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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202205249
|2 doi
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|a pubmed24n1150.xml
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|a (DE-627)NLM345255496
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|a (NLM)36007144
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
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|a Yang, Ce
|e verfasserin
|4 aut
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| 245 |
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|a A Machine-Learning-Enhanced Simultaneous and Multimodal Sensor Based on Moist-Electric Powered Graphene Oxide
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 17.10.2022
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|a Date Revised 17.10.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2022 Wiley-VCH GmbH.
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|a Simultaneous multimodal monitoring can greatly perceive intricately multiple stimuli, which is important for the understanding and development of a future human-machine fusion world. However, the integrated multisensor networks with cumbersome structure, huge power consumption, and complex preparation process have heavily restricted practical applications. Herein, a graphene oxide single-component multimodal sensor (GO-MS) is developed, which enables simultaneous monitoring of multiple environmental stimuli by a single unit with unique moist-electric self-power supply. This GO-MS can generate a sustainable moist-electric potential by spontaneously adsorbing water molecules in air, which has a characteristic response behavior when exposed to different stimuli. As a result, the simultaneous monitoring and decoupling of the changes of temperature, humidity, pressure, and light intensity are achieved by this single GO-MS with machine-learning (ML) assistance. Of practical importance, a moist-electric-powered human-machine interaction wristband based on GO-MS is constructed to monitor pulse signals, body temperature, and sweating in a multidimensional manner, as well as gestures and sign language commanding communication. This ML-empowered moist-electric GO-MS provides a new platform for the development of self-powered single-component multimodal sensors, showing great potential for applications in the fields of health detection, artificial electronic skin, and the Internet-of-Things
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|a Journal Article
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|a moist-electric
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|a multimodal sensor
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|a self-powered sensors
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|a simultaneous monitoring
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|a graphene oxide
|2 NLM
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|a Water
|2 NLM
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|a 059QF0KO0R
|2 NLM
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|a 7782-42-5
|2 NLM
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| 700 |
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|a Wang, Haiyan
|e verfasserin
|4 aut
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|a Yang, Jiawei
|e verfasserin
|4 aut
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| 700 |
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|a Yao, Houze
|e verfasserin
|4 aut
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|a He, Tiancheng
|e verfasserin
|4 aut
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|a Bai, Jiaxin
|e verfasserin
|4 aut
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|a Guang, Tianlei
|e verfasserin
|4 aut
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|a Cheng, Huhu
|e verfasserin
|4 aut
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|a Yan, Jianfeng
|e verfasserin
|4 aut
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|a Qu, Liangti
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 34(2022), 41 vom: 25. Okt., Seite e2205249
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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|g volume:34
|g year:2022
|g number:41
|g day:25
|g month:10
|g pages:e2205249
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|u http://dx.doi.org/10.1002/adma.202205249
|3 Volltext
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