Identifying the Optimal Conductive Filler Conditions for Maximum Sensitivity in Conductive Polymer Composite Sensors via Dynamic Percolation Modeling Using Monte Carlo Simulation 


Vol. 26,  No. 11, pp. 4715-4724, Nov.  2025
10.1007/s12221-025-01123-8


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  Abstract

This study aims to identify the optimal filler design strategy for maximizing the piezoresistive sensitivity of conductive polymer composites (CPCs) under compressive deformation. To this end, a dynamic percolation model was developed, incorporating filler geometry, surface-to-volume ratio, orientation entropy, compressive strain, and Poisson-induced lateral expansion. Monte Carlo simulations were performed in a GPU-accelerated Python environment using rod- and plate-shaped conductive fillers with various volume fractions (0.002%–6%), aspect ratios (AR = 50–300), and Poisson’s ratios (ν = 0–0.5). Results showed that CPCs with excessively low or high initial filler content exhibited limited sensitivity due to the absence of network formation or early saturation. The highest sensitivity (1.82) was observed near the percolation threshold under ν = 0, while saturated networks yielded sensitivity below 0.20. At the onset of percolation—the minimum filler content where networks begin to form dynamically under strain—sensitivity was maximized, balancing conductivity gain and structural adaptability. Poisson’s effect significantly reduced connectivity at low volume fractions, especially for plate-shaped fillers. The proposed model offers a quantitative, structure-aware framework for optimizing CPC sensor design. It provides a foundation for tunable, high-sensitivity pressure sensors in flexible and wearable electronics.

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  Cite this article

[IEEE Style]

S. Kim and J. Kim, "Identifying the Optimal Conductive Filler Conditions for Maximum Sensitivity in Conductive Polymer Composite Sensors via Dynamic Percolation Modeling Using Monte Carlo Simulation," Fibers and Polymers, vol. 26, no. 11, pp. 4715-4724, 2025. DOI: 10.1007/s12221-025-01123-8.

[ACM Style]

SangUn Kim and Jooyong Kim. 2025. Identifying the Optimal Conductive Filler Conditions for Maximum Sensitivity in Conductive Polymer Composite Sensors via Dynamic Percolation Modeling Using Monte Carlo Simulation. Fibers and Polymers, 26, 11, (2025), 4715-4724. DOI: 10.1007/s12221-025-01123-8.

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