0.21

RSC and Discussion

This test, in contrast with the others, behaves differently. In the case the buttering and floating technique is used: the adhesive is applied to the tile and substrate and the skin that forms on both surfaces is almost completely broken up when the wetted tile is laid in the adhesive, thus, the cellulose does not improve the performance. The RSC (in Figure 69 with: 20% of cement, 2.5% of polymer and 10% of filler on the left; 0.45% of cellulose, average quantity of CE mod., 10% of filler on the right) shows that:

Cement is the only ingredient that contributes more to the increase in performance. CE modification penalize pull-off test when there is a low amount of cellulose ether and

cement. Polymer only improves the performance when there is a high amount of cement and

cellulose and, anyway, it does not improve the overall performance of this test as ex-pected.

Figure 69. CE mod. Vs. cellulose amount (left); cement vs. polymer amount (right).

147

Deformability Coefficient plot and experimental vs. fitted plot (from R):

Table 93. Coefficients of deformability. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

-0.11 0.11 -0.04444 0.718333 -0.04278 0.018125 0.029375 -0.20813 0.078125 0.023125 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.010625 -0.00813 0.056875 0.005625 -0.07438 -0.07612 0.063879 -0.08612 0.128879 -0.01112

Table 94. Predicted vs. observed values of verification points (first model). Test n°

Predicted (mm)

Observed (mm)

Difference (mm)

Confidence interval (mm)

30 2.25 2.30 -0.05 ± 0.20 31 2.67 2.62 0.05

32 2.28 2.29 -0.01 33 2.31 2.22 0.09

Table 95. Coefficients of deformability. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 -0.10763 0.11 -0.04487 0.717148 -0.04142 0.018125 0.029375 -0.20813 0.078125 0.023125

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² 0.010625 -0.00813 0.056875 0.005625 -0.07438 -0.0838 0.066869 -0.08121 0.126535 -0.00204

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.2

0.00.2

0.40.6

******

***

***

***** *

* *

1.5 2.0 2.5 3.0 3.5

1.52.0

2.53.0

3.5

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

1718 192021

22

23

24252627

2829

30

31

3233

148

Table 96. Predicted vs. observed values (second model). Test n° Predicted (N/mm2) Observed (N/mm2) Difference (N/mm2)

30 2.25 2.30 -0.05 31 2.66 2.62 0.04 32 2.27 2.29 -0.02 33 2.31 2.22 0.09

RSC and Discussion

Deformability test can be affected by many factors (as illustrated in Figure 70). Cement generates a more rigid structure with a consequent decrease in deformability. Polymer guarantees a strong increase of deformability in all cases. The amount of cellulose ether can also give a little contribution to tile adhesives de-

formability because it delays the hardening of the cement, lowering its elastic modulus. Fine calcium carbonate lowers both the deformability and the breaking load as it makes

the adhesive more fragile and brittle.

Figure 70. Cement vs. polymer amount (left) and filler vs. cellulose amount (right) in deformability

test.

149

Deformability Load Coefficient plot and experimental vs. fitted plot (from R):

Table 97. Coefficients of deformability load. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

3.006111 -1.41167 -0.15389 1.129444 0.717222 -0.68625 0.2325 -0.60625 -0.2025 -0.155 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.07625 -0.6725 -0.2175 0.04125 -0.235 -0.97964 -0.26964 1.250356 -0.82964 0.710356

Table 98. Predicted vs. observed values of verification points (first model). Test n° Predicted (N) Observed (N) Difference (N) Confidence interval (N)

30 10.5 8.66 1.84 ± 0.34 31 10.3 7.79 2.51

32 10.3 8.84 1.46 33 8.21 7.46 0.75

Table 99. Coefficients of deformability load. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 3.01464 -1.41167 -0.12523 1.073732 0.679878 -0.68625 0.2325 -0.60625 -0.2025 -0.155

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0.07625 -0.6725 -0.2175 0.04125 -0.235 -0.79843 -0.05004 1.340977 -0.86075 0.761907

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-2

-10

12

3 ***

***

***

*

4 6 8 10 12 14 16

46

810

1214

16

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

12

3

4

5

6

7

8

9

1011

12

13

14

1516171819 20

21

22

2324 2526

27

28

29

3031 32

33

150

Table 100. Predicted vs. observed values (second model). Test n° Predicted (N) Observed (N) Difference (N)

30 10 8.66 1.34 31 9.8 7.79 2.01 32 9.87 8.84 1.03 33 7.77 7.46 0.31

RSC and Discussion

Deformability load is influenced from different ingredients: Quantity of cement increase the rigidity of the structure which can therefore tolerate a

higher load before it breakdowns. Fine calcium carbonate generates a more brittle structure, but it seems to increase the

deformability load when there is a little amount of cellulose ether and cement. For the same reason described above, the breaking load decreases with the increase of

cellulose because the cement is gradually less hardened. Polymer increases the tolerance to the breaking load, making the adhesive structure less

fragile.

