Substrate-Free Multilayer Graphene Electronic Skin for Intelligent Diagnosis

Yancong Qiao, Xiaoshi Li, Jinming Jian, Qi Wu, Yuhong Wei, Hua Shuai, Thomas Hirtz, Yao Zhi, Ge Deng, Yunfan Wang, Guangyang Gou, Jiandong Xu, Tianrui Cui, He Tian, Yi Yang, Tian Ling Ren

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)49945-49956
Number of pages12
JournalACS Applied Materials and Interfaces
Volume12
Issue number44
DOIs
StatePublished - 4 Nov 2020
Externally publishedYes

Keywords

  • flexible system
  • laser scribing graphene
  • neural network
  • physiological signal monitoring
  • substrate-free

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