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Carbon nanotube thin film strain sensor models assembled using nano- and micro-scale imaging

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Abstract

Nanomaterial-based thin films, particularly those based on carbon nanotubes (CNT), have brought forth tremendous opportunities for designing next-generation strain sensors. However, their strain sensing properties can vary depending on fabrication method, post-processing treatment, and types of CNTs and polymers employed. The objective of this study was to derive a CNT-based thin film strain sensor model using inputs from nano-/micro-scale experimental measurements of nanotube physical properties. This study began with fabricating ultra-low-concentration CNT-polymer thin films, followed by imaging them using atomic force microscopy. Image processing was employed for characterizing CNT dispersed shapes, lengths, and other physical attributes, and results were used for building five different types of thin film percolation-based models. Numerical simulations were conducted to assess how the morphology of dispersed CNTs in its 2D matrix affected bulk film electrical and electromechanical (strain sensing) properties. The simulation results showed that CNT morphology had a significant impact on strain sensing performance.

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Acknowledgements

This research was supported by the US National Science Foundation (NSF) Faculty Early Career Development (CAREER) Program under Grant Number CMMI-1253564. Additional support was provided by the Jacobs School of Engineering, University of California-San Diego.

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Authors and Affiliations

  1. Department of Structural Engineering, University of California-San Diego, La Jolla, CA, 92093-0085, USA

    Bo Mi Lee & Kenneth J. Loh

  2. Department of Civil Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan

    Yuan-Sen Yang

Authors
  1. Bo Mi Lee
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  2. Kenneth J. Loh
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  3. Yuan-Sen Yang
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Correspondence to Kenneth J. Loh.

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Lee, B.M., Loh, K.J. & Yang, YS. Carbon nanotube thin film strain sensor models assembled using nano- and micro-scale imaging. Comput Mech 60, 39–49 (2017). https://doi.org/10.1007/s00466-017-1391-6

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