Figure 71. Cement vs. polymer amount (left) and filler vs. cellulose amount (right) in deformability

load test.

151

DMA ΔE’ (5-50 °C) Coefficient plot and experimental vs. fitted plot (from R):

Table 101. Coefficients of ΔE’. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

187.6667 -319.833 10.27778 1040.833 -43.7778 -110 -112.25 -47.125 -49.125 -38.75 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-130.625 -66.875 75.875 -54.625 -49.75 -268.15 -78.6504 320.3496 -434.65 233.8496

Table 102. Predicted vs. observed values of verification points (first model). Test n°

Predicted (MPa)

Observed (MPa)

Difference (MPa)

Confidence interval (MPa)

30 2064 1470 594 ± 324 31 2439 2040 399

32 2102 1605 497 33 1866 1530 336

Table 103. Coefficients of ΔE’. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 193.4795 -319.833 20.36491 1033.356 -56.433 -110 -112.25 -47.125 -49.125 -38.75

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -130.625 -66.875 75.875 -54.625 -49.75 -233.557 -17.8989 335.709 -407.547 237.6525

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-50

00

500100

0

***

***

*

*

1000 2000 3000 4000

1000

2000

3000

4000

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16171819

2021

22

23242526

2728

2930

31

3233

152

Table 104. Predicted vs. observed values (second model). Test n° Predicted (MPa) Observed (MPa) Difference (MPa)

30 1926 1470 456 31 2309 2040 269 32 1968 1605 363 33 1739 1530 209

RSC and Discussion

This test is specific to observe the glass transition temperature of the polymer present in the cement adhesive. By looking at the graph of this test in the introductory chapter, the ΔE’ value is given by the difference between the elastic modulus at 5 °C minus the one taken at 50 °C. In particular:

The polymer is the ingredient that most influences this result: the polymers, in fact, undergo the glass transition after a certain temperature, becoming more rubbery and viscous, with a lower modulus. The glass transition temperature of the polymer used in this work is about 30 °C. This suggests to us why ΔE’ increases as the polymer increases in quantity.

Cellulose and cement also tend to increase the ΔE’ even if much less than the polymer. Calcium carbonate does not seem to affect the results in an important way.

153

Figure 72. Difference of the module between 5 and 50 °C.

These results are related to those of deformability. An adhesive that contains a lot of polymer will have a module that varies greatly due to the glass transition and will have a high deforma-bility value (Figure 70).

154

Stress Sweep: G’ and G” at 200 and 300 Pa G’ at 200 Pa

Coefficient plot and experimental vs. fitted plot (from R):

Table 105. Coefficients of G’ at 200 Pa. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.085761 -0.28223 0.533392 -0.33182 0.088839 -0.02943 -0.04756 0.082685 0.201885 -0.06919 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.105375 0.036587 -0.09113 -0.03127 0.035843 0.121995 0.144874 -0.29781 0.026969 0.186669

Table 106. Predicted vs. observed values of verification points (first model). Test n°

Predicted (kPa)

Observed (kPa)

Difference (kPa)

Confidence interval (kPa)

30 21.7 14.0 7.7 ± 5.2 31 12.2 13.0 -0.8

32 8.0 7.3 0.7 33 17.2 15.0 2.2

Table 107. Coefficients of G’ at 200 Pa. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.08719 -0.28223 0.534396 -0.32546 0.083864 -0.02943 -0.04756 0.082685 0.201885 -0.06919

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² 0.105375 0.036587 -0.09113 -0.03127 0.035843 0.117337 0.146645 -0.30056 0.057345 0.166053

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.4

-0.20.0

0.20.4

0.6

***

***

***

*****

**

* * **

0.5 1.0 1.5 2.0 2.5 3.0

0.51.0

1.52.0

2.53.0

Experimental vs. Fitted Values

Experimental ValueFitt

ed Va

lue

1

2

3

4

5

6

7

8910

11

12

13

14

1516

1718192021

22

23

24

2526

272829

3031

32

33

155

Table 108. Predicted vs. observed values (second model). Test n° Predicted (kPa) Observed (kPa) Difference (kPa)

30 21.1 14.0 7.1 31 12.4 13.0 -0.6 32 7.9 7.3 0.6 33 16.9 15.0 1.9

G’ at 300 Pa

Coefficient plot and experimental vs. fitted plot (from R):

Table 109. Coefficients of G’ at 300 Pa. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.065826 -0.27122 0.536981 -0.323 0.06945 -0.01129 -0.09797 0.121879 0.17587 -0.06876 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.138689 0.04847 -0.08054 -0.03546 0.072024 0.180241 0.14495 -0.38951 0.039524 0.177841

