Geometric Optimization of Patterned Conductive Polymer Composite-based Strain Sensors Toward Enhanced Sensing Performance

Abstract

The patterned design of flexible sensors enables customized performance to meet diverse application demands. However, when multiple geometric parameters and sensing metrics are involved, experimental approaches to establish structure-performance relationships become costly and inefficient. Here, a novel universal piezoresistive model--overcoming limitations of commonly used models that are only applicable to small strains and linear responses--is developed to capture the relationship between conductivity tensor components and strain. A numerical method incorporating this model simulates the electromechanical properties of conductive composites and predicts patterned strain sensors' behavior. To validate this approach, a flexible strain sensor based on laser-induced graphene technology is fabricated and tested. Additionally, a rapid, cost-effective workflow combining Latin hypercube sampling and Pareto-optimal solutions is demonstrated for multi-parameter and multi-objective optimization of the sinusoidal-patterned sensor. This study provides valuable insights for investigating the structure-performance relationship of strain sensors and advances optimization methods for sensor designs.