Multiscale modeling and numerical analyses of the electric conductivity of CNT/polymer nanocomposites taking into account the tunneling effect. (14th September 2021)

• Record Type: Journal Article
• Authors: Lu, Xiaoxin; Pichon, Lionel; Bai, Jinbo
• Other Names: Yan Su guestEditor.; Liu Yang guestEditor.
• Is Part Of: International journal of numerical modelling. Volume 34:Number 6(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-14
• Subjects: carbon nanotubes (CNTs) -- electric conductivity -- finite element method (FEM) -- nanocomposites -- percolation threshold -- tunneling effect; Electric networks -- Mathematical models -- Periodicals; Electronics -- Mathematical models -- Periodicals; 621.3011
• DOI: 10.1002/jnm.2955
• Languages: English
• ISSNs: 0894-3370

Abstract

Abstract: Tunneling effect plays a significant part in the extremely low electric percolation threshold and large conductivity of carbon nanotube (CNT)/polymer nanocomposites, which allows electric conduction between two CNTs separated at nanometric distances. In this work, a numerical model taking into account the nonlinear tunneling effect is proposed to evaluate the effective electric conductivity of CNT nanocomposites. The nonlinear finite element formulation is introduced, as well as the definitions of effective quantities for homogenization. Moreover, the CNTs are modeled by highly conducting line segments in order to avoid meshing the thin cylindrical tubes. With this technique, the percolation behavior of the composites has been simulated, and the effects of barrier height, CNT length distribution, CNT aspect ratio as well as alignment have been estimated. It turns out that the barrier height dominates the maximum electric conductivity, while the CNT aspect ratio determines the percolation threshold value, respectively. In addition, the anisotropic behavior is obtained by aligning the CNTs, which also results in higher percolation threshold compared with randomly distributed CNTs. Finally, the results are validated by available experimental data.