A 2D percolation-based model for characterizing the piezoresistivity of carbon nanotube-based films
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Carbon nanotubes (CNTs) have attracted considerable attention due to their unique electrical, mechanical, and electromechanical properties. In particular, thin films formed by embedding CNTs in polymer matrices have been shown to exhibit strain-sensitive electromechanical properties, which can serve as an alternative to traditional strain sensors. Although numerous experimental studies have characterized their electrical properties and piezoresistivity, it remains unclear as to what nano-scale mechanisms dominate to govern nanocomposite electromechanical properties. Therefore, the objective of this study is to create a two-dimensional (2D) percolation-based numerical model to understand the electrical and coupled electromechanical behavior of CNT-based thin films. First, a percolation-based model with randomly dispersed straight nanotubes was generated. Second, the percolation and unstrained electrical properties of the model were evaluated as a function of CNT density and length. Next, uniaxial tensile–compressive strains were applied to the model for characterizing their electromechanical response and piezoresistivity. In addition, the effects of different intrinsic strain sensitivities of individual nanotubes were also considered. The results showed that bulk film strain sensitivity was strongly related to CNT density, length, and its intrinsic strain sensitivity. In particular, it was found that strain sensitivity decreased with increasing CNT density. While these strain sensitivity trends were consistent for different intrinsic CNT gage factors, the results were more complicated near the percolation threshold. These results were also compared to other experimental research so as to understand how different nano-scale parameters propagate and affect bulk film response.
KeywordsPercolation Threshold Strain Sensitivity Gage Factor Electromechanical Property Bulk Film
The authors thank the U.S. National Science Foundation (NSF), under Grant number CMMI-CAREER 1253564, for the support of this research.
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