Eliminating common biases in modelling the electrical conductivity of carbon nanotube-polymer nanocomposites

Modelling carbon nanotube-polymer nanocomposites to predict their electrical conductivity demands high computational power. Past research has led to the assumption that conductive networks follow a periodic pattern; however, the impact of the underlying biases had never been investigated. This work provides insights into evaluating such biases and eliminating them to improve simulation accuracy. This work provides insights into a simulation approach to polymer nanocomposites' electrical conductivity that can eliminate biases caused by common assumptions.