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

• DOI: 10.1002/adfm.202314419
• Publication Date: 2024-01-12T06:17:16Z
• Authors: Ning Li; Zhaosu Wang; Xinru Yang; Zhiyi Zhang; Wengdong Zhang; Shengbo Sang; Hulin Zhang
• Keywords: Signature (topology); SIGNAL (programming language)

Abstract 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, inadequate comprehensive performance, thereby hindering their practical applications. Herein, novel highly stretchable (534.5%), ionic conductive (4.54 S m −1 ), thermogalvanic (1.82 mV K ) hydrogel (TGH) is facilely fabricated one‐pot method. Owing to the formation of Li + (H 2 O) n hydration structure, TGH presents excellent anti‐freezing non‐drying performance. It remains flexible (3.86 at −20 °C shows no obvious degradation thermoelectrical performance over 10 days. Besides, acting e‐skin, combined with deep learning technology for signature recognition biometric authentication successfully demonstrated, achieving an accuracy 92.97%. This work exhibits TGH‐based e‐skin's tremendous potential new generation human‐computer interaction information security.