A new analytical model for predicting the electrical conductivity of carbon nanotube nanocomposites. (February 2021)

• Record Type: Journal Article
• Authors: Tang, Zhen-Hua; Li, Yuan-Qing; Huang, Pei; Fu, Ya-Qin; Hu, Ning; Fu, Shao-Yun
• Is Part Of: Composites communications. Volume 23(2021)
• Publication Date: 2021-02
• Subjects: Carbon nanotube -- Polymer composite -- Electrical conductivity -- Modelling
• DOI: 10.1016/j.coco.2020.100577
• Languages: English
• ISSNs: 2452-2139

Abstract

Abstract: In predicting the electrical conductivity (EC) of carbon nanotube (CNT)/polymer nanocomposites (CPCs), previous analytical models did not simultaneously consider the effects of CNT waviness and dispersion state, which exist inevitably for real CPCs. In this work, an effective and comprehensive analytical model is developed to predict the percolation threshold and the EC of CPCs by taking into account the effects of CNT waviness and dispersion, etc. A detailed investigation of the effects of CNT content, conductivity, size, waviness and dispersion is conducted on the EC of CPCs. In particular, it is shown that both CNT waviness and dispersion play significant roles in determining the composite EC. Moreover, the predicted results agree well with the existing experimental results and display a higher prediction accuracy compared with the previous typical models for predicting the composite EC. Analytical results indicate that a high EC can be achieved for CPCs with CNTs of a high conductivity, a large aspect ratio (AR), a perfect dispersion state and a low degree of waviness. Highlights: An effective model is proposed for predicting the electrical conductivity of carbon nanotube (CNT)/polymer composites. The theoretical model can be used to predict the electrical conductivity of the composites precisely. The model can evaluate the effects of CNT waviness and dispersion on the electrical conductivity of the composites.