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.