Bioinspired All-Fibrous Directional Moisture-Wicking Electronic Skins for Biomechanical Energy Harvesting and All-Range Health Sensing

Abstract

HighlightsBioinspired directional moisture-wicking electronic skin (DMWES) was successfully realized by surface energy gradient and push–pull effect via the design of distinct hydrophobic-hydrophilic difference.The DMWES membrane showed excellent comprehensive pressure sensing performance with high sensitivity and good single-electrode triboelectric nanogenerator performanceThe superior pressure sensing and triboelectric performance enabled the DMWES for all-range healthcare sensing, including accurate pulse monitoring, voice recognition, and gait recognition.Electronic skins can monitor minute physiological signal variations in the human skins and represent the body’s state, showing an emerging trend for alternative medical diagnostics and human–machine interfaces. In this study, we designed a bioinspired directional moisture-wicking electronic skin (DMWES) based on the construction of heterogeneous fibrous membranes and the conductive MXene/CNTs electrospraying layer. Unidirectional moisture transfer was successfully realized by surface energy gradient and push–pull effect via the design of distinct hydrophobic-hydrophilic difference, which can spontaneously absorb sweat from the skin. The DMWES membrane showed excellent comprehensive pressure sensing performance, high sensitivity (maximum sensitivity of 548.09 kPa−1), wide linear range, rapid response and recovery time. In addition, the single-electrode triboelectric nanogenerator based on the DMWES can deliver a high areal power density of 21.6 µW m−2 and good cycling stability in high pressure energy harvesting. Moreover, the superior pressure sensing and triboelectric performance enabled the DMWES for all-range healthcare sensing, including accurate pulse monitoring, voice recognition, and gait recognition. This work will help to boost the development of the next-generation breathable electronic skins in the applications of AI, human–machine interaction, and soft robots.