(621f) Computational Design of Polymer-Clay Nanocomposites: A Multiscale Hierarchical Approach for Barrier Property Prediction

Polymer-clay nanocomposites (PCNs) have drawn increasing attention due to their significantly improved properties as compared with pure polymers. It is recognized that, however, optimal design of the PCN product and the associated synthesis process can be hardly achieved. This is mainly due to the lack of fundamental knowledge about this type of nano-structured materials. Computational methods play a critical role in revealing quantitative correlation among formulation, processing, structure, property and performance. They should be able to greatly facilitate PCN design. In this work, an effort on computational prediction of barrier properties of PCNs is pursued.

Various analytical and numerical methods have been proposed for calculation of PCNs' permeability which is a direct indicator of the barrier property. However, there has been no method available that can account for the hierarchical morphology of PCNs. At a millimeter scale or beyond, the structure is a dispersion of high aspect ratio clay particles in the polymer matrix. At a micrometer scale, the clay particle structure is a mix of exfoliated clay sheets of nanometer level thickness and intercalated clay sheets with interlayer galleries of nanometer level height. At a nanometer scale, polymer chain configurations near a clay sheet surface and in a gallery space are significantly different from those in the bulk matrix. In order to gain a holistic understanding on the material behavior of PCNs and to predict accurately their barrier properties, the development of a multiscale hierarchical approach is essential.

The proposed methodology will accomplish a number of tasks at three levels. (1) First, a PCN configuration (at nanometer scale) containing a stack of two parallel clay sheets in a polymer matrix is to be equilibrated by the Metropolis Monte Carlo (MC) algorithm. This allows derivation of the following key information: the polymer chain configurations in the bulk matrix, the interfacial layer, and the gallery space, and the thicknesses of the interfacial layer and the gallery space. (2) The polymer chain configurations are then transmitted to the second level, where gas permeation through the bulk matrix, the interfacial layer, and the gallery space are investigated using Gusev-Suter transition state theory (TST) analysis and a kinetic MC simulation; this permits calculation of permeability coefficients. (3) Finally, a millimeter-size PCN sample featuring a hierarchical morphology is constructed and a direct finite-element-based method is used to derive its effective permeability coefficient.

This work is among the earliest efforts on prediction of PCNs' barrier property with a full consideration of their multiscale hierarchical morphology. The method is generic and should be applicable to a wide range of polymer nanocomposites. The superiority of the multiscale approach over existing monoscale models are demonstrated through detailed case studies. Moreover, the effectiveness of the methodology in generating insightful understandings and guiding PCN design will be thoroughly explored.