Jump to main content
Jump to site search

Issue 24, 2017
Previous Article Next Article

Self-adapted and tunable graphene strain sensors for detecting both subtle and large human motions

Author affiliations

Abstract

Conventional strain sensors rarely have both a high gauge factor and a large strain range simultaneously, so they can only be used in specific situations where only a high sensitivity or a large strain range is required. However, for detecting human motions that include both subtle and large motions, these strain sensors can't meet the diverse demands simultaneously. Here, we come up with laser patterned graphene strain sensors with self-adapted and tunable performance for the first time. A series of strain sensors with either an ultrahigh gauge factor or a preferable strain range can be fabricated simultaneously via one-step laser patterning, and are suitable for detecting all human motions. The strain sensors have a GF of up to 457 with a strain range of 35%, or have a strain range of up to 100% with a GF of 268. Most importantly, the performance of the strain sensors can be easily tuned by adjusting the patterns of the graphene, so that the sensors can meet diverse demands in both subtle and large motion situations. The graphene strain sensors show significant potential in applications such as wearable electronics, health monitoring and intelligent robots. Furthermore, the facile, fast and low-cost fabrication method will make them possible and practical to be used for commercial applications in the future.

Graphical abstract: Self-adapted and tunable graphene strain sensors for detecting both subtle and large human motions

Back to tab navigation

Supplementary files

Publication details

The article was received on 16 Mar 2017, accepted on 22 May 2017 and first published on 24 May 2017


Article type: Paper
DOI: 10.1039/C7NR01862B
Citation: Nanoscale, 2017,9, 8266-8273
  •   Request permissions

    Self-adapted and tunable graphene strain sensors for detecting both subtle and large human motions

    L. Tao, D. Wang, H. Tian, Z. Ju, Y. Liu, Y. Pang, Y. Chen, Y. Yang and T. Ren, Nanoscale, 2017, 9, 8266
    DOI: 10.1039/C7NR01862B

Search articles by author

Spotlight

Advertisements