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author:

Zhong, Hongchuan (Zhong, Hongchuan.) [1] | Fu, Rongda (Fu, Rongda.) [2] | Chen, Shiqi (Chen, Shiqi.) [3] | Zhou, Zaiwei (Zhou, Zaiwei.) [4] | Zhang, Yue (Zhang, Yue.) [5] | Yin, Xiangyu (Yin, Xiangyu.) [6] | He, Bingwei (He, Bingwei.) [7]

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Abstract:

The achievement of well-performing pressure sensors with low pressure detection, high sensitivity, large-scale integration, and effective analysis of the subsequent data remains a major challenge in the development of flexible piezoresistive sensors. In this study, a simple and extendable sensor preparation strategy was proposed to fabricate flexible sensors on the basis of multiwalled carbon nanotube/polydimethylsiloxane (MWCNT/PDMS) composites. A dispersant of tetrahydrofuran (THF) was added to solve the agglomeration of MWCNTs in PDMS, and the resistance of the obtained MWCNT/PDMS conductive unit with 7.5 wt.% MWCNTs were as low as 180 ω/hemisphere. Sensitivity (0.004 kPa-1), excellent response stability, fast response time (36 ms), and excellent electromechanical properties were demonstrated within the pressure range from 0 to 100 kPa. A large-area flexible sensor with 8 × 10 pixels was successfully adopted to detect the pressure distribution on the human back and to verify its applicability. Combining the sensor array with deep learning, inclination of human sitting was easily recognized with high accuracy, indicating that the combined technology can be used to guide ergonomic design. © 2022 IOP Publishing Ltd.

Keyword:

Deep learning Multiwalled carbon nanotubes (MWCN) Organic solvents Polymer matrix composites Pressure sensors Signal processing

Community:

  • [ 1 ] [Zhong, Hongchuan]College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Fu, Rongda]College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 3 ] [Chen, Shiqi]College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 4 ] [Zhou, Zaiwei]College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 5 ] [Zhang, Yue]College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 6 ] [Zhang, Yue]Fujian Engineering Research Center of Joint Intelligent Medical Engineering, Fuzhou; 350108, China
  • [ 7 ] [Yin, Xiangyu]College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 8 ] [Yin, Xiangyu]Fujian Engineering Research Center of Joint Intelligent Medical Engineering, Fuzhou; 350108, China
  • [ 9 ] [He, Bingwei]College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 10 ] [He, Bingwei]Fujian Engineering Research Center of Joint Intelligent Medical Engineering, Fuzhou; 350108, China

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Source :

Nanotechnology

ISSN: 0957-4484

Year: 2022

Issue: 34

Volume: 33

3 . 5

JCR@2022

2 . 9 0 0

JCR@2023

ESI HC Threshold:91

JCR Journal Grade:2

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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