A Machine‐Learning‐Enhanced Simultaneous and Multimodal Sensor Based on Moist‐Electric Powered Graphene Oxide. Issue 41 (12th September 2022) - Yang, Ce; Wang, Haiyan; Yang, Jiawei; Yao, Houze; He, Tiancheng; Bai, Jiaxin; Guang, Tianlei; Cheng, Huhu; Yan, Jianfeng; Qu, Liangti
Abstract: 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. Abstract : Graphene oxide multimodal sensor (GO‐MS) capable of self‐powered sensing of various environmental stimuli based on a spontaneously generated moist‐electric potential is developed. Through the decoupling of characteristic response signals by machine‐learning models, simultaneous monitoring of multiple stimuli by a single GO‐MS is realized, providing a promising technical means for real‐time multimodal perception and complex information system identification.