Thermogalvanic organohydrogel-based non-contact self-powered electronics for advancing smart agriculture

Yang, Xinru; Zhang, Zhiyi; Khan, Saeed Ahmed; Sun, Lei; Wang, Zhaosu; Cui, Xiaojing; Huang, Zhiquan; Zhang, Hulin

doi:10.1039/d3tc04133f

J. Mater. Chem. C

2024

DOI: 10.1039/d3tc04133f

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Thermogalvanic organohydrogel-based non-contact self-powered electronics for advancing smart agriculture

Xinru Yang,

Zhiyi Zhang,

Saeed Ahmed Khan

et al.

Abstract: Non-contact sensors avoid physical contact with the external environment, ensuring long-term reliability and offering a broad spectrum of sensing applications. However, the requirement for an external power supply, poor mechanical...

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Cited by 6 publications

References 38 publications

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Machine Learning Assisted Self‐Powered Identity Recognition Based on Thermogalvanic Hydrogel for Intelligent Security

Ma,

Wang,

Cui

et al. 2024

Small

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Identity recognition as the first barrier of intelligent security plays a vital role, which is facing new challenges that are unable to meet the need of intelligent era due to low accuracy, complex configuration and dependence on power supply. Here, a finger temperature‐driven intelligent identity recognition strategy is presented based on a thermogalvanic hydrogel (TGH) by actively discerning biometric characteristics of fingers. The TGH is a dual network PVA/Agar hydrogel in an H2O/glycerol binary solvent with [Fe(CN)6]3−/4− as a redox couple. Using a concave‐arranged TGH array, the characteristics of users can be distinguished adequately even under an open environment by extracting self‐existent intrinsic temperature features from five typical sites of fingers. Combined with machine learning, the TGH array can recognize different users with a high average accuracy of 97.6%. This self‐powered identity recognition strategy is further applied to a smart lock, attaining a more reliable security protection from biometric characteristics than bare passwords. This work provides a promising solution for achieving better identity recognition, which has great advantages in intelligent security and human‐machine interaction toward future Internet of everything.

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Machine Learning Assisted Self‐Powered Identity Recognition Based on Thermogalvanic Hydrogel for Intelligent Security

Ma,

Wang,

Cui

et al. 2024

Small

View full text Add to dashboard Cite

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

Li,

Wang,

Cui

et al. 2024

Small

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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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Conductive ionic thermoelectric hydrogel with negative Seebeck coefficient, self-healing and highly sensitive to temperature for photothermoelectric conversion and non-contact sensing device

Sha,

Wang,

Xiao

et al. 2024

Chemical Engineering Journal

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Thermogalvanic organohydrogel-based non-contact self-powered electronics for advancing smart agriculture

Abstract: Non-contact sensors avoid physical contact with the external environment, ensuring long-term reliability and offering a broad spectrum of sensing applications. However, the requirement for an external power supply, poor mechanical...

Cited by 6 publications

References 38 publications

Machine Learning Assisted Self‐Powered Identity Recognition Based on Thermogalvanic Hydrogel for Intelligent Security

Machine Learning Assisted Self‐Powered Identity Recognition Based on Thermogalvanic Hydrogel for Intelligent Security

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

Conductive ionic thermoelectric hydrogel with negative Seebeck coefficient, self-healing and highly sensitive to temperature for photothermoelectric conversion and non-contact sensing device

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