Electrical conductivity modeling and experimental study of densely packed SWCNT networks

• Published in: Nanotechnology 2010-05, Vol.21 (19), p.195703-195703
• Main Authors: Jack, D A, Yeh, C-S, Liang, Z, Li, S, Park, J G, Fielding, J C
• Format: Article
• Language: English
• Subjects: Electrical conductivity; Electrical resistivity; Mathematical models; Nanostructure; Networks; Orientation; Resistivity; Single wall carbon nanotubes; Stochasticity
• ISSN: 0957-4484; 1361-6528
• DOI: 10.1088/0957-4484/21/19/195703

Single-walled carbon nanotube (SWCNT) networks have become a subject of interest due to their ability to support structural, thermal and electrical loadings, but to date their application has been hindered due, in large part, to the inability to model macroscopic responses in an industrial product with any reasonable confidence. This paper seeks to address the relationship between macroscale electrical conductivity and the nanostructure of a dense network composed of SWCNTs and presents a uniquely formulated physics-based computational model for electrical conductivity predictions. The proposed model incorporates physics-based stochastic parameters for the individual nanotubes to construct the nanostructure such as: an experimentally obtained orientation distribution function, experimentally derived length and diameter distributions, and assumed distributions of chirality and registry of individual CNTs. Case studies are presented to investigate the relationship between macroscale conductivity and nanostructured variations in the bulk stochastic length, diameter and orientation distributions. Simulation results correspond nicely with those available in the literature for case studies of conductivity versus length and conductivity versus diameter. In addition, predictions for the increasing anisotropy of the bulk conductivity as a function of the tube orientation distribution are in reasonable agreement with our experimental results. Examples are presented to demonstrate the importance of incorporating various stochastic characteristics in bulk conductivity predictions. Finally, a design consideration for industrial applications is discussed based on localized network power emission considerations and may lend insight to the design engineer to better predict network failure under high current loading applications.