journal article·2022-05

Porous fiber paper and 3D patterned electrodes composed high-sensitivity flexible piezoresistive sensor for physiological signal monitoring

XiaoJuan Hou, JiXin Zhong, Jian He, ChangJun Yang, JunBin Yu, LinYu Mei, JiLiang Mu, WenPing Geng, XiuJian Chou

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Abstract

The research on flexible pressure sensors has drawn widespread attention in recent years, especially in the fields of health care and intelligent robots. In practical applications, the sensitivity of sensors directly affects the precision and integrity of weak pressure signals. Here, a pressure sensor with high sensitivity and a wide measurement range composed of porous fiber paper and 3D patterned electrodes is proposed. Multi-walled carbon nanotubes with excellent conductivity were evenly sprayed on the fiber paper to form the natural spatial conducting networks, while the copper-deposited polydimethylsiloxane films with micro-pyramids array were used as electrodes and flexible substrates. Increased conducting paths between electrodes and fibers can be obtained when high-density micro-pyramids fall into the porous structures of the fiber paper under external pressure, thereby promoting the pressure sensor to show an ultra-high sensitivity of 17.65 kPa−1 in the pressure range of 0–2 kPa, 16 times that of the device without patterned electrodes. Besides, the sensor retains a high sensitivity of 2.06 kPa−1 in an ultra-wide measurement range of 150 kPa. Moreover, the sensor can detect various physiological signals, including pulse and voice, while attached to the human skin. This work provides a novel strategy to significantly improve the sensitivity and measurement range of flexible pressure sensors, as well as demonstrates attractive applications in physiological signal monitoring.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Muscle activation and electromyography studies · Tactile and Sensory Interactions

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

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