Table 110. Predicted vs. observed values of verification points (first model). Test n°

Predicted (kPa)

Observed (kPa)

Difference (kPa)

Confidence interval (kPa)

30 12.1 6.8 5.3 ± 0 31 7.1 6.3 0.8

32 4.3 3.7 0.6 33 10.5 7.5 3

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-0.6-0.4

-0.20.0

0.20.4

0.6

***

***

***

** **

**

**

0.0 0.5 1.0 1.5 2.0 2.5 3.0

0.00.5

1.01.5

2.02.5

3.0

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

5

6

7

8 910

11

12

13

14

1516

1718192021

22

23

24

2526

272829

3031

32

33

156

Table 111. Coefficients of G’ at 300 Pa. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.06922 -0.27122 0.538484 -0.31569 0.063143 -0.01129 -0.09797 0.121879 0.17587 -0.06876

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² 0.138689 0.04847 -0.08054 -0.03546 0.072024 0.171538 0.151523 -0.3897 0.078978 0.156031

Table 112. Predicted vs. observed values (second model).

Test n° Predicted (kPa) Observed (kPa) Difference (kPa) 30 11.5 6.8 4.7 31 7.1 6.3 0.8 32 4.2 3.7 0.5 33 9.9 7.5 2.4

G” at 200 Pa

Coefficient plot and experimental vs. fitted plot (from R):

Table 113. Coefficients of G” at 200 Pa. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.105127 -0.27464 0.478048 -0.30494 0.085287 -0.07073 -0.04192 0.073464 0.122075 0.007703 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.050686 0.03331 -0.01697 -0.02138 0.030181 0.10652 0.200063 -0.36241 -0.05801 0.191763

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-0.4-0.2

0.00.2

0.4

***

***

***

** **

**

**

0.0 0.5 1.0 1.5 2.0 2.5

0.00.5

1.01.5

2.02.5

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

5

6

7

8

910

11

12

13

14

1516

171819202122

23

24

25

26

27 2829

30

3132

33

157

Table 114. Predicted vs. observed values of verification points (first model). Test n°

Predicted (kPa)

Observed (kPa)

Difference (kPa)

Confidence interval (kPa)

30 15.5 8.8 6.7 ± 2.5 31 8.6 9.6 -1.0

32 5.9 4.4 1.5 33 11.9 12.0 -0.1

Table 115. Coefficients of G” at 200 Pa. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.104689 -0.27464 0.481119 -0.29983 0.079048 -0.07073 -0.04192 0.073464 0.122075 0.007703

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² 0.050686 0.03331 -0.01697 -0.02138 0.030181 0.114833 0.206406 -0.36988 -0.02867 0.170031

Table 116. Predicted vs. observed values (second model).

Test n° Predicted (kPa) Observed (kPa) Difference (kPa) 30 14.8 8.8 6 31 8.5 9.6 -1.1 32 5.7 4.4 1.3 33 11.6 12.0 -0.4

G” at 300 Pa

Coefficient plot and experimental vs. fitted plot (from R):

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-0.6-0.4

-0.20.0

0.20.4

0.6

***

***

*****

*

0.0 0.5 1.0 1.5 2.0 2.5

0.00.5

1.01.5

2.02.5

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

12

3

4

5

6

7

8910

11

12

13

14

15 16

17181920 21 22

23

24

25

26

27

282930

31

32

33

158

Table 117. Coefficients of G” at 300 Pa. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.030899 -0.30128 0.507589 -0.22696 0.001567 0.01102 -0.0036 0.038927 0.142261 0.07703 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.03355 0.095981 -0.09677 0.041477 0.002138 0.21997 0.193347 -0.45191 -0.01572 0.215366

Table 118. Predicted vs. observed values of verification points (first model). Test n°

Predicted (kPa)

Observed (kPa)

Difference (kPa)

Confidence interval (kPa)

30 9.4 5.5 3.9 ± 5.0 31 6.3 5.2 1.1

32 3.5 2.3 1.2 33 9.0 6.7 2.3

Table 119. Coefficients of G” at 300 Pa. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.033839 -0.30128 0.512071 -0.22 -0.00405 0.01102 -0.0036 0.038927 0.142261 0.07703

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² 0.03355 0.095981 -0.09677 0.041477 0.002138 0.217003 0.20361 -0.46182 0.025833 0.200344

Table 120. Predicted vs. observed values (second model).

