Electrical conduction in carbon nanotubes under mechanical deformations

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The enormous potential of carbon nanotubes (CNTs) as primary components in electronic devices and NEMS necessitates the understanding and predicting of the effects of mechanical deformation on electron transport in CNTs. In principle, detailed atomic/electronic calculations can provide both the deformed configuration and the resulting electrical transport behavior of the CNT. However, the computational expense of these simulations limits the size of the CNTs that can be studied with this technique and a direct analysis of CNTs of the dimension used in nano-electronic devices, particularly multi-wall CNTs (MWNTs), seems prohibitive at the present. Here a computationally effective mixed finite element/tight-binding (to be referred to as FE-TB) approach able to simulate the electromechanical behavior of CNTs devices is presented. The FE-based structural procedure computes the mechanical deformation of the CNTs and provides a tight-binding (TB) code with the atomic coordinates in the deformed configuration. The TB code is carefully designed to realize orders-of-magnitude reduction in computational time in calculating deformation-induced changes in electrical transport properties of the nanotubes. The FE-TB computational approach is validated in a simulation of laboratory experiments on a multiwall CNT and then used to demonstrate the role of the multiwall structure in providing robustness to conductivity in the event of imposed mechanical deformations.
Original languageEnglish
Title of host publicationTrends in Computational Nanomechanics: Transcending Length and Time Scales
Pages335-363
Number of pages29
Publication statusPublished - 2009

Publication series

NameChallenges and Advances in Computational Chemistry and Physics

Fingerprint

Dive into the research topics of 'Electrical conduction in carbon nanotubes under mechanical deformations'. Together they form a unique fingerprint.

Cite this