Issue 26, 2016

Electrically conductive strain sensing polyurethane nanocomposites with synergistic carbon nanotubes and graphene bifillers

Abstract

Thermoplastic polyurethane (TPU) based conductive polymer composites (CPCs) with a reduced percolation threshold and tunable resistance–strain sensing behavior were obtained through the addition of synergistic carbon nanotubes (CNT) and graphene bifillers. The percolation threshold of graphene was about 0.006 vol% when the CNT content was fixed at 0.255 vol% that is below the percolation threshold of CNT/TPU nanocomposites. The synergistic effect between graphene and CNT was identified using the excluded volume theory. Graphene acted as a ‘spacer’ to separate the entangled CNTs from each other and the CNT bridged the broad gap between individual graphene sheets, which was beneficial for the dispersion of CNT and formation of effective conductive paths, leading to better electrical conductivity at a lower conductive filler content. Compared with the dual-peak response pattern of the CNT/TPU based strain sensors, the CPCs with hybrid conductive fillers displayed single-peak response patterns under small strain, indicating good tunability with the synergistic effect of CNT and graphene. Under larger strain, prestraining was adopted to regulate the conductive network, and better tunable single-peak response patterns were also obtained. The CPCs also showed good reversibility and reproductivity under cyclic extension. This study paves the way for the fabrication of CPC based strain sensors with good tunability.

Graphical abstract: Electrically conductive strain sensing polyurethane nanocomposites with synergistic carbon nanotubes and graphene bifillers

Supplementary files

Article information

Article type
Paper
Submitted
16 Marts 2016
Accepted
27 Maijs 2016
First published
27 Maijs 2016

Nanoscale, 2016,8, 12977-12989

Electrically conductive strain sensing polyurethane nanocomposites with synergistic carbon nanotubes and graphene bifillers

H. Liu, J. Gao, W. Huang, K. Dai, G. Zheng, C. Liu, C. Shen, X. Yan, J. Guo and Z. Guo, Nanoscale, 2016, 8, 12977 DOI: 10.1039/C6NR02216B

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