Development of Series-Parallel and Neural-Network Based Models for Predicting Electrical Conductivity of Polymer Nanocomposite

Abstract

Polymer nanocomposites are emerging hybrid materials for the production of energy storage electrodes, biomedical sensors, and building construction materials. However, experimentation cost and time can be unfavorable to their performance investigation. Therefore, using a modeling approach to predict the electrical conductivity of polymer nanocomposite is an effective approach in mitigating experimentation cost and time. Since the polymer nanocomposites’ electrical conductivity depends on several factors, the engagement of efficient analytical models for predicting their properties, cannot be overemphasized. Herein, this study developed a series-parallel model, which incorporates the connection between the polymer and the nanofillers for the prediction of the electrical conductivity of graphene-polypyrrole (Gr-PPy) and reduced graphene oxide/polyvinyl alcohol/polypyrrole (RGO/PVA/PPy) nanocomposites. In addition to explicit modelling, an artificial intelligence approach (neural network) was also explored for the prediction tasks. The results of the models in an entity and when compared to an existing model, show flexibility and accuracy for the polymer nanocomposites electrical conductivity prediction. It can be inferred that the model can be suitable to predict the electrical conductivity of polymer nanocomposites.

Keywords

• Electrical conductivity
• energy storage
• graphene
• modeling
• nanocomposite
• polymer