Modeling and Prediction of Polymer Nanocomposite Properties

Overview

The book series 'Polymer Nano-, Micro- and Macrocomposites' provides complete and comprehensive information on all important aspects of polymer composite research and development, including, but not limited to synthesis, filler modification, modeling,
characterization as well as application and commercialization issues. Each book focuses on a particular topic and gives a balanced in-depth overview of the respective subfi eld of polymer composite science and its relation to ...

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Overview

The book series 'Polymer Nano-, Micro- and Macrocomposites' provides complete and comprehensive information on all important aspects of polymer composite research and development, including, but not limited to synthesis, filler modification, modeling,
characterization as well as application and commercialization issues. Each book focuses on a particular topic and gives a balanced in-depth overview of the respective subfi eld of polymer composite science and its relation to industrial applications. With the books the readers obtain dedicated resources with information relevant to their research, thereby helping to save time and money.

This book lays the theoretical foundations and emphasizes the close connection between theory and experiment to optimize models and real-life procedures for the various stages of polymer composite development. As such, it covers quantum-mechanical approaches to understand the chemical processes on an atomistic level, molecular mechanics simulations to predict the filler surface dynamics, finite element methods to investigate the macro-mechanical behavior, and thermodynamic models to assess the temperature stability. The whole is rounded off by a look at multiscale models that can simulate properties at various length and time scales in one go - and with predictive accuracy.

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Product Details

Meet the Author

Vikas Mittal is an Assistant Professor at the Chemical Engineering Department of The Petroleum Institute, Abu Dhabi. He obtained his PhD in 2006 in Polymer and Materials Engineering from the Swiss Federal Institute of Technology in Zurich, Switzerland. Later, he worked as Materials Scientist in the Active and Intelligent Coatings section of SunChemical in London, UK and as Polymer Engineer at BASF Polymer Research in Ludwigshafen, Germany. His research interests include polymer nano-composites, novel filler surface modifications, thermal stability enhancements, polymer latexes with functionalized surfaces etc. He has authored over 40 scientific publications, book chapters and patents on these subjects.

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Table of Contents

List of Contributors XI

Preface XV

1 Convergence of Experimental and Modeling Studies1
Vikas Mittal

1.1 Introduction 1

1.2 Review of Various Model Systems 1

References 10

2 Self-Consistent Field Theory Modeling of PolymerNanocomposites 11
Valeriy V. Ginzburg

2.1 Introduction 11

2.2 Theoretical Methods 13

2.2.1 Incompressible SCFT 13

2.2.2 Compressible SCFT 17

2.3 Applications of SCFT Modeling: Predicting the NanocompositePhase Behavior 18

2.3.1 Organically Modifi ed Nanoclays in a Homopolymer Matrix18

2.3.2 Organically Modifi ed Nanoclays in a Binary BlendContaining End-Functionalized Polymers 24

2.4 Summary and Outlook 32

Acknowledgments 33

References 33

3 Modern Experimental and Theoretical Analysis Methods ofParticulate-Filled Nanocomposites Structure 39
Georgy V. Kozlov, Yurii G. Yanovskii, and Gennady E.Zaikov

3.1 Introduction 39

3.2 Experimental 40

3.3 Results and Discussion 42

3.4 Conclusions 60

References 61

4 Reptation Model for the Dynamics and Rheology of ParticleReinforced Polymer Chains 63
Kalonji K. Kabanemi and Jean-François Hétu

4.1 Introduction 63

4.2 Terminal Relaxation Time 66

4.2.1 Linear Entangled Chains 66

4.2.2 Linear Entangled Chains with Rigid Spherical Nanoparticles66

4.3 Detachment/Reattachment Dynamics 72

4.4 Constitutive Equation 74

4.5 Numerical Results 75

4.5.1 Step Shear Strain 75

4.5.2 Steady Shear Flow 78

4.5.3 Start-up of Steady Shear Flow 84

4.5.4 Experimental Validation 85

4.6 Discussion and Generalization of the Model 88

4.6.1 Preliminaries 88

4.6.2 Diffusion of an Attached Chain 89

4.6.3 Multimode Constitutive Equation 91

4.7 Conclusions 92

References 93

5 Multiscale Modeling Approach for Polymeric Nanocomposites95
Paola Posocco, Sabrina Pricl, and Maurizio Fermeglia

5.1 Multiscale Modeling of Polymer-Based NanocompositeMaterials: Toward “Virtual Design” 95

5.2 Atomistic Scale: Basic Instincts 101

5.2.1 Sodium Montmorillonite Silylation: Unexpected Effect ofthe Aminosilane Chain Length 101

5.2.2 Water-Based Montmorillonite/Poly(Ethylene Oxide)Nanocomposites: A Molecular Viewpoint 106

5.3 Mesoscale: Connecting Structure to Properties 109

5.3.1 Water-Based Montmorillonite/Poly(Ethylene Oxide)Nanocomposites at the Mesoscale 109

5.3.2 Nanoparticles at the Right Place: Tuning NanostructureMorphology of Self-Assembled Nanoparticles in Diblock Copolymers112

5.4 Macroscale: Where Is the Detail? The Matter at Continuum119

5.4.1 Small Is Different. Size and Shape Effects ofNanoparticles on the Enhancement Efficiency in PCNs 119

5.5 Conclusions 123

References 125

6 Modeling of Oxygen Permeation and Mechanical Properties ofPolypropylene-Layered Silicate Nanocomposites Using DoE Designs129
Vikas Mittal

