材料科学
热电效应
可穿戴计算机
计算机科学
纳米技术
机械工程
嵌入式系统
工程类
热力学
物理
作者
Yunsheng Li,Wenxu Wang,Xiaojing Cui,Ning Li,Xueliang Ma,Zhaosu Wang,Y. Nie,Zhiquan Huang,Hulin Zhang
出处
期刊:Small
[Wiley]
日期:2024-10-31
标识
DOI:10.1002/smll.202405911
摘要
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.
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