Test n° Predicted (kPa) Observed (kPa) Difference (kPa) 30 8.8 5.5 3.3 31 6.2 5.2 1.0 32 3.3 2.3 1.0 33 8.6 6.7 1.9

159

RSC Discussion The results expressed in the next figures, represent how the elastic and viscous modulus at 200 and 300 Pa are influenced by the five ingredients. It can immediately be deduced that the ingredients influence both the modules in the same way at both 200 and 300 Pa. What is different is that G' is always greater than G" both at 200 and at 300 Pa because the oscillatory stress is not yet sufficient to breakdown the structure of the fresh adhesive. At these values therefore, the elastic behavior prevails over that viscous one. At 300 Pa both the modulus are always lower than those at 200 Pa (in equal formulations). In particular, we can deduce that:

CE modification greatly increase, in all formulation, G’ and G” at 200 and 300 Pa (Fig-ure 73 with: 30% of cement, 3% of polymer and 10% of filler. Figure 74 with: 30% of cement, 0.35% of cellulose ether and 10% of filler).

Cellulose ether and polymer, on the contrary, are the two ingredients that decrease both modules in both tests (Figure 73 and Figure 74).

Figure 73. Elastic (on the left) and viscous (on the right) modulus at 200 Pa.

k k

160

Figure 74. Elastic (on the left) and viscous (on the right) modulus at 300 Pa.

Cement and filler, on the other hand, contribute less than the ingredients mentioned

above: both increase the modules a lot, but only when there is a low amount of polymer and cellulose and a high amount of modification. This because, as previously men-tioned, modification, cellulose and polymer have more influence than cement and car-bonate and therefore tend to cover the effects of the latter (Figure 75 with: high quantity of modified cellulose, 1% of polymer and 15% of filler) (Figure 76 with 35% of cement, 0.30% of cellulose and high quantity of modified cellulose).

Figure 75. Elastic (on the left) and viscous (on the right) modulus at 200 Pa.

k k

k k

161

Figure 76. Elastic (on the left) and viscous (on the right) modulus at 300 Pa.

It is interesting to note that these trends can be compared to those of slips and creeps, but the values they present are opposite. In fact, the ingredients that decrease the value of slips and creeps, increase those of the elastic and viscous modules; while on the contrary, the ingredients that increase the values of the slips and creeps, decrease in turn G' and G".

k k

162

Stress Sweep: G’ and G” at Plateau G’ at Plateau

Coefficient plot and experimental vs. fitted plot (from R):

Table 121. Coefficients of G’ at plateau. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.120186 -0.31524 0.442532 -0.36668 0.153673 0.110642 0.129962 0.125807 -0.03205 -0.15182 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.08885 0.164374 -0.12305 0.153542 0.142202 -0.59759 0.365232 -0.48335 0.319856 0.405515

Table 122. Predicted vs. observed values of verification points (first model). Test n°

Predicted (kPa)

Observed (kPa)

Difference (kPa)

Confidence interval (kPa)

30 100.0 76.0 24.0 ± 36.7 31 52.5 55.0 -2.5

32 30.3 34.0 -3.7 33 41.2 73.0 -31.8

Table 123. Coefficients of G’ at plateau. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.114185 -0.31524 0.441826 -0.36245 0.149834 0.110642 0.129962 0.125807 -0.03205 -0.15182

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0.08885 0.164374 -0.12305 0.153542 0.142202 -0.58256 0.353252 -0.49215 0.326945 0.376261

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.5

0.00.5

***

***

***** **

**

* * ** *

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

0.00.5

1.01.5

2.02.5

3.03.5

Experimental vs. Fitted Values

Experimental ValueFitt

ed Va

lue

12

3

4

5

6

7

8

9

10

11

12

13

14

1516

17181920

2122

23

24

25

26

27

2829

3031

3233

163

Table 124. Predicted vs. observed values (second model). Test n° Predicted (kPa) Observed (kPa) Difference (kPa)

30 104.0 76.0 28.0 31 56.3 55.0 1.3 32 32.1 34.0 -1.9 33 44.4 73.0 -28.6

G” at Plateau

Coefficient plot and experimental vs. fitted plot (from R):

Table 125. Coefficients of G” at plateau. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

-0.05063 -0.16878 0.471022 -0.21344 0.029956 -0.01822 0.007394 0.003484 0.050615 -0.0037 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.00468 0.025391 0.026122 0.02946 0.010267 -0.20684 0.248355 -0.51823 0.211462 0.301675

Table 126. Predicted vs. observed values of verification points (first model). Test n°

Predicted (kPa)

Observed (kPa)

Difference (kPa)

Confidence interval (kPa)

30 45.2 34.0 11.2 ± 18.8 31 32.4 25.0 7.4

32 15.8 18.0 -2.2 33 34.6 32.0 2.6

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-0.50.0

0.5 ***

****

0.5 1.0 1.5 2.0 2.5

0.51.0

1.52.0

2.5

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

5

6

7

8

9

10

11

12

13

14

1516

17 1819

20 2122

23

24

25

26

27

2829

3031

32

33

164

Table 127. Coefficients of G” at plateau. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 -0.04986 -0.16878 0.469432 -0.21244 0.026781 -0.01822 0.007394 0.003484 0.050615 -0.0037

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0.00468 0.025391 0.026122 0.02946 0.010267 -0.20801 0.25065 -0.50879 0.218229 0.289684

Table 128. Predicted vs. observed values (second model).

