Multiscale Analysis of the Highly Stretchable Carbon-Based Polymer Strain Sensor.

In this paper, a multiscale analysis method was proposed to simulate carbon nanoparticles (CNPs)-filled polymers which can be strain sensors applied in wearable electronic devices, flexible skin, and health monitoring fields. On the basis of the microstructure characteristics of the composite, a microscale representative volume element model of the CNPs-filled polymer was established using the improved nearest-neighbor algorithm. By finite element analysis, the variation of the junction widths of adjacent aggregates can be extracted from the simulation results. Then, according to the conductive mechanism of CNP-filled polymers, the composite was simplified as a circuit network composed of vast random resistors which were determined by the junction widths between adjacent aggregates. Hence, by taking junction widths as the link, the resistance variation of the CNPs-filled polymer with the strain can be obtained. To verify the proposed method, the electromechanical responses of silicone elastomer filled with different CNPs under different filling amounts were investigated numerically and experimentally, respectively, and the results were in good agreement. Therefore, the multiscale analysis method can not only reveal the strain-sensing mechanism of the composite from the microscale, but also effectively predict the electromechanical behavior of the CNPs-filled polymer with different material parameters.