An intelligent nanomesh-reinforced graphene pressure sensor with an ultra large linear range. Issue 9 (9th February 2022)

• Record Type: Journal Article
• Authors: Qiao, Yancong; Jian, Jinming; Tang, Hao; Ji, Shourui; Liu, Ying; Liu, Haidong; Li, Yuanfang; Li, Xiaoshi; Han, Fei; Liu, Zhiyuan; Cui, Tianrui; Gou, Guangyang; Jiang, Lelun; Yang, Yi; Zhou, Bingpu; Ren, Tian-Ling; Zhou, Jianhua
• Is Part Of: Journal of materials chemistry. Volume 10:Issue 9(2022)
• Page Start: 4858
• Page End: 4869
• Publication Date: 2022-02-09
• Subjects: Materials -- Research -- Periodicals; Chemistry, Analytic -- Periodicals; Environmental sciences -- Research -- Periodicals; 543.0284
• DOI: 10.1039/d1ta09813f
• Languages: English
• ISSNs: 2050-7488

Abstract

Abstract : Inspired by the reinforced concrete structure, the nanomesh-reinforced graphene pressure sensor (NRGPS) has been realized with ultra large linearity (1 MPa), high sensitivity (4.19 kPa −1 ), and excellent stability (more than 10 000 cycles). Abstract : A pressure sensor is an important device in daily life, especially for physiological signal monitoring. However, to realize an intelligent pressure sensor, more features should be achieved, such as high sensitivity, large linearity range, in situ signal processing, and automatic analysis. In this article, inspired by the reinforced concrete structure, a nanomesh-reinforced graphene pressure sensor (NRGPS) has been designed and realized with not only excellent mechanical performance but also water vapor permeability. Unlike the negative-resistance pressure sensor, the resistance of the NRGPS increases under a larger pressure, which greatly increases the measuring range. Thanks to the nanomesh skeleton, the NRGPS has ultra large linearity (1 MPa), high sensitivity (4.19 kPa −1 ), and excellent stability (more than 10 000 cycles). To explain the sensing mechanism of the NRGPS, a finite element model was proposed for the microstructure of nanomesh-reinforced graphene. With the aid of large linearity and high sensitivity, the NRGPS can simulate the mechanical Metal–Oxide–Semiconductor Field Effect Transistors (MOSFET) and realize the in situ pulse signal amplification. Finally, an intelligent tactile sensor was achieved by combining the NRGPS with a convolutional neural network. Convex Braille numbers can be distinguished by the intelligent tactile sensor with an accuracy of 88%. This work has potential in the intelligent diagnosis and tactile reconstruction field.