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Review demonstrates multiscale modeling frameworks for piezoresistive self-sensing composites, highlighting critical gaps in translating electrical resistivity changes to mechanical strain.
Materials that exhibit self-sensing via the piezoresistive effect (i.e. having strain-dependent electrical properties) have been massively explored in diverse and far-reaching potential use cases including civil and aeronautical structural sensing, biomedical and human health monitoring, consumer products (e.g. tactile sensors), and, among many other examples, robotics. In all of these applications, however, users of self-sensing materials are typically not directly interested in the electrical state of the material. Rather, they want to know the underlying strain state that gives rise to an observed electrical change. Deducing strain requires a model of the piezoresistive effect—a model of the coupling between electrical conductivity (or resistivity) and strain. Existent modeling approaches for piezoresistivity are diverse, including microscale modeling of the interactions among individual conductive fillers and homogenization approaches capable of making macroscale predictions. This review article therefore has two goals: First, we summarize prevailing modeling approaches, including methods, observations, and limitations. And second, we identify key gaps in the state of the art that remain to be closed, especially with an eye toward the goal of using models in practice to characterize self-sensing effects. A brief summary of the physics of self-sensing is also provided.
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Tallman et al. (2026) studied this question.
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