journal article·2024-07-26

High-Performance Wearable Piezoresistive Sensor with a Wide Temperature Range via a Ti3C2Tx MXene/Au Dual-Layer Conductive Network and Microspike Structure

Ning Yang, Xiangyu Yin, Hailian Liu, Xue Zhou, Xin Yan, Yong Zhao, Tonglei Cheng

ACS Applied Nano Materials· Volume 7· Issue 14
Publisher page

Abstract

Currently, with the increasing popularity of wearable electronic devices, there is growing demand for the performance and application scenarios of flexible pressure sensors. However, effectively balancing a sensor’s sensitivity and sensing range over a wide temperature range remains an urgent challenge. In this paper, a simple and economical template transfer method was used to prepare microspike structures with random distributions, while a dual-layer conductive network was constructed using Ti 3 C 2 T x MXene (two-dimensional transition metal carbides and nitrides) and gold nanoparticles (Au). The working mechanism of the piezoresistive sensor was systematically analyzed. The special surface microstructure and the synergistic effect between the double-layer conductive network enabled the sensor to acquire high sensitivity (497.08 kPa –1 ) over a wide sensing range (>1000 kPa) and maintain stability for working even at high temperatures (>400 °C). In addition, the obtained piezoresistive sensor had a short response time and relaxation time, good linearity, a low detection limit, and good fatigue resistance. Furthermore, we demonstrated the application of this sensor in table tennis and electronic sports (eSports) scenarios, showing its bright prospect in human motion pattern recognition. Its insensitivity to temperature also showed great potential for the development of wearable electronic products that can work stably in harsh environments.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: MXene and MAX Phase Materials · Advanced Sensor and Energy Harvesting Materials · Advanced Memory and Neural Computing

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

State Key Laboratory of Synthetical Automation for Process Industries, the College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Foshan Graduate School of Innovation, Northeastern University, Foshan, Guangdong 528311, China

Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China

Funding

Natural Science Foundation of Hebei Province · China Association for Science and Technology · Basic and Applied Basic Research Foundation of Guangdong Province · Natural Science Foundation of Liaoning Province · Fundamental Research Funds for the Central Universities · National Natural Science Foundation of China · Higher Education Discipline Innovation Project

References (55)

Show source-provided references
1. 10.1016/j.cej.2020.12602910.1016/j.cej.2020.126029 ↗
2. 10.1021/acsnano.9b0895210.1021/acsnano.9b08952 ↗
3. 10.1016/j.scib.2022.07.02010.1016/j.scib.2022.07.020 ↗
4. 10.1002/adfm.20190960310.1002/adfm.201909603 ↗
5. 10.1002/advs.20220530310.1002/advs.202205303 ↗
6. 10.1038/s41586-021-03295-810.1038/s41586-021-03295-8 ↗
7. 10.1021/acsnano.1c0074910.1021/acsnano.1c00749 ↗
8. 10.1002/adsu.20220044110.1002/adsu.202200441 ↗
9. 10.1002/adma.20210085910.1002/adma.202100859 ↗
10. 10.1021/acsami.2c1224010.1021/acsami.2c12240 ↗
11. 10.1002/adfm.20190248410.1002/adfm.201902484 ↗
12. 10.1002/adfm.20180882910.1002/adfm.201808829 ↗
13. 10.1016/j.eml.2020.10071410.1016/j.eml.2020.100714 ↗
14. 10.1016/j.cej.2019.12399810.1016/j.cej.2019.123998 ↗
15. 10.1002/smll.20210331210.1002/smll.202103312 ↗
16. 10.1016/j.sse.2018.04.00310.1016/j.sse.2018.04.003 ↗
17. 10.1002/adma.20180208410.1002/adma.201802084 ↗
18. 10.1016/j.ceramint.2023.02.21110.1016/j.ceramint.2023.02.211 ↗
19. 10.1002/adfm.20230372310.1002/adfm.202303723 ↗
20. 10.1021/acsami.0c2267010.1021/acsami.0c22670 ↗
21. 10.1038/s41467-021-21729-910.1038/s41467-021-21729-9 ↗
22. 10.1039/C8NR01495G10.1039/C8NR01495G ↗
23. 10.1016/j.cej.2022.13604610.1016/j.cej.2022.136046 ↗
24. 10.1002/aisy.20210007010.1002/aisy.202100070 ↗
25. 10.1016/j.cej.2021.13310310.1016/j.cej.2021.133103 ↗
26. 10.1021/acsami.9b0926510.1021/acsami.9b09265 ↗
27. 10.1021/acsami.0c0891010.1021/acsami.0c08910 ↗
28. 10.1016/j.nanoen.2019.10443610.1016/j.nanoen.2019.104436 ↗
29. 10.1016/j.cej.2022.13971610.1016/j.cej.2022.139716 ↗
30. 10.1016/j.snb.2021.13094410.1016/j.snb.2021.130944 ↗
31. 10.1039/D0TC00372G10.1039/D0TC00372G ↗
32. 10.1039/C9TA04352G10.1039/C9TA04352G ↗
33. 10.1002/smll.20180165710.1002/smll.201801657 ↗
34. 10.1557/jmr.2015.16010.1557/jmr.2015.160 ↗
35. 10.1021/acsnano.9b0953310.1021/acsnano.9b09533 ↗
36. 10.1002/adfm.20231631410.1002/adfm.202316314 ↗
37. 10.1002/admt.20190067910.1002/admt.201900679 ↗
38. 10.1016/j.nanoen.2019.02.03610.1016/j.nanoen.2019.02.036 ↗
39. 10.1002/smll.20180455910.1002/smll.201804559 ↗
40. 10.1021/acsami.1c1739110.1021/acsami.1c17391 ↗
41. 10.1038/nmat76810.1038/nmat768 ↗
42. 10.1021/acsnano.1c0761410.1021/acsnano.1c07614 ↗
43. 10.1039/C8NR09506J10.1039/C8NR09506J ↗
44. 10.1039/D1TC02251B10.1039/D1TC02251B ↗
45. 10.1021/acsami.1c0997510.1021/acsami.1c09975 ↗
46. 10.1021/acsami.8b0363910.1021/acsami.8b03639 ↗
47. 10.1002/adfm.20200360110.1002/adfm.202003601 ↗
48. 10.1038/s41427-018-0031-810.1038/s41427-018-0031-8 ↗
49. 10.1039/C8NR08503J10.1039/C8NR08503J ↗
50. 10.1002/adfm.20180234310.1002/adfm.201802343 ↗
51. 10.1002/admt.20170015810.1002/admt.201700158 ↗
52. 10.1002/adma.20110230610.1002/adma.201102306 ↗
53. 10.1038/s41467-017-01136-910.1038/s41467-017-01136-9 ↗
54. 10.1016/j.colsurfa.2022.12867610.1016/j.colsurfa.2022.128676 ↗
55. 10.1038/s41467-023-42361-910.1038/s41467-023-42361-9 ↗