Deep-Learning Enabled Active Biomimetic Multifunctional Hydrogel Electronic Skin.

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Abstract

There is huge demand for recreating human skin with the functions of epidermis and dermis for interactions with the physical world. Herein, a biomimetic, ultrasensitive, and multifunctional hydrogel-based electronic skin (BHES) was proposed. Its epidermis function was mimicked using poly(ethylene terephthalate) with nanoscale wrinkles, enabling accurate identification of materials through the capabilities to gain/lose electrons during contact electrification. Internal mechanoreceptor was mimicked by interdigital silver electrodes with stick-slip sensing capabilities to identify textures/roughness. The dermis function was mimicked by patterned microcone hydrogel, achieving pressure sensors with high sensitivity (17.32 mV/Pa), large pressure range (20-5000 Pa), low detection limit, and fast response (10 ms)/recovery time (17 ms). Assisted by deep learning, this BHES achieved high accuracy and minimized interference in identifying materials (95.00% for 10 materials) and textures (97.20% for four roughness cases). By integrating signal acquisition/processing circuits, a wearable drone control system was demonstrated with three-degree-of-freedom movement and enormous potentials for soft robots, self-powered human-machine interaction interfaces of digital twins.