Modelling the effects of carbon nanotube length non-uniformity and waviness on the electrical behavior of polymer composites

被引:20
作者
Wang, De-Yang [1 ]
Tang, Zhen-Hua [1 ]
Huang, Pei [1 ]
Li, Yuan-Qing [1 ]
Fu, Shao-Yun [1 ]
机构
[1] Chongqing Univ, Coll Aerosp Engn, Chongqing 400044, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Polymer nanocomposite; Carbon nanotube; Percolation behavior; Electrical conductivity; CONDUCTIVITY; NANOCOMPOSITES; INTERPHASE;
D O I
10.1016/j.carbon.2022.09.070
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Carbon nanotube (CNT) length non-uniformity and waviness exist inevitably in practical CNT/polymer com-posites (CPCs) and are two major factors that have significant influences on the composite electrical behavior. However, these two factors have been rarely considered simultaneously in previous modellings. In this study, a numerical model is presented to predict the electrical behavior of CPCs by simultaneously taking into account the two factors besides other ones considered in existing modellings. The length non-uniformity of CNTs is repre-sented by the Weibull distribution and each wavy CNT is modeled by a piecewise sequence of several line segments. Besides, the tunneling distance is reasonably defined to calculate the tunneling resistance between adjacent CNTs. The prediction results reveal that both the CNT length non-uniformity and waviness have sig-nificant effects on the electrical behavior of CPCs. It is shown that the percolation threshold evidently decreases with either increasing the CNT length non-uniformity or decreasing the CNT waviness degree. Furthermore, the predictions agree well with existing experimental data, indicating the validity of the present numerical model in predicting the electrical conductivity of CPCs. In particular, it should be emphasized that compared with the previous models without simultaneously considering the effects of these two factors, the present model provides better predictions of the electrical behavior of CPCs.
引用
收藏
页码:910 / 919
页数:10
相关论文
共 49 条
[1]   Electrical conductivity and dielectric properties of multiwalled carbon nanotube and alumina composites [J].
Ahmad, Kaleem ;
Pan, Wei ;
Shi, Sui-Lin .
APPLIED PHYSICS LETTERS, 2006, 89 (13)
[2]   Effect of carbon nanotube geometry upon tunneling assisted electrical network in nanocomposites [J].
Bao, W. S. ;
Meguid, S. A. ;
Zhu, Z. H. ;
Pan, Y. ;
Weng, G. J. .
JOURNAL OF APPLIED PHYSICS, 2013, 113 (23)
[3]   Tunneling resistance and its effect on the electrical conductivity of carbon nanotube nanocomposites [J].
Bao, W. S. ;
Meguid, S. A. ;
Zhu, Z. H. ;
Weng, G. J. .
JOURNAL OF APPLIED PHYSICS, 2012, 111 (09)
[4]   Modeling electrical conductivities of nanocomposites with aligned carbon nanotubes [J].
Bao, W. S. ;
Meguid, S. A. ;
Zhu, Z. H. ;
Meguid, M. J. .
NANOTECHNOLOGY, 2011, 22 (48)
[5]   Modeling percolation in high-aspect-ratio fiber systems. II. The effect of waviness on the percolation onset [J].
Berhan, L. ;
Sastry, A. M. .
PHYSICAL REVIEW E, 2007, 75 (04)
[6]   Coupled electromechanical modeling of piezoresistive behavior of CNT-reinforced nanocomposites with varied morphology and concentration [J].
Chen, X. ;
Alian, A. R. ;
Meguid, S. A. .
EUROPEAN JOURNAL OF MECHANICS A-SOLIDS, 2020, 84
[7]   Numerical investigation on the influence factors of the electrical properties of carbon nanotubes-filled composites [J].
De Vivo, B. ;
Lamberti, P. ;
Spinelli, G. ;
Tucci, V. .
JOURNAL OF APPLIED PHYSICS, 2013, 113 (24)
[8]   An analytical model of effective electrical conductivity of carbon nanotube composites [J].
Deng, Fei ;
Zheng, Quan-Shui .
APPLIED PHYSICS LETTERS, 2008, 92 (07)
[9]   Uncertainty quantification of percolating electrical conductance for wavy carbon nanotube-filled polymer nanocomposites using Bayesian inference [J].
Doh, Jaehyeok ;
Park, Sang-In ;
Yang, Qing ;
Raghavan, Nagarajan .
CARBON, 2021, 172 :308-323
[10]   A Monte Carlo model with equipotential approximation and tunneling resistance for the electrical conductivity of carbon nanotube polymer composites [J].
Fang, Chao ;
Zhang, Juanjuan ;
Chen, Xiqu ;
Weng, George J. .
CARBON, 2019, 146 :125-138