Test n° Predicted (kPa) Observed (kPa) Difference (kPa) 30 44.2 34.0 10.2 31 32.2 25.0 7.2 32 15.8 18.0 -2.2 33 34.1 32.0 2.1

RSC and Discussion

The following figures describe the variation of the elastic modulus G’ and the viscous modulus G” at the plateau (the highest value of the test) in stress sweep testing. It can be deduced that both elastic and viscous component at their plateau follow the same trend, both behave in the same way when the ingredients change. It should be however noted that it is the elastic modulus to prevail (G’ > G”) over the viscous one because the oscillatory stress is not yet sufficient to breakdown the structure of the fresh adhesive. In particular, the cellulose and the polymer considerably lower the elastic and viscous modulus of the adhesive because they give softness to the fresh paste. The modified cellulose, on the other hand, increases G’ and G” at plateau (Figure 77).

165

Figure 77. Elastic (on the left) and viscous (on the right) modulus at plateau.

This result confirms the correlation previously seen using the PCA: the loadings of the modules are inversely correlated to the slip and creep ones. In fact, it is noteworthy that the graph has the same shape as that of the slips and creeps (fig.). This means that an adhesive with high modules will give low creep and slip values but also low performances in extended open time tests. In addition, cement would seem to increase both modules only up to a certain amount (30%) above which it decreases them. Calcium carbonate, on the other hand, does not seem to make a big contribution (Figure 78).

Figure 78. Elastic (on the left) and viscous (on the right) modulus at plateau.

k k

k k

166

Stress Sweep: G’ ½ Coefficient plot and experimental vs. fitted plot (from R):

Table 129. Coefficients of G’1/2 at plateau. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.074029 -0.14759 0.274744 -0.10299 0.124913 -0.06429 -0.04774 0.065576 0.165061 0.024444 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.06207 0.037412 -0.10542 -0.12522 0.087588 0.112873 0.024828 -0.07723 0.024828 0.024828

Table 130. Predicted vs. observed values of verification points (first model). Test n°

Predicted (kPa)

Observed (kPa)

Difference (kPa)

Confidence interval (kPa)

30 59 40 19 ± 29 31 45 40 5

32 35 15 20 33 49 50 -1

Table 131. Coefficients of G’1/2 at plateau. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.072993 -0.14759 0.283675 -0.10099 0.121314 -0.06429 -0.04774 0.065576 0.165061 0.024444

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0.06207 0.037412 -0.10542 -0.12522 0.087588 0.133532 0.040826 -0.10142 0.049851 0.024631

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.2

-0.10.0

0.10.2

0.3

***

******

**

**

****

* *

1.0 1.5 2.0 2.5

1.01.5

2.02.5

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

12

3

4

56

7

8

9

1011

12

13

14

1516

17 181920

2122

23

24

2526

2728

2930

3132

33

167

Table 132. Predicted vs. observed values (second model). Test n° Predicted (kPa) Observed (kPa) Difference (kPa)

30 54 40 14 31 42 40 2 32 31 15 16 33 46 50 -4

RSC and Discussion

G’ ½ , as explained in the theory chapter, is the oscillatory stress (Pa) necessary to halve the elastic modulus of the material, that is when the structure of the adhesive begins to flake off and loses a large amount of its elastic modulus. From the images depicted below, we can deduce that the ingredients that contribute most in the increasing the value of G’1/2 are cement and modified cellulose ether, while the cellulose be-haves exactly in the opposite way. Also the polymer contributes, even if less, to the decrease of G’1/2.

Figure 79. Modified C.E. vs. polymer (left). Cellulose ether vs. cement (right) in stress sweep testing

at G’ ½ .

k k

168

Yield Yield TA

Coefficient plot and experimental vs. fitted plot (from R):

Table 133. Coefficients of yield TA. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.059889 -0.18098 0.362195 -0.07449 0.044297 -0.07911 -0.10996 0.092145 0.123894 0.043777 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

-0.04559 -0.02899 -0.1454 -0.05805 0.153364 0.016246 -0.06563 0.090162 0.090099 0.0899

Table 134. Predicted vs. observed values of verification points (first model). Test n°

Predicted (Pa·s)

Observed (Pa·s)

Difference (Pa·s)

Confidence interval (Pa·s)

30 35648 31730 3918 ± 8335 31 31095 39580 -8485

32 22328 22190 138 33 30306 30070 236

Table 135. Coefficients of yield TA. Model with verification points.

