Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/174745
Title: High-sensitivity, ultrawide linear range, antibacterial textile pressure sensor based on chitosan/MXene hierarchical architecture
Authors: Gu, Mengxi
Zhou, Xuan
Shen, Jienan
Xie, Ruibin
Su, Yuhan
Gao, Junxue
Zhao, Binzhe
Li, Jie
Duan, Yingjie
Wang, Zhixun
Hu, Yougen
Gu, Guoqiang
Wang, Lei
Wei, Lei
Yang, Chunlei
Chen, Ming
Keywords: Engineering
Issue Date: 2024
Source: Gu, M., Zhou, X., Shen, J., Xie, R., Su, Y., Gao, J., Zhao, B., Li, J., Duan, Y., Wang, Z., Hu, Y., Gu, G., Wang, L., Wei, L., Yang, C. & Chen, M. (2024). High-sensitivity, ultrawide linear range, antibacterial textile pressure sensor based on chitosan/MXene hierarchical architecture. IScience, 27(4), 109481-. https://dx.doi.org/10.1016/j.isci.2024.109481
Project: MOE2019-T2-2-127 
MOE2019-T1-001-103 
MOE2019-T1-001-111 
NRF-CRP18-2017-02 
Journal: iScience 
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.
URI: https://hdl.handle.net/10356/174745
ISSN: 2589-0042
DOI: 10.1016/j.isci.2024.109481
Schools: School of Electrical and Electronic Engineering 
Rights: © 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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