LAUSR

Abstract Carbon nanofiller-modified polymers have received enormous attention from the research community for a diverse range of applications including self-sensing and structural health monitoring (SHM). It is well known that nanocomposites become electrically conductive with the addition of sufficiently many nanofillers to surpass the percolation threshold. The relation between direct current (DC) conductivity and nanofiller networking has been thoroughly elucidated via expansive efforts aimed at electrically modeling the microstructure of nanofiller-modified polymers. Beyond DC conductivity, it is also well known that nanocomposites exhibit complex, frequency-dependent electrical properties. However, the underlying network-centric mechanisms of complex impedance have received less dutiful treatment. Most work on nanocomposite alternating current (AC) properties have emphasized the development of macroscale equivalent circuit models. Even though such models can adeptly replicate net input-output responses, they ignore the microstructure of the material. This is an important oversight because the microstructure intrinsically drives the macroscale response. Therefore, the work herein presented seeks to advance the state of the art by developing a microscale computational percolation model to understand the underlying network-centric mechanisms of nanocomposite complex impedance. The proposed network connectivity model is calibrated against experimental data obtained from carbon nanofiber (CNF)-modified epoxy, and a sensitivity study is conducted in order to elucidate how network properties impact overall impedance. This results in key revelations not available from prevailing macroscale equivalent circuit models. Insights herein obtained could be critically important to producing exotic, next-generation nanocomposites with highly optimized or application-specific electrical admittivity and/or piezoimpedivity.