(291g) Multiscale Hierarchical Design of Polymer Nanocomposite Coatings

In the development of polymer nanocomposites (PNCs), computational prediction of material performance is of great advantages in achieving rational and cost-effective design. Although the modeling activity offers comprehensive and scientific information flow from material formulation, to structure, property, and further to performance, effective utilization of such information for design solution derivation remains as a very challenging task. Consequently, development of decision making methods that can generate the reverse information flow from the anticipated performance to promising material formulations is of great importance as well. We believe the most effective design can only be realized when the modeling and decision making activities are systematically integrated.

This work introduces a new multiscale hierarchical design framework specifically tailored for PNCs. Due to its distinct features of generality and comprehensiveness, the introduced methodology outperforms most existing computational methods in this area. By this methodology, a wide range of PNCs containing any-shaped nanoparticles dispersed in a thermoplastic or thermoset polymer matrix can be thoroughly investigated for multiple property prediction. Furthermore, a reliable prediction is promoted through giving a full consideration of the intrinsic multiscale hierarchical structure of PNCs. Most importantly, modeling and decision making activities at individual time and length scales are systematically coordinated in a multi-level hierarchical fashion. The interactive modeling and decision making activities offer unique opportunities for gaining in-depth scientific understandings and achieving superior design efficiency simultaneously. The efficacy of the introduced methodology will be fully demonstrated by the design of a scratch- and corrosion-resistant nanopaint.