journal article·2021-09

Design of flexible strain sensor with both ultralow detection limit and wide sensing range via the multiple sensing mechanisms

Jianwen Chen, Guoxuan Zhu, Fei Wang, Youquan Xu, Chengbao Wang, Yutian Zhu, Wei Jiang

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Abstract

Abstract An increasing utilization of flexible and wearable electronics in human motion monitoring, healthcare monitoring and electronic skin urges the rapid development of flexible strain sensors. However, it is still a challenge to fabricate flexible strain sensors with both ultralow detection limit and wide sensing range. Herein, we develop a universal strategy to design the flexible strain sensor with both ultralow detection limit and wide sensing range based on the dual sensing layers with multiple sensing mechanisms, which can combine the advantages of sensing mechanisms of tunneling effect and crack propagation. Attributed to the multiple sensing mechanisms, the designed strain sensor possesses an ultralow detection limit of 0.01% and a wide sensing range (The maximum sensing strain is greater than 100%, high enough for all human motion monitoring). Moreover, excellent stretchability, fast response (68 m s)/recovery (68 m s), and outstanding reproducibility are achieved by this dual-sensing-layer sensor. Because of these outstanding performances, this strain sensor is capable of detecting and distinguishing both subtle physiological movements (e.g. pulse, coughing, and swallowing) and large-scale human motions (e.g. walking, leg lifting, and squatting), indicating great potential applications in wearable electronics. This work proposes a new strategy, i.e. constructing multilayer sensing structure with multiple response mechanisms, which may overcome the long-standing challenge of high-performance flexible strain sensor.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Tactile and Sensory Interactions · Gas Sensing Nanomaterials and Sensors

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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