Morphology-Dependent Percolation and Conductive Network Formation in Polymer Nanocomposites with Multi-Shaped Nanofillers

Abstract

The electrical performance of polymer nanocomposites strongly depends on the morphology of nanofillers and the structure of the resulting conductive networks. To elucidate the mechanisms governing conductive network formation in multi-morphology nanofiller systems, a ternary coarse-grained model composed of rod-, Y-, and X-shaped nanofillers is constructed. The effects of nanofiller volume fraction (VF) and nanofiller composition ratios on percolation behavior are systematically investigated. By incorporating an efficient cKDTree-based neighbor search method, conductive networks are identified and their topological characteristics are quantified with high computational efficiency. The results demonstrate that nanofiller morphology ratios play a crucial role in controlling local structural evolution and the percolation threshold. Statistical analyses of the main cluster size (MCs) and the number of clusters (Nc) further reveal the synergistic and competitive effects among different filler morphologies. The combination of filler morphologies is shown to be a key factor in determining the percolation threshold and network topology. The multi-morphology simulation framework together with structural characterization approach proposed in this work provide theoretical guidance for the rational design of high-performance conductive polymer nanocomposites.