期刊
ACS APPLIED MATERIALS & INTERFACES
卷 12, 期 44, 页码 49945-49956出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsami.0c12440
关键词
laser scribing graphene; substrate-free; physiological signal monitoring; flexible system; neural network
资金
- National Key R&D Program of China [2016YFA0200400, 2018YFC2001202]
- National Natural Science Foundation of China [61434001, 61574083, 61874065, 51861145202]
- National Basic Research Program of China [2015CB352101]
- Tsinghua University Tutor Research Fund
- Tsinghua University Initiative Scientific Research Program
- Beijing Innovation Center for Future Chip
- Beijing Natural Science Foundation [4184091]
- Tsinghua Fuzhou Institute for Date Technology [TFIDT2018008]
- Shenzhen Science and Technology Program [JCYJ20150831192224146]
Current wearable sensors are fabricated with substrates, which limits the comfort, flexibility, stretchability, and induces interface mismatch. In addition, the substrate prevents the evaporation of sweat and is harmful to skin health. In this work, we have enabled the substrate-free laser scribed graphene (SFG) electronic skin (e-skin) with multifunctions. Compared with the e-skin with the substrate, the SFG has good gas permeability, low impedance, and flexibility. Only assisted using water, the SFG can be transferred to almost any objects including silicon and human skin and it can even be suspended. Many through-holes like stomas in leaf can be formed in the SFG, which make it breathable. After designing the pattern, the gauge factor (GF) of graphene electronic skin (GES) can be designed as the strain sensor. Physiological signals such as respiration, human motion, and electrocardiogram (ECG) can be detected. Moreover, the suspended SFG detect vibrations with high sensitivity. Due to the substrate-free structure, the impedance between SFG e-skin and the human body decreases greatly. Finally, an ECG detecting system has been designed based on the GES, which can monitor the body condition in real time. To analyze the ECG signals automatically, a convolutional neural network (CNN) was built and trained successfully. This work has high potential in the field of health telemonitoring.
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