6.1 Introduction 129

6.2 Materials and Methods 131

6.2.1 Materials 131

6.2.2 Filler Surface Modification and Composite Preparation131

6.2.3 Characterization and Modeling Techniques 131

6.3 Results and Discussion 132

6.4 Conclusions 141

Acknowledgment 141

References 141

7 Multiscale Stochastic Finite Elements Modeling of PolymerNanocomposites 143
Antonios Kontsos and Jefferson A. Cuadra

7.1 Introduction 143

7.2 Multiscale Stochastic Finite Elements Method 144

7.2.1 Modeling State-of-the-Art and MSFEM Motivation 144

7.2.2 Definition of a Representative Material Region (MR)145

7.2.3 Spatial Randomness Identifi cation 146

7.2.4 Multiscale Homogenization Model 148

7.2.5 Monte Carlo Finite Element Model 152

7.3 Applications and Results 153

7.3.1 Estimation of Bulk Mechanical Properties 153

7.3.2 Modeling of Nanoindentation Data 161

References 165

8 Modeling of Thermal Conductivity of Polymer Nanocomposites169
Wei Lin

8.1 Models for Thermal Conductivity of Polymer Composites– A Historical Review on Effective Medium Approximations andMicromechanical Models 169

8.1.1 Parallel and Series Models 170

8.1.2 Maxwell’s Model (Maxwell–Garnett Equation)172

8.1.3 Fricke’s Model 172

8.1.4 Hamilton–Crosser Model 174

8.1.5 Hashin’s Model 175

8.1.6 Nielsen’s Micromechanics Model 176

8.1.7 Equivalent Inclusion Method 178

8.1.8 Benveniste–Miloh Model 180

8.1.9 Davis’ Model 182

8.1.10 Empirical Model by Agari and Uno 182

8.1.11 Hasselman–Johnson Model 183

8.1.12 Bruggeman Asymmetric Equation 183

8.1.13 Felske’s Model 185

8.2 A Generalized Effective Medium Theory 186

8.2.1 ATA 187

8.2.2 CPA 188

8.2.3 Further Extension of ATA and CPA to Anisotropic Fillerwith Orientation Distributions 189

8.2.4 Incorporation of Size Distribution Functions into ATA andCPA 190

8.2.5 Incorporation of Interfacial Thermal Resistance into ATAand CPA 191

8.3 Challenges for Modeling Thermal Conductivity of PolymerNanocomposites 191

8.3.1 Size Effect and Surface Effect 191

8.3.2 Sensitivity of κf to a Specific Environment 192

8.3.3 Interfacial Resistance Plays a Very Important Role 193

8.3.4 Filler-Induced Change in κm 195

8.3.5 Dispersion and Distribution 196

Acknowledgments 196

References 197

9 Numerical–Analytical Model for Nanotube-ReinforcedNanocomposites 201
Antonio Pantano

9.1 Introduction 201

9.2 Numerical–Analytical Model 204

9.2.1 The Mori–Tanaka Method 204

9.2.1.1 Calculation of the Correlation Matrix A1 dil 206

9.2.1.2 Calculation of the Stiffness Matrix of the EquivalentInclusion Cl 207

9.2.2 FEM Model Design 207

9.2.2.1 RVE Geometry 207

9.2.2.2 Matrix Constitutive Model 208

9.2.2.3 Carbon Nanotube 208

9.2.2.4 Contact Model 208

9.2.2.5 Deformation Mode 209

9.2.2.6 Calculation of the Equivalent Young’s Modulus ofthe MWCNT 209

9.2.2.7 Calculation of the Eshelby Tensor 209

9.3 Results 210

9.4 Conclusions 212

Appendix 9.A 212

References 213

10 Dissipative Particles Dynamics Model for PolymerNanocomposites 215
Shin-Pon Ju, Yao-Chun Wang, and Wen-Jay Lee

10.1 Introduction 215

10.2 Scheme for Multiscale Modeling 218

10.2.1 Dissipative Particle Dynamics Simulation Method 219

10.2.2 Coarse-Grained Mapping 219

10.2.3 Mixing Energy and Compressibility 220

10.2.4 Dissipative Particle Dynamics Scales to Physical Scales222

10.3 Two Case Studies 222

10.3.1 PE/PLLA Composite 222

10.3.2 CNT/PE/PLLA Composite 228

10.4 Future Work 234

References 234

11 Computer-Aided Product Design of Wheat Straw PolypropyleneComposites 237
Rois Fatoni, Ali Almansoori, Ali Elkamel, and Leonardo Simon11.1 Natural Fiber Plastic Composites 237

11.1.1 History and Current Market Situation 237

11.1.2 Technical Issues and Current Research Progress 238

11.2 Wheat Straw Polypropylene Composites 240

11.3 Product Design and Computer-Aided Product Design 242

11.4 Modeling Natural Fiber Polymer Composites 245

11.5 Mixture Design of Experiments 247

References 252

12 Modeling of the Chemorheological Behavior of ThermosettingPolymer Nanocomposites 255
Luigi Torre, Debora Puglia, Antonio Iannoni, and AndreaTerenzi

12.1 Introduction 255

12.2 The Cure Kinetics Model 258

12.3 The Chemoviscosity Model 263

12.4 Relationship between Tg and α 268

12.5 Case Study 1: Carbon Nanofibers in Unsaturated Polyester268

12.5.1 Cure Kinetic Analysis 271

12.5.2 Chemorheological Analysis 275

12.6 Case Study 2: Montmorillonite in Epoxy Resin 277

12.6.1 Cure Kinetic Analysis 279

12.6.2 Relation between Tg and Degree of Cure 281

12.6.3 Chemorheological Analysis 282

References 285

Index 289

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