Optimizing patterned laser-induced graphene strain sensors via novel piezoresistive modeling and multi-objective analysis

The patterned design of flexible sensors facilitates tailored performance to meet diverse application demands. However, experimental approaches to establish structure-performance relationships become costly and inefficient, particularly when multiple geometric parameters and sensing metrics are involved. In this study, we propose a universal piezoresistive model that overcomes the limitations of existing small-strain linear models, effectively capturing the relationship between conductivity tensor components and strain under large deformation conditions. A numerical method incorporating this model was developed, significantly improving accuracy and computational efficiency in predicting electromechanical behavior and optimizing sensor performance. Moreover, we introduce a rapid, cost-effective workflow that integrates Latin hypercube sampling with Pareto-optimal solutions to achieve multi-parameter and multi-objective optimization of sinusoidal-patterned sensors. This work establishes a generalizable and simulation-driven design paradigm that expedites flexible sensor development while enhancing adaptability across diverse application scenarios.