Materials Science and Engineering: Chapter 16. From Drug Discovery QSAR to Predictive Materials QSPR: The Evolution of Descriptors, Methods, and Models by Breneman Curt M. & Krein Mike & Morkowchuk Lisa & Natarajan Bharath & Wu Ke

Author:Breneman, Curt M. & Krein, Mike & Morkowchuk, Lisa & Natarajan, Bharath & Wu, Ke
Language: eng
Format: epub
Publisher: Elsevier Science
Published: 2013-07-10T04:00:00+00:00

4 Integration of Physical and MQSPR Models for Nanocomposite Materials Modeling

Having discussed the details of descriptor-based model construction, the integration of MQSPR modeling with physics-based continuum modeling is discussed. The effectiveness of such a combination in linking phenomena at different length scales is demonstrated using polymer nanocomposites as a model system. Polymer nanocomposites (PNCs) are complex material systems in which the dominant length scales, i.e. the particle sizes, radii of gyration of the polymers and the interparticle spacings begin to converge (Krishnamoorti and Vaia, 2007). The energetic interactions between the constituent species at this length scale (nanometers) dictate the mesoscale nanoparticle dispersion morphology and interphase polymer properties. These mesoscopic characteristics are further expressed as deviations in the continuum properties of the neat polymer (Schadler et al., 2007). The PNC literature has shown these materials to be useful in a multitude of applications, such as smart lighting, high-voltage transmission, germ resistance, flame retardation, and a multitude of other areas (Fu et al., 2005; Tao et al., 2013; Vaia et al., 1999; Wang et al., 2012). However, it is recognized that PNCs are yet to realize their market potential due to the inability to design or predict their macroscopic properties using microscopic constituent information (Schaefer and Justice, 2007). This shortcoming is attributed to the lack of understanding of the interactions at various length scales and the physics bridging them (Kumar and Krishnamoorti, 2010). A thorough understanding of the underlying physics to enable ab initio prediction of properties would require rigorous multiscale modeling and the development of novel scale-bridging techniques, both of which are extremely time and resource intensive. A novel interdisciplinary approach combining heuristic MQSPR, physics-based continuum modeling and experimental validation is demonstrated to predict the thermomechanical properties of polymers embedded with silane-modified spherical nanoparticles.

The guiding rationale in this study was that the dispersion morphology and interface polymer properties can be predicted based on the surface energetic components (dispersive, polar) of the particle and matrix polymer, using correlations built from experiments (Stockelhuber et al., 2010, 2011). These predicted dispersions and interface mobilities can be employed to build 3-D continuum FEM models that further predict the bulk viscoelastic properties. Thus, if the surface energies themselves can be augured from properties of the starting functionalized particle and matrix chemistries using MQSPR models, the thermomechanical properties of the resulting nanocomposites can be virtually predicted. The paradigm used for this approach is shown in Figure 16.10.

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.