V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3 0.060062 -0.18098 0.362349 -0.07179 0.042871 -0.07911 -0.10996 0.092145 0.123894 0.043777

V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5² -0.04559 -0.02899 -0.1454 -0.05805 0.153364 0.013868 -0.06723 0.087867 0.100659 0.08188

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients-0.2

-0.10.0

0.10.2

0.30.4

***

***

***

***

***

**

**** *

4.0 4.5 5.0 5.5

4.04.5

5.05.5

Experimental vs. Fitted Values

Experimental ValueFitt

ed Va

lue

1 2

3

4

5

6

7

8

9

1011

12

1314

15

16

17 1819202122

23

24

25

26

27

282930 31

3233

169

Table 136. Predicted vs. observed values (second model). Test n° Predicted (Pa·s) Observed (Pa·s) Difference (Pa·s)

30 35712 31730 3982 31 31636 39580 -7944 32 22475 22190 285 33 30503 30070 433

Yield AP

Coefficient plot and experimental vs. fitted plot (from R):

Table 137. Coefficients of yield AP. Model without verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.033686 -0.08364 0.329714 -0.11751 0.020196 -0.07384 -0.0045 -0.00253 0.006112 0.043814 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.004851 0.031323 -0.03591 0.087568 0.015089 -0.01883 -0.01101 0.001631 0.055871 0.083542

Table 138. Predicted vs. observed values of verification points (first model). Test n°

Predicted (Pa·s)

Observed (Pa·s)

Difference (Pa·s)

Confidence interval (Pa·s)

30 9649 10625 -976 ± 164 31 8104 8412 -308

32 6152 4680 1472 33 8550 7190 1360

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-0.10.0

0.10.2

0.3

***

***

******

**** *

*

*

3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8

3.43.6

3.84.0

4.24.4

4.64.8

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1 2

3

4

5

6

7

89

10

11

12

1314

1516

1718192021

22

23

24

25

26

27282930

31

32

33

170

Table 139. Coefficients of yield AP. Model with verification points. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

0.035453 -0.08364 0.332639 -0.11685 0.02154 -0.07384 -0.0045 -0.00253 0.006112 0.043814 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.004851 0.031323 -0.03591 0.087568 0.015089 -0.02225 -0.00648 -0.00701 0.06338 0.094118

Table 140. Predicted vs. observed values (second model). Test n° Predicted (Pa·s) Observed (Pa·s) Difference (Pa·s)

30 9649 10625 -976 31 8104 8412 -308 32 6152 4680 1472 33 8550 7190 1360

RSC and Discussion

The yield corresponds to the viscosity value at the first point of the flow curve obtained with the parallel-plate geometry with the TA rheometer and the ball-measuring system geometry using Anton Paar rheometer .

Figure 80. CE mod. vs. cement amount in yield test using parallel-plate (left) and ball-measuring sys-

tem geometry (right). The cement and the modified cellulose increase the yield value although the latter does it more (especially using the parallel-plate geometry) (Figure 80). On the contrary, the polymer and

171

the cellulose lower it for the same reasons explained in the PCA considerations. In particular, the cement gives a major contribution only when there is a low amount of cellulose (0.25% in this case) and a high amount of its modified formula and the cellulose and polymer lower the results more when there is a low amount of modified cellulose (Figure 81). The same can be said for both instruments with the difference that the TA instrument measures a higher yield than the Anton Paar does. This is because two different geometries were used.

Figure 81. . Cellulose ether vs. polymer amount in yield test using parallel-plate (left) and ball-meas-

uring system geometry (right). Dustiness

E Min Coefficient plot and experimental vs. fitted plot (from R):

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-2-1

01

23

4 ******

*****

* *

* *

5 10 15 20 25

510

1520

25

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17 1819

20

21

22

23

2425262728

29

172

Table 141. Coefficients of E Min. Dustiness Model. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

-0.95556 0.05 0.016667 3.744444 2.95 -0.48125 0.78125 0.63125 -2.03125 -0.36875 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.40625 0.26875 0.04375 -0.86875 -0.61875 0.354617 -0.09538 0.204617 0.054617 0.504617

E Max Coefficient plot and experimental vs. fitted plot (from R):

Table 142. Coefficients of E Max. Dustiness Model. V1 V2 V3 V4 V5 V1V2 V1V3 V1V4 V1V5 V2V3

2.794444 3.338889 1.622222 8.838889 4.111111 -1.65625 0.23125 0.94375 -4.43125 -1.05625 V2V4 V2V5 V3V4 V3V5 V4V5 V1² V2² V3² V4² V5²

0.33125 1.43125 0.51875 -2.48125 -3.14375 4.440237 -4.55976 -2.30976 -2.25976 4.190237

RSC and Discussion The emission of dust from cementitious tile adhesives can be described by two different phe-nomena: a first release of dust immediately consequent to the act of poring powder in a bucket and the remaining dust at the end of the precipitation process. These two mechanisms are described by the dustiness model generated in present work. The first mechanism seems to involve all the raw materials investigated: cement, calcium car-bonates (fine and coarse), polymers and fibers all contribute to the generation of powder, as shown in Figure 82.

