journal article·2024-01-10

Graphene-based Two-Stage Enhancement Pressure Sensor for Subtle Mechanical Force Monitoring

He Tian, Xiaoshi Li, Guang-Yang Gou, Jin-Ming Jian, Boyi Zhu, Shourui Ji, Hengbin Ding, Zhanfeng Guo, Yi Yang, Tian-Ling Ren

Publisher page

Abstract

The development of pressure sensors with high sensitivity and a low detection limit for subtle mechanical force monitoring and the understanding of the sensing mechanism behind subtle mechanical force monitoring are of great significance for intelligent technology. Here, we proposed a graphene-based two-stage enhancement pressure sensor (GTEPS), and we analyzed the difference between subtle mechanical force monitoring and conventional mechanical force monitoring. The GTEPS exhibited a high sensitivity of 62.2 kPa –1 and a low detection limit of 0.1 Pa. Leveraging its excellent performance, the GTEPS was successfully applied in various subtle mechanical force monitoring applications, including acoustic wave detection, voice-print recognition, and pulse wave monitoring. In acoustic wave detection, the GTEPS achieved a 100% recognition accuracy for six words. In voiceprint recognition, the sensor exhibited accurate identification of distinct voiceprints among individuals. Furthermore, in pulse wave monitoring, GTEPS demonstrated effective detection of pulse waves. By combination of the pulse wave signals with electrocardiogram (ECG) signals, it enabled the assessment of blood pressure. These results demonstrate the excellent performance of GTEPS and highlight its great potential for subtle mechanical force monitoring and its various applications. The current results indicate that GTEPS shows great potential for applications in subtle mechanical force monitoring.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Non-Invasive Vital Sign Monitoring · Muscle activation and electromyography studies

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

Affiliations

School of Integrated Circuits & Beijing National Research on Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Funding

National Key Research and Development Program of China · Toyota Foundation · Fok Ying Tung Education Foundation · Beijing Innovation Center for Future Chip · Beijing Municipal Natural Science Foundation · Guoqiang Institute, Tsinghua University · Tsinghua University · National Natural Science Foundation of China · STI 2030?Major Projects · CIE-Tencent Robotics X Rhino-Bird Focused Research Program · School of Integrated Circuits, Tsinghua University · Tsinghua University-Zhuhai Huafa Industrial Share Company Joint Institute for Architecture Optoelectronic Technologies

References (57)

Show source-provided references
1. 10.1021/acsnano.9b0635410.1021/acsnano.9b06354 ↗
2. 10.1021/acsami.2c0657410.1021/acsami.2c06574 ↗
3. 10.1038/s41467-021-25637-w10.1038/s41467-021-25637-w ↗
4. 10.1021/acsami.8b2028410.1021/acsami.8b20284 ↗
5. 10.1016/j.mattod.2019.12.00410.1016/j.mattod.2019.12.004 ↗
6. 10.1039/c7nh00147a10.1039/c7nh00147a ↗
7. 10.1021/acsami.8b1831210.1021/acsami.8b18312 ↗
8. 10.1021/acsami.0c2278410.1021/acsami.0c22784 ↗
9. 10.1038/s41467-022-28760-410.1038/s41467-022-28760-4 ↗
10. 10.1002/adfm.20220236010.1002/adfm.202202360 ↗
11. 10.1126/sciadv.abn215610.1126/sciadv.abn2156 ↗
12. 10.1021/acsnano.7b0761310.1021/acsnano.7b07613 ↗
13. 10.1038/ncomms1457910.1038/ncomms14579 ↗
14. 10.1038/ncomms1110810.1038/ncomms11108 ↗
15. 10.1021/acsnano.0c0329410.1021/acsnano.0c03294 ↗
16. 10.1021/acsnano.2c0643210.1021/acsnano.2c06432 ↗
17. 10.1002/adfm.20140071210.1002/adfm.201400712 ↗
18. 10.1021/acsami.0c2140710.1021/acsami.0c21407 ↗
19. 10.1002/adfm.20200129610.1002/adfm.202001296 ↗
20. 10.3390/mi1307114210.3390/mi13071142 ↗
21. 10.1021/acsami.9b1334910.1021/acsami.9b13349 ↗
22. 10.1021/acsnano.0c0593210.1021/acsnano.0c05932 ↗
23. 10.1002/adfm.20191002010.1002/adfm.201910020 ↗
24. 10.3390/mi1206069510.3390/mi12060695 ↗
25. 10.1038/nature1400210.1038/nature14002 ↗
26. 10.1021/acsnano.8b0216210.1021/acsnano.8b02162 ↗
27. 10.1002/adma.20220082310.1002/adma.202200823 ↗
28. 10.1016/j.mee.2022.11177910.1016/j.mee.2022.111779 ↗
29. 10.1021/acsnano.2c0434810.1021/acsnano.2c04348 ↗
30. 10.1016/j.cej.2021.12994910.1016/j.cej.2021.129949 ↗
31. 10.1021/acsami.7b1525210.1021/acsami.7b15252 ↗
32. 10.1002/smtd.20200104110.1002/smtd.202001041 ↗
33. 10.1021/acsami.7b0039810.1021/acsami.7b00398 ↗
34. 10.1002/smll.20180081910.1002/smll.201800819 ↗
35. 10.1039/C9NR05532K10.1039/C9NR05532K ↗
36. 10.3389/fchem.2019.0039910.3389/fchem.2019.00399 ↗
37. 10.1002/admt.20210102110.1002/admt.202101021 ↗
38. 10.1039/C8NR08503J10.1039/C8NR08503J ↗
39. 10.1039/D0TA04915H10.1039/D0TA04915H ↗
40. 10.1088/1674-1056/22/5/05770110.1088/1674-1056/22/5/057701 ↗
41. 10.1016/j.matt.2019.10.00110.1016/j.matt.2019.10.001 ↗
42. 10.1002/admt.20210042110.1002/admt.202100421 ↗
43. 10.1021/nl104227t10.1021/nl104227t ↗
44. 10.1039/C7TA11348J10.1039/C7TA11348J ↗
45. 10.1016/j.nanoen.2023.10821510.1016/j.nanoen.2023.108215 ↗
46. 10.1002/adfm.20200361810.1002/adfm.202003618 ↗
47. 10.1021/acsami.0c1994910.1021/acsami.0c19949 ↗
48. 10.1021/acsami.2c0253810.1021/acsami.2c02538 ↗
49. 10.1016/j.jcis.2022.07.18810.1016/j.jcis.2022.07.188 ↗
50. 10.1021/acsami.9b1651110.1021/acsami.9b16511 ↗
51. 10.1109/LED.2018.283546710.1109/LED.2018.2835467 ↗
52. Van der Maaten L. · J. Mach. Learn. Res. · 2008 · 9 · 2579
53. 10.1016/j.xcrp.2022.10107510.1016/j.xcrp.2022.101075 ↗
54. 10.1038/s41551-018-0287-x10.1038/s41551-018-0287-x ↗
55. 10.1088/1674-4926/43/8/08260110.1088/1674-4926/43/8/082601 ↗
56. 10.1109/TIM.2017.274508110.1109/TIM.2017.2745081 ↗
57. 10.1038/s41598-021-01358-410.1038/s41598-021-01358-4 ↗