Deep-Learning-Assisted Thermogalvanic Hydrogel E-Skin for Self-Powered Signature Recognition and Biometric Authentication

Self-powered electronic skins (e-skins), as on-skin human-machine interfaces, play a significant role in cyber security and personal electronics. However, current self-powered e-skins are primarily constrained by complex fabricating process, intrinsic stiffness, signal distortion under deformation, and inadequate comprehensive performance, thereby hindering their practical applications. Herein, a novel highly stretchable (534.5%), ionic conductive (4.54 S m-1), thermogalvanic (1.82 mV K-1) hydrogel (TGH) is facilely fabricated by a one-pot method. Owing to the formation of Li+(H2O)n hydration structure, the TGH presents excellent anti-freezing and non-drying performance. It remains flexible and conductive (3.86 S m-1) at -20 degrees C and shows no obvious degradation in the thermoelectrical performance over 10 days. Besides, acting as a self-powered e-skin, the TGH combined with deep learning technology for signature recognition and biometric authentication is successfully demonstrated, achieving an accuracy of 92.97%. This work exhibits the TGH-based e-skin's tremendous potential in the new generation of human-computer interaction and information security. A highly stretchable (534.5%), conductive (4.54 S m-1), thermogalvanic (1.82 mV K-1) hydrogel is fabricated, which remains conductive (3.86 S m-1) at -20 degrees C and hardly shows degradation in thermoelectrical performance over 10 days. Besides, acting as a self-powered e-skin, the hydrogel is combined with deep learning technology for signature recognition and biometric authentication, achieving an accuracy of 92.97%.image