1 2 3 4 5 6 7 8 9 11 13 15 17 19

Coefficients

-10-5

05

10 ***

**

**

* *

*

20 30 40 50 60

2030

4050

60

Experimental vs. Fitted Values

Experimental Value

Fitted

Value

1

2

3

4

5

6

78

9

10

11

12

13

14

15

16

171819 20

21

22

23

2425

26

2728

29

173

Figure 82. Coarse carbonate vs. Cement amount (left) and fibers vs. polymer amount (right) in initial

dust formation.

174

The second mechanism mostly involves light components of the formulation, like fibers and polymer that, after 30 second from powder pouring, are still fluctuating, generating the perma-nent cloud of dust, as shown in the following figure.

Figure 83. Filler vs. Cement amount (left) and fibers vs. polymer amount (right) in permanent dust

development.

175

IV. CONCLUSIONS This work confirms that chemometrics demonstrates to have the possibility to strongly improve the knowledge generated by experimentations, as the definitions of rational Experimental De-signs and the multivariate analysis approach can give a wide ranging among information that is usually lost when evaluating tests in single points. In addition a univariate approach do not take in consideration the interaction between the high amount of ingredients and variables of a com-plex system. This discipline, applied to cement based product science, also demonstrated its extreme flexi-bility and adaptability to any kind of experimentation and its perfect applicability in formulation science if it is setup correctly, clearly surpassing the classical univariate approach. The tile adhesive model was able to predict the behavior of the adhesive from the rheological and dynamic-mechanical point of view, in its fresh and hardened state. The dustiness model, for its part, made us understand the way in which the dustiness of dry blends depends on the fine and coarse components. Many further studies can be carried in R&D laboratories of Mapei using these techniques; an example could be the realization of a model able to foresee the coloring of the tile joints and therefore able to predict the right dosage of the inorganic pigments.

176

References

1 Varmuza K.; Filzmoser P. (2009) Introduction to Multivariate Statistical Analysis in Chemometrics. Boca Raton, FL. CRC Press.

2 Kramer R. (1998) Chemometric Techniques for Quantitative Analysis. New York, NY. Marcel Dekker, Inc. 3 Varmuza K. (2012) Chemometrics in Practical Applications. Rijeka. InTech. 4 Patricia M.; Sthor-Hunt (1996) An Analysis of Frequency of Hands-on Experience and Science Achievement. J.

of Research in Science Teaching. 33. 5 Monaco M.; Carrà S. (2018) Chemometrics Application in Cementitious Tile Adhesives Formulation. 6 Box G.E.; Hunter J.S.; Hunter W.G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery,

2nd edition. Wiley. 7 Oehlert G.W. (2010). A First Course in Design and Analysis of Experiment. Minneapolis, MN. University of

Minnesota. 8 Joliffe I.T. (2010) Principal Component Analysis. 2nd edition. Springer. 9 Kinloch A.J. (1987). Adhesion and Adhesives: Science and Technology (Reprinted ed.). London. Chapman and

Hall. p. 1. 10 Mazza, P et al. (2006). A new Paleolithic discovery: tar-hafted stone tools in a European Mid-Pleistocene bone-

bearing bed. J. of Archaeological Science. 33 (9): 1310. 11 Van-Tien P. (2012) Relationship between the adhesive properties and the rheological behavior of fresh mortars.

Cachan. ENS. 12 Comyn J. (1997) Adhesion Science. Cambridge. RSC Paperbacks. 13Bayer R.; Lutz H. (2005) Dry Mortars. Weinheim. Wiley-VCH Verlag GmbH & Co. 14Konietzko A. (1985) The Application of Modern Dry, Factory Mixed, Mortar Products. ZKG International. 48,

no. 12, 625–659. 15 Data from EMO (European Mortar Organization) and Wacker Polymer Systems GmbH & Co. KG. 16 Fritze P. (2002) Deformability and Water Resistance of C1 and C2 Adhesives According to EN 12004 and EN

12002. Burghausen. Waker Polymer System. 17 ASTM C185-15a (2015) Standard Test Method for Air Content of Hydraulic Cement Mortar. www.ASTM.org.

