A computational model for characterizing electrical properties of flexible polymer composite filled with CNT/GNP nanoparticles

Abstract

Ultrasensitive flexible sensors with a high sensitivity in a broad pressure range are required for various applications in flexible electronics era. Hybrid nanocomposites reinforced by carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs) have shown improvements in cost-effectiveness and electrical properties. Simulating the hybrid nanocomposites is of vital significance since it offers an insight into the functional and electrical performance of flexible sensors. In this paper, the structural evolution of CNT and GNP nanocomposites in response to mechanical deformation is theoretically modeled to clarify the synergy mechanism of two nanofillers. The influence of the microstructure parameters including the aspect ratio (AR) and volume ratio of CNT and GNP on the conductivity and sensitivity of the flexible composites are comprehensively analyzed and discussed. Analytical simulation and experimental results show that the conductivity of the mixed filler system fails to outperform any individual system, but shows superiority regarding to the piezoresistive sensitivity. Besides, the Poisson's ratio of the polymer is innovatively introduced to characterize the elastic deformation of the nanocomposites. It was found that the Poisson's ratio of the polymer had the negative correlation with the sensitivity of the CNT/GNP nanocomposite. This work provides considerable guidance for the design and manufacture of flexible piezoresistive sensors.