Characterization of anisotropic conductive networks in polymer composites aligned with wavy carbon nanotubes

Abstract

Carbon nanotube (CNT)-based polymer nanocomposites (PNCs) are of significant interest due to the exceptional intrinsic properties of CNTs and their ability to impart anisotropic functionalities to bulk materials. Understanding the structure-to-property relationships of these composites is essential for designing materials with application-tailored electrical performance. However, existing modeling approaches often rely on computationally intensive simulations or oversimplify critical geometric parameters, and they lack quantitative analysis of conductive network structures crucial to transport mechanisms. In this study, an improved Monte Carlo framework is introduced, employing efficient stacked representative volume elements embedded with realistic anisotropic CNT networks incorporating key morphological features. Systematic network decomposition via depth-first search algorithm enabled strict quantification of conduction pathways, leading to the identification of CNT waviness as a dominant factor at high anisotropy. A novel path-to-length ratio was proposed as a robust and predictive metric for network conductivity, offering a new pathway for rational design of high-performance PNCs.