Electrical conductivity of random and aligned nanocomposites: Theoretical models and experimental validation. (October 2021)

• Record Type: Journal Article
• Authors: Chanda, Amit; Sinha, Sujeet K.; Datla, Naresh V.
• Is Part Of: Composites. Volume 149(2021)
• Publication Date: 2021-10
• Subjects: A. Carbon nanotubes and nanofibers -- A. Polymer-matrix composites (PMCs) -- B. Electrical properties -- C. Analytical modelling -- Percolation threshold; Composite materials -- Periodicals; Manufacturing processes -- Periodicals; Composite materials; Manufacturing processes; Periodicals; 620.11805
• DOI: 10.1016/j.compositesa.2021.106543
• Languages: English
• ISSNs: 1359-835X

Abstract

Highlights: Analytical models predict electrical conductivity of random/aligned nanocomposites. Micromechanics based model and fiber network-based model were proposed. Modelled filler geometry, concentration, orientation, interphase and percolation. Electron tunneling was considered as the major contributor of conductivity. Both models were validated with conductivity data from experiments and literature. Abstract: In this study, two different analytical models were developed to predict electrical conductivity of random and aligned nanofiber/polymer composites. Model-I calculated conductivity using micromechanics based analytical equation and model-II calculates effective conductivity by considering the composite as simplified fiber–fiber contact network based equivalent resistor model. The models incorporated the effect of filler geometry, concentration, orientation, interphase and percolation threshold. Electron tunneling along thin insulating layers between conductive fillers was considered as the major contributor of conductivity. The proposed models were validated with measured conductivity from random and electric field aligned CNF/epoxy composites of different concentrations. The models were further validated with literature data on the conductivity of random and aligned CNT and CNF/polymer composites. The proposed models can also be extended to predict electrical conductivity of nanocomposites with fillers with different geometry.