Wow! eBook
Modeling and Prediction of Polymer Nanocomposite Properties
Author(s): Vikas Mittal
- Publisher: Vch Verlagsgesellschaft Mbh
- Publication Date: February 25, 2013
- Edition: 1st
- Language: English
- Print length: 298 pages
- ISBN-10: 3527331506
- ISBN-13: 9783527331505
Book Description
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.
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.