West Conshohocken, PA. ASTM International.doi:10.1520/C0185-15A. 18 Mindess S.; Young F. J. (1981): Concrete. Englewood Cliffs, NJ. Prentice-Hall, Inc. pp. 671. 19 Cody A.M.; Lee H.; Cody R.D.; Spry P.G. (2003) The Effects of Chemical Environment on the Nucleation,

Growth, and Stability of Ettringite. Cement and Concrete Research. 34. pp. 869–881. 20 Kosmatka S.; Panarese W. (1988) Design and Control of Concrete Mixes, Portland Cement Association, Skokie,

Ill. pp. 205. 21 Mamlouk M.; Zaniewski J. (1999): Materials for Civil and Construction Engineers. Addison Wesley Longman,

Inc. 22 Crawford R.L. (1981). Lignin biodegradation and transformation. New York, NY. John Wiley and Sons. 23 Updegraff D.M. (1969). Semimicro Determination of Cellulose in Biological Materials. Analytical Biochemis-

try. 32 (3): 420–424. 24 Müller I. (2007) Influence of Cellulose Ethers on the Kinetics of Early Portland Cement Hydration. Karlsruher

Mineralogische und Geochemische Hefte: Schriftenreihe des Instituts für Mineralogie und Geochemie; 32. 25 Hayakawa K.; Soshiroda T. (1985) An Experimental Study on the Concrete Containing Cellulose Ether. Trans.

Japan Concrete Institute, 7, pp. 17-24. 26 Hayakawa K.; Soshiroda T. (1986) Effects of cellulose ether on bond between matrix and aggregate in concrete.

Adhesion Between Polymers and Concrete Proceedings. London. Chapman & Hall, pp. 22-31. 27 Tanaka Y.; Uryu T.; Yaguchi M. (1996) US Patent No. 5,573,589. Cement compositions containing a sulfated

polysaccharide and method.

177

28 Yamamuro H. (1999) Property of new polisaccaride derivative as a viscosity agent for self compacting concrete.

First International Rilem Symposium on Self Compacting Concrete. Stockholm, Sweden. RILEM Publications, Bagneux, France. pp 449-459.

29 Ghio V.A.; Monteiro P. J. M. (1998) The Effects of Polysaccharide Gum Additives on the Shotcrete Process. ACI Mater J. 95(2), pp. 152-157.

30 Ghio V.A.; Monteiro P. J. M.; Demsetz L. A. (1994) The Rheology of Fresh Cement Paste Containing Polyssa-charide Gums. Cement and Concrete Research. 24(2), pp. 243-249.

31 Pourchez A. et al. (2006) HPMC and HEMC Influence on Cement Hydration. Cement and Concrete Research. 36(2), pp. 288-294.

32 Felixberger J.K. (2008) Polymer-Modified Thin-Bed Tile Adhesives. Augsburg. BASF SE – PCI. 33 Pakravan H.R.; Jamshidi M.; Latifi M. (2009) A Study on Bonding Strength of Polymeric Fibers to Cementitious

Matrix. PAN. 34 Van-Tien P. (2012) Relationship Between the Adhesive Properties and the Rheological Behavior of Fresh Mor-

tars. Cachan. ENS Cachan. 35 Eley R.R. (2005) Applied Rheology in the Protective and Decorative Coating Industry. Rheology Reviews. 173-

140. 36 Freeman Steffe J. (1996). Rheological Methods in Food Process Engineering. Freeman Press. 37 Beris A.N.; Giacomin A.J. (2014). πάντα ῥεῖ : Everything Flows. Applied Rheology. 24: 52918. 38 BS 5168: (1975) Glossary of Rheological Terms, London, British Standards Institution. 39 Schowalter W.R. (1978) Mechanics of Non-Newtonian Fluids. Pergamon Press. 40 Bird R.B.; Stewart W.E.; Lightfoot E.N. (1960) Transport Phenomena. John Wiley & Sons. 41 Bird R.B.; Curtiss C.F.; Armstrong R.C. (1989) Dynamics of Polymeric Liquids. Vol 1 & 2. Wiley Interscience. 42 Morrison F.A. (2001) Understanding Rheology. Oxford University Press. 43 Viswanath D.S. et al. (2007) Viscosity of Liquid. Dordrecht. Springer. 44 Meyers M.A.; Chawla K.K. (2008) Mechanical Behavior of Materials. Cambridge, Cambridge University Press.

pp. 98-103 45 Pileggi R.G. et al. Characterisation of Rendering Mortars by Squeeze-Flow and Rotational Rheometry. Depart-

ment of Construction Engineering, Escola Politécnica, University of São Paulo 46 Gruppo Italiano di Chemiometria, www.gruppochemiometria.it. 47 Monaco M. (2018) C2 Tile Adhesive General Model v1.17. Mapei S.p.A. 48 Monti M. (2008) Meccanismo di interazione di un etere di cellulosa con le fasi costituenti il clinker di un ce-

mento Portland 52,5 durante il processo di idratazione. Mapei S.p.A.; Unimib.