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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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PIIS2589004224007028.pdf | 5.1 MB | Adobe PDF | View/Open |
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