4.8 Article

Fingerprint-Inspired Strain Sensor with Balanced Sensitivity and Strain Range Using Laser-Induced Graphene

Journal

ACS APPLIED MATERIALS & INTERFACES
Volume 14, Issue 1, Pages 1315-1325

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsami.1c16646

Keywords

laser-induced graphene; bioinspired structure; microcrack; strain sensor; resistive

Funding

  1. Natural Science Foundation of Guangdong Province, China [2021B1515020087]
  2. National Natural Science Foundation of China [51775197]

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Inspired by the characteristics of human fingertip skin, a fingerprint-inspired strain sensor exhibits fast response time, balanced sensitivity and strain range, and good reliability. Self-organized microcracks in weak mechanical areas cause significant resistance changes during reconnection/disconnection but irreversibly fail after excessive stretching, demonstrating the robust function of fingerprint-inspired sensors.
Sensitivity and strain range are two mutually exclusive features of strain sensors, where a significant improvement in flexibility is usually accompanied by a reduction in sensitivity. The skin of a human fingertip, due to its undulating fingerprint pattern, can easily detect environmental signals and enhances sensitivity without losing elasticity. Inspired by this characteristic, laser-induced graphene (LIG) with a fingerprint structure is prepared in one step on a polyimide (PI) film and transferred into an Ecoflex substrate to assemble resistive strain sensors. Experimentally, the fingerprint-inspired strain sensor exhibits a superfast response time (similar to 70 ms), balanced sensitivity and strain range (a gauge factor of 191.55 in the 42-50% strain range), and good reliability (>1500 cycles). Self-organized microcracks, initiated in weak mechanical areas, cause prominent resistance changes during reconnection/disconnection but irreversibly fail after excessive stretching. The robust function of fingerprint-inspired sensors is further demonstrated by real-time monitoring of tiny pulses, large body movements, gestures, and voice recognition.

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