Analytical model for the electrical conductivity of the hybrid nanocomposite comprising conducting nanowires and insulating particulate fillers

This work presents an analytical model to predict the electrical conductivity of hybrid nanocomposites consisting of conducting nanowires and insulating particles. The model utilizes a single variable, the ratio of particle diameter to nanowire length, determined through data mining techniques. Machine learning techniques show a monotonically increasing relationship between this variable and conductivity. The conductivity equation is derived from the Kozeny-Carman relation, which relates conductivity to nanowire density and current path tortuosity. The Voronoi tessellation simplifies the filler distribution, allowing analytic expressions for these two characteristics. Results are compared to numerical simulation data to validate the model's accuracy.HighlightsNovel equation developed for nanocomposite design based on physical parameters.Data mining identifies key parameters governing the conductivity of nanocomposites.Machine learning reveals the relationship between key parameters and conductivity.Computational geometry quantizes the dispersion structure of fillers.Derivation of a conductivity equation using the Kozeny-Carman relation. This study introduces an analytical model for predicting the electrical conductivity of hybrid nanocomposites made of conducting nanowires and insulating particles. Leveraging machine learning techniques and the Kozeny-Carman relation, the model calculates conductivity based on a single variable-namely, the ratio of particle diameter to nanowire length. The model's predictions are validated against numerical simulation data, demonstrating its accuracy.image