Collaborative Research: Enabling Design of Polymer Nanocomposites Guided by Mesoscale Simulations and Scattering Experiments

University Of Cincinnati Main Campus, Cincinnati OH

Investigators

Abstract

Nanoscale materials are found in many consumer products to serve as particle reinforcement in a polymer matrix; the material system is called a polymer nanocomposite. One such example is the use of nanoscale silica particles in tires to improve fuel economy. Choices of material combinations are often determined through trial and error experiments. This research grant will enable the design of polymer nanocomposites via an informed approach using computational modeling and experiments, resulting in a simple tool to predict compatibility as a function of material types and processing conditions. Potential applications for polymer nanocomposites include solar cells, sports equipment, medical devices and aerospace structures. The project will involve several female undergraduate students through a program at the University of Cincinnati as well as high school students through the University of Dayton Summer Honors Institute and the Minority Engineering & Technology Enrichment Camp. Long-standing relationships with Ethiopian universities will be leveraged via grants through the NSF Partnerships for Enhanced Engagement in Research (PEER) program. Multicomponent polymer mixtures such as nanocomposites are among the most commonly used polymeric materials, but there is a significant gap in the understanding of how hierarchical structure develops in such systems. This research tests the hypothesis that it is possible to accurately determine a parameter controlling filler dispersion in a polymer matrix, and to employ this parameter in a toolbox to predict optimized structure and performance. The approach couples a pseudo-thermodynamic analysis of binary mixtures to obtain a pseudo-second order virial coefficient which quantifies binary enthalpic interactions, and which relates to a coarse-grained potential. This parameter will be employed in mesoscale simulations to predict optimal compositions, processing conditions, dispersion and/or segregation of components in complex blends, correlation functions and correlation lengths for fillers. These features can also be experimentally determined in separate x-ray and neutron scattering measurements. Therefore, the researched work involves three novel components: 1) tabulation of pseudo-second order virial coefficients using scattering and determination of potential functions for simulation; 2) dissipative particle dynamics simulations of binary, ternary, quaternary mixtures using potentials from part 1; 3) x-ray and neutron scattering, microscopy, dynamic mechanical and rheological measurements to verify simulation results. The outcome of our approach is a practical solution to compounding issues, based on a mutually validating experimental and simulation methodology.