MODELING MORPHOLOGICAL EFFECTS OF CONDUCTIVE NANOFILLER NETWORKS IN POLYMER COMPOSITES

Abstract

The electrical characteristics (e.g., conductivity and piezoresistivity) of conductive nanofiller/polymer composites are strongly dependent on the morphology of their internal networks. Predictive modeling remains a challenge in describing how nanoscale configurations translate into bulk properties due to the intricate connectivity of percolating nanofillers. This work introduces an integrated modeling framework to bridge this gap, developed for carbon nanotube (CNT) networks in polymer composites. Monte Carlo simulations are employed to evaluate the electrical conductivity of a domain comprising realistic CNT geometries with spline-based waviness and statistical length variation. Novel descriptors are defined to unify the effects of various morphological parameters. The model is extended to elucidate the mechanisms of piezoresistivity by coupling with finite element mechanical analysis. This approach reveals that the global piezoresistive response is closely correlated with the proposed descriptor and is dominated by a small subset of active tunneling junctions engaged in a hierarchical activation sequence. A theoretical model is developed to analyze the electromechanical behavior of CNT/polymer nanocomposites under uniaxial deformation. The method accounts for critical factors such as field-induced alignment and agglomeration morphology through an effective resistor formulation. Validation against experimental data for both random and aligned CNT configurations confirms its reliability, while systematic analysis of individual parameters identifies optimal conditions for reducing the percolation threshold and enhancing piezoresistive sensitivity. This work provides a mechanistically grounded and quantitatively validated understanding of structure–property relationships in CNT composites. These modeling efforts may serve as a useful platform for future investigations into the geometric determinants of charge transport in one-dimensional conductive nanofiller networks, guiding the design and optimization of multifunctional materials.