Journal
ADVANCED MATERIALS
Volume 34, Issue 41, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202205249
Keywords
moist-electric; multimodal sensor; self-powered sensors; simultaneous monitoring
Categories
Funding
- National Science Foundation of China [22035005, 52022051, 22075165, 52073159, 52090032]
- State Key Laboratory of Tribology in Advanced Equipment [SKLT2021B03]
- Tsinghua-Foshan Innovation Special Fund [2020THFS0501]
- Scientific Research Project of Beijing Educational Committee [KZ202110017026, 2019GQG1025]
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Researchers have developed a graphene oxide single-component multimodal sensor that can simultaneously monitor multiple environmental stimuli through a moist-electric self-power supply. With the support of machine learning, this sensor can monitor and decouple changes in temperature, humidity, pressure, and light intensity simultaneously.
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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