A New Method for Conductivity Prediction in Polymer Carbon Nanofiber System by the Interphase Size and Total Conductivity of Constituents

Abstract

Current modeling approaches for the conductivity of polymer composites containing carbon nanofiber (CNF) called as PCNFs exhibit limitations. Herein, we introduce an enhanced Ouali model to accurately forecast the PCNF conductivity by incorporating the operative CNF amount and the conductivity contributions of CNFs, interphase region, and tunneling zones. The effective CNF volume fraction is derived from the dimensions of both CNFs and interphase, while the overall conductivity calculation integrates the resistances of interphase region and tunnels. The model's accuracy is validated through empirical conductivity measurements of various PCNF samples and extensive parametric analyses. An interphase depth (t) of less than 8 nm renders the composite insulative, whereas peak conductivity of 0.04 S/m is achieved at an interphase depth of 40 nm and interphase conductivity of 400 S/m. These results underscore the significant influence of interphase depth and conductivity on the overall electrical performance of the composites. Furthermore, a CNF length (l) below 13 μm or a contact diameter (d) under 10 nm also results in an insulative composite. Conversely, maximum values of CNF length (80 μm) and contact diameter (40 nm) enhance the composite's conductivity to 0.1 S/m. These findings illustrate the advantageous impact of longer nanofibers and wider tunnels on the electrical conductivity of PCNFs.