Skin-inspired flexible and high-performance MXene@polydimethylsiloxane piezoresistive pressure sensor for human motion detection

In recent years, flexible high-performance piezoresistive pressure sensors have attracted considerable attention for the important application potential in the emerging fields of smart robots, wearable electronics and electronic skin. Herein, inspired by human skin, a new strategy was proposed for the fabrication of a double-layer piezoresistive pressure sensor with wide sensing range and high sensitivity. It was based on the utilization of sandpaper as template and MXene for the constructions of micro-protrusion rough surface on polydimethylsiloxane film and electrically conductive pathways, respectively. The prepared sensor demonstrated high sensitivity of 2.6 kPa(-1) in wide linear range of 0-30 kPa, fast response/ recovery time of 40/40 ms and excellent repeatability. Importantly, the sensor was successfully applied for the real-time detection of radial artery heart rate, limb movement, handwriting and vocal cord vocalization. Moreover, the integrated device by the sensors had the capability of identifying and visualizing spatial pressure distribution. The findings conceivably stand out a new methodology to prepare flexible high-performance piezoresistive pressure sensors for wearable electronics, human-computer interaction, intelligent robots and health monitoring. (C) 2022 Elsevier Inc. All rights reserved.

Automated summary

Flexible high-performance piezoresistive pressure sensors have gained significant attention for their potential applications in smart robots, wearable electronics, and electronic skin. This study presents a new strategy for fabricating a double-layer piezoresistive pressure sensor with a wide sensing range and high sensitivity, inspired by human skin. The sensor exhibited high sensitivity, fast response/recovery time, and excellent repeatability. It was successfully applied for real-time detection of various physiological activities and spatial pressure distribution visualization.