Self‐Powered Machine‐Learning‐Assisted Material Identification Enabled by a Thermogalvanic Dual‐Network Hydrogel with a High Thermopower

Abstract

Wearable devices equipped with high‐performance flexible sensors that can identify diverse physical information free from batteries are playing an indispensable role in various fields. However, previous studies on flexible sensors have primarily focused on their elasticity and temperature‐sensing capability, with few reports on material identification. In this paper, a thermogalvanic dual‐network hydrogel is fabricated with [Fe(CN)6]3‐/4− as a redox couple and lithium magnesium silicate, Gdm+ and lithium bromide as key electrolytes to optimize the interconnected porous structure of the gel, which shows excellent mechanical and thermoelectric properties with a thermopower as high as 4.01 mV K−1. A self‐powered material identification ring is developed based on the temperature‐triggered thermoelectric response of the gel in conjunction with machine learning, which can actively infer materials without an external power connection by analyzing the voltage signals correlated with interfacial heat transfer produced upon contact with different materials. The proposed gel ring has important applications for future areas such as human–computer interaction and haptic‐associated artificial intelligence.