High-sensitivity, ultrawide linear range, antibacterial textile pressure sensor based on chitosan/MXene hierarchical architecture

Mengxi Gu; Xuan Zhou; Jienan Shen; Ruibin Xie; Yuhan Su; Junxue Gao; Binzhe Zhao; Jie Li; Yingjie Duan; Zhixun Wang; Yougen Hu; Guoqiang Gu; Lei Wang; Lei Wei; Chunlei Yang; Ming Chen

doi:10.1016/j.isci.2024.109481

High-sensitivity, ultrawide linear range, antibacterial textile pressure sensor based on chitosan/MXene hierarchical architecture

iScience. 2024 Mar 11;27(4):109481. doi: 10.1016/j.isci.2024.109481. eCollection 2024 Apr 19.

Authors

Mengxi Gu^{1

2}, Xuan Zhou^{1

3}, Jienan Shen¹, Ruibin Xie¹, Yuhan Su^{1

2}, Junxue Gao^{1

3}, Binzhe Zhao^{1

2}, Jie Li^{1

2}, Yingjie Duan^{1

3}, Zhixun Wang⁴, Yougen Hu¹, Guoqiang Gu¹, Lei Wang¹, Lei Wei⁴, Chunlei Yang^{1

2}, Ming Chen^{1

2}

Affiliations

¹ Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.
² University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China.
³ Department of Nano Science and Technology Institute, University of Science and Technology of China, Suzhou 215123, People's Republic of China.
⁴ School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.

Abstract

It is still a great challenge for the flexible piezoresistive pressure sensors to simultaneously achieve wide linearity and high sensitivity. Herein, we propose a high-performance textile pressure sensor based on chitosan (CTS)/MXene fiber. The hierarchical "point to line" architecture enables the pressure sensor with high sensitivity of 1.16 kPa^-1 over an ultrawide linear range of 1.5 MPa. Furthermore, the CTS/MXene pressure sensor possesses a low fatigue over 1000 loading/unloading cycles under 1.5 MPa pressure load, attributed to the strong chemical bonding between CTS fiber and MXene and excellent mechanical stability. Besides, the proposed sensor shows good antibacterial effect benefiting from the strong interaction between polycationic structure of CTS/MXene and the predominantly anionic components of bacteria surface. The sensor is also applied to detect real-time human action, an overall classification accuracy of 98.61% based on deep neural network-convolutional neural network (CNN) for six human actions is realized.

Keywords: Artificial intelligence; Health technology; Materials science.