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Proceedings Paper

Modeling the electromechanical and strain response of carbon nanotube-based nanocomposites
Author(s): Bo Mi Lee; Kenneth J. Loh; Andrew R Burton; Bryan R. Loyola
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Paper Abstract

Over the last few decades, carbon nanotube (CNT)-based thin films or nanocomposites have been widely investigated as a multifunctional material. The proposed applications extend beyond sensing, ultra-strong coatings, biomedical grafts, and energy harvesting, among others. In particular, thin films characterized by a percolated and random distribution of CNTs within a flexible polymeric matrix have been shown to change its electrical properties in response to applied strains. While a plethora of experimental work has been conducted, modeling their electromechanical response remains challenging. Furthermore, their design and optimization require the derivation of accurate electromechanical models that could predict thin film response to applied strains. Thus, the objective of this study is to implement a percolation-based piezoresistive model that could explain the underlying mechanisms for strain sensing. First, a percolation-based model with randomly distributed, straight CNTs was developed in MATLAB. Second, the number of CNTs within a unit area was varied to explore its influence on percolation probability. Then, to understand how the film’s electrical properties respond to strain, two different models were implemented. Both models calculated the geometrical response of the film and CNTs due to applied uniaxial strains. The first model considered the fact that the electrical resistance of individual CNTs changed depending solely on its length between junctions. The other model further explored the idea of incorporating strain sensitivity of individual CNTs. The electromechanical responses and the strain sensitivities of the two models were compared by calculating how their bulk resistance varied due to applied tensile and compressive strains. The numerical model results were then qualitatively compared to experimental results reported in the literature.

Paper Details

Date Published: 10 April 2014
PDF: 11 pages
Proc. SPIE 9061, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2014, 906117 (10 April 2014); doi: 10.1117/12.2044566
Show Author Affiliations
Bo Mi Lee, Univ. of California, Davis (United States)
Kenneth J. Loh, Univ. of California, Davis (United States)
Andrew R Burton, Sandia National Labs. (United States)
Univ. of Michigan (United States)
Bryan R. Loyola, Sandia National Labs. (United States)


Published in SPIE Proceedings Vol. 9061:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2014
Jerome P. Lynch; Kon-Well Wang; Hoon Sohn, Editor(s)

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