Predicting effective electrical resistivity and conductivity of carbon nanotube/carbon black-filled polymer matrix hybrid nanocomposites

A two-step analytical model based on a percolation network model and electron tunneling theory has been developed to predict the electrical resistivity and percolation threshold of a hybrid nanocomposite system comprising carbon black (CB) and carbon nanotube (CNT). The nanostructure of a tunneling network consisting of CNT and CB agglomerates has been generated to study the effects of various parameters on electrical conductivity. Electron tunneling is the primary mechanism for electrical percolation, which is incorporated into the model by considering the effective tunneling distance of CNTs. Later, a percolation network model is introduced to evaluate the electrical properties of the hybrid nanocomposite. Our results indicate that a high level of alignment leads to a significant decrease of the percolation threshold with an increase in conductivity, while a low CB volume fraction with low intrinsic electrical conductivity degrades the percolation and overall conductivity. Our results also reveal that the addition of CB as a second filler in a hybrid nanocomposite leads to improvements in conductance and percolation threshold. Analytical results show that the current model agrees well with existing experimental data, which reveals that tunneling and percolation are the dominant mechanisms for transition behavior in electrical conductivity.

Automated summary

The study successfully predicted the electrical resistivity and percolation threshold of a hybrid nanocomposite system comprising carbon black and carbon nanotube using a two-step analytical model based on percolation network model and electron tunneling theory. The results showed that high alignment leads to a significant decrease in percolation threshold, while a low volume fraction of CB results in degraded percolation and overall conductivity.