A probabilistic micromechanical modeling for electrical properties of nanocomposites with multi-walled carbon nanotube morphology

The nanoscopic characteristics of the multi-walled carbon nanotubes (MWCNTs) used in composites are crucial for attempting to understand and design nanocomposites of a novel class. We investigate the correlations between the nanofiller properties and effective electrical properties of MWCNT-embedded polycarbonate composites by theoretical and experimental approaches. A probabilistic computational model is proposed to predict the influence of MWCNT morphology on the electrical behaviors of MWCNTs-embedded polymer composites. A parameter optimization method in accordance with a genetic algorithm is then applied to the model, resulting that the ideal sets of model constant for the simulation are computationally estimated. For the experimental validation purpose, a comparison between the present theoretical and experimental results is made to assess the capability of the proposed methods. In overall, good agreement between the predictions and experimental results can be observed and the electrical performance of the composites can be improved as the MWCNT length increases.