Comprehensive quantification of electrically conductive networks with complex morphologies in carbon nanotube-polymer composites

Abstract

Carbon nanotube (CNT)-based polymer nanocomposites exhibit highly variable electrical properties due to the complex interplay between filler geometry and network formation. However, previous studies have largely lacked quantitative analysis of the internal network structure, and more importantly, the underlying mechanisms by which individual geometrical factors affect network connectivity remain poorly understood. To address this limitation, the present work provides a systematic investigation into the role of key CNT parameters on network morphology and resulting electrical conductivity. A Monte Carlo model was developed to incorporate realistic CNT features, including statistical length distributions and waviness represented via splines. The model was validated against experimental results and subsequently used to analyze the influence of CNT geometry on conductive network formation. A depth-first search algorithm was then applied to decompose the simulated networks into discrete conduction paths, enabling quantification of both path count and path length. Based on these findings, unified metrics are proposed that encapsulate the combined effects of multiple morphological factors and provide practical descriptors for predicting and optimizing the electrical performance of CNT networks.