Characterizing Six Percolation Cases in Flexible Electronic Composites: A Monte Carlo-Based 3D Compressive Percolation Model for Wearable Pressure Sensors

This study employs a Monte Carlo-based 3D compressive percolation model to systematically analyze the electrical behavior of flexible electronic composites under compressive deformation. By simulating the spatial distribution and connectivity of conductive particles, this study identifies six distinct percolation cases, each describing a unique connectivity evolution under strain. The model reveals that excessive initial connectivity leads to saturation effects, reducing sensitivity, while a high Poisson's ratio (≥0.3) causes connectivity loss due to shear plane expansion. Notably, asymmetric particle shapes, such as cylinders and rectangles, exhibit superior percolation behavior, forming infinite clusters at lower strain thresholds (~0.4) compared to spherical particles (~0.5). Monte Carlo simulations with 3000 particles validate these findings, showing consistent trends in percolation behavior across different deformation states. By classifying and quantifying these six connectivity scenarios, this research provides a structured framework for optimizing flexible sensor designs, ensuring an optimal balance between conductivity and sensitivity. These findings contribute to advancing flexible electronics, particularly in wearable health monitoring, robotics, and smart textiles.