2D Microstructure Image-based Simulation of Piezoresistive Graphene Networks
Flexible and highly sensitive piezoresistive nanomaterial systems have been demonstrated to possess considerable potential for monitoring structural integrity and human physiological performance. The piezoresistivity of the nanomaterial systems mainly stem from conductive/semi-conductive nanomaterials’ intrinsic piezoresistivity, tunneling effect, and contact resistance of the nanomaterial networks. Characterizing the mechanisms of the strain responsive performance (i.e., piezoresistivity) of the nanomaterial-based strain sensors has attracted increasingly more attention. Different computational models and numerical simulation strategies, such as tunneling effect- and Monte Carlo-based methods, among others, have been developed to analyze the piezoresistivity mechanisms of the strain sensors. However, few reported efforts have associated the piezoresistive behaviors with the actual microstructures of the sensing materials. In this study, binder-free graphene networks self-assembled on corona charged ultrathin stretchable medical tapes, forming strain sensing graphene patterns. To investigate the tensile strain-induced electromechanical response of the graphene patterns, microstructure-based finite element analysis (FEA) has been conduced using COMSOL Multiphysics software. In particular, in-situ 2D optical microscopy images were taken for the graphene networks, which were subjected to different static tensile strains (i.e., 5%, 10%, 15%, and 20%). Then, the 2D images were post-processed using Matlab software to form isolated entities, which could be reconstructed as geometrical component models in COMSOL. In addition, the material properties of graphite and air (from COMSOL in-built material library) were allocated to the graphene particles and the gaps between the particles, respectively. The Electric Current module was employed to analyze the electrical properties of the graphene networks under different tensile strains. Finally, the numerical simulation results were compared with the experimental measurements. It was found that the proposed microstructure-based FEA could relatively accurately simulate the strain sensing performance of the graphene networks.
