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Journal = Physchem
Section = Statistical and Classical Mechanics

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13 pages, 1731 KiB  
Article
Monte Carlo Investigation of Orientation-Dependent Percolation Networks in Carbon Nanotube-Based Conductive Polymer Composites
by Sang-Un Kim and Joo-Yong Kim
Physchem 2025, 5(3), 27; https://doi.org/10.3390/physchem5030027 - 7 Jul 2025
Viewed by 340
Abstract
Conductive polymer composites (CPCs) filled with anisotropic materials such as carbon nanotubes (CNTs) exhibit electrical behavior governed by percolation through filler networks. While filler volume and shape are commonly studied, the influence of orientation and alignment remains underexplored. This study uses Monte Carlo [...] Read more.
Conductive polymer composites (CPCs) filled with anisotropic materials such as carbon nanotubes (CNTs) exhibit electrical behavior governed by percolation through filler networks. While filler volume and shape are commonly studied, the influence of orientation and alignment remains underexplored. This study uses Monte Carlo simulations to examine how the mean orientation angle and angular dispersion of CNTs affect conductive network formation. The results demonstrate that electrical connectivity is highly sensitive to orientation. Contrary to conventional assumptions, maximum connectivity occurred not at 45° but at around 55–60°. A Gaussian-based orientation probability function was proposed to model this behavior. Additionally, increased orientation dispersion enhanced conductivity in cases where alignment initially hindered connection, highlighting the dual role of alignment and randomness. These findings position orientation as a critical design parameter—beyond filler content or geometry—for engineering CPCs with optimized electrical performance. The framework provides guidance for processing strategies that control alignment and supports applications such as stretchable electronics, directional sensors, and multifunctional materials. Future research will incorporate full 3D orientation modeling to reflect complex manufacturing conditions. Full article
(This article belongs to the Section Statistical and Classical Mechanics)
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