4.7 Article

A highly stretchable and stable strain sensor based on hybrid carbon nanofillers/polydimethylsiloxane conductive composites for large human motions monitoring

期刊

COMPOSITES SCIENCE AND TECHNOLOGY
卷 156, 期 -, 页码 276-286

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.compscitech.2018.01.019

关键词

Flexible composites; Nano particles; Polymer-matrix composites (PMCs); Electrical properties

资金

  1. National Natural Science Foundation [51603193, 51773183, 11572290, 11432003]
  2. National Natural Science Foundation of China-Henan Province Joint Funds [U1604253]
  3. China Postdoctoral Science Foundation [2015M580637, 2016790675]
  4. State Key Laboratory of Polymer Materials Engineering (Sichuan University) [sklpme2016-4-21]
  5. Zhengzhou University [1421320041]

向作者/读者索取更多资源

Stretchable strain sensors have promising potentials in wearable electronics for human motion detection, health monitoring and so on. A reliable strain sensor with high flexibility and good stability should be designed to detect human joints motions with a large deformation. Here, a simple and facile solution mixing-casting method was adopted to fabricate a highly stretchable strain sensor based on composites mixing polydimethylsiloxane (PDMS) with hybrid carbon nanotubes (CNTs) and carbon black (CB) conductive nanofillers (CNTs-CB). Bridged and overlapped hybrid CNTs-CB nanofillers structure was achieved in the composite on the basis of the morphology observation. In monotonic stretching test, the CNTs-CB/PDMS composites strain sensors exhibited high stretchability, strain-dependent sensitivity in a wide strain sensing range (ca. 300% strain) and an excellent linear current-voltage behavior. During stretching-releasing cycles, the strain sensors presented excellent repeatability, good stability and superior durability (2500 cycles at 200% strain). Combined with the above outstanding strain sensing performances, the fabricated stretchable strain sensors were attached onto different joints of human body to monitor the corresponding human motions, demonstrating their attractive perspective in large human motions detection. (C) 2018 Elsevier Ltd. All rights reserved.

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