journal article·2023-05Open access

Optical Emission from Triboelectric Gas Discharge toward Self‐Powered Gas Sensing

Jingjing Fu, Ziwu Song, Guoqiang Xu, Haoyu Wang, Xinyuan Li, Jiaqi Wang, Wenbo Ding, Wei Ren, Iek Man Lei, Yunlong Zi

Advanced Optical Materials· Volume 11· Issue 9

Abstract

Abstract Optical emission from gas molecules is considered an excellent solution to gas detection because of its fairly high sensitivity and selectivity. However, its further development is constrained by its large size, high energy consumption, and security risks. Herein, a self‐powered gas sensing solution is proposed by taking advantage of the optical emission of triboelectric discharge (TD). Based on the triboelectric effect, the high output voltage of ≈kV can lead to producing optical emissions through gas discharges, including emission spectra and discharge images, which are affected by gas species and pressure. By means of machine learning, the optical emission signals process the ability to recognize 60 gas atmospheres simultaneously, with an accuracy of 97.67%. With the assistance of the mobile phone or closed‐circuit television, a self‐powered gas sensing system based on TD may serve as a portable detecting device and a vital component of the Internet of Things system.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Gas Sensing Nanomaterials and Sensors · Analytical Chemistry and Sensors

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

Affiliations

Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin, N.T. Hong Kong SAR 999077 China

Tsinghua Shenzhen International Graduate School and Tsinghua‐Berkeley Shenzhen Institute Tsinghua University Shenzhen Guangdong 518055 China

Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing 101400 China

Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai Guangdong 519082 China

School of Marine Sciences Sun Yat‐Sen University Zhuhai Guangdong 519082 China

Faculty of Science and Technology University of Macau Taipa Macau 999078 China

Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology (Guangzhou) Nansha Guangzhou Guangdong 511400 China

HKUST Shenzhen‐Hong Kong Collaborative Innovation Research Institute Futian Shenzhen Guangdong 518048 China

References (44)

Show source-provided references
1. 10.1016/j.snb.2005.02.00810.1016/j.snb.2005.02.008 ↗
2. 10.1016/0925-4005(94)01596-A10.1016/0925-4005(94)01596-A ↗
3. 10.1007/s00340-005-1764-y10.1007/s00340-005-1764-y ↗
4. 10.1088/0004-637X/728/1/5410.1088/0004-637X/728/1/54 ↗
5. 10.1088/0957-0233/24/1/01200410.1088/0957-0233/24/1/012004 ↗
6. 10.3390/chemosensors301000110.3390/chemosensors3010001 ↗
7. 10.1002/anie.20060259710.1002/anie.200602597 ↗
8. 10.1029/97JB0278410.1029/97JB02784 ↗
9. 10.1016/j.jqsrt.2019.02.01510.1016/j.jqsrt.2019.02.015 ↗
10. 10.1366/000370279492589510.1366/0003702794925895 ↗
11. 10.3390/s1917376010.3390/s19173760 ↗
12. 10.1007/s10796-014-9492-710.1007/s10796-014-9492-7 ↗
13. 10.1109/ACCESS.2020.297011810.1109/ACCESS.2020.2970118 ↗
14. 10.1016/j.jhazmat.2022.12883610.1016/j.jhazmat.2022.128836 ↗
15. 10.1016/j.snb.2022.13179010.1016/j.snb.2022.131790 ↗
16. 10.1002/adfm.20220352810.1002/adfm.202203528 ↗
17. 10.1016/j.surfcoat.2005.02.21710.1016/j.surfcoat.2005.02.217 ↗
18. 10.1002/aenm.20180290610.1002/aenm.201802906 ↗
19. 10.1088/1674-4926/42/10/10160110.1088/1674-4926/42/10/101601 ↗
20. 10.1016/j.nanoen.2018.09.05710.1016/j.nanoen.2018.09.057 ↗
21. 10.1038/nnano.2017.1710.1038/nnano.2017.17 ↗
22. 10.1016/j.nanoen.2014.11.03410.1016/j.nanoen.2014.11.034 ↗
23. 10.1016/j.nanoen.2022.10718310.1016/j.nanoen.2022.107183 ↗
24. 10.1021/acsami.0c2124610.1021/acsami.0c21246 ↗
25. Paschen F. · 1889
26. 10.1007/978-3-642-61247-310.1007/978-3-642-61247-3 ↗
27. 10.1557/mre.2020.210.1557/mre.2020.2 ↗
28. 10.1002/adfm.20190110210.1002/adfm.201901102 ↗
29. 10.1038/ncomms937610.1038/ncomms9376 ↗
30. 10.1038/s41467-019-12465-210.1038/s41467-019-12465-2 ↗
31. 10.1002/advs.20200175710.1002/advs.202001757 ↗
32. 10.1021/acsnano.9b0627210.1021/acsnano.9b06272 ↗
33. Berger L. · Carbon · 2006 · 1 · 2
34. 10.1080/0026897730010226110.1080/00268977300102261 ↗
35. Firsov O. · Zhur. Eksptl'. Teoret. Fiz. · 1957 · 36 · 1517
36. 10.1002/j.1520-6378.1977.tb00104.x10.1002/j.1520-6378.1977.tb00104.x ↗
37. 10.1002/978047017563710.1002/9780470175637 ↗
38. 10.1007/978-3-662-48041-010.1007/978-3-662-48041-0 ↗
39. 10.1016/S0584-8547(01)00406-210.1016/S0584-8547(01)00406-2 ↗
40. 10.1063/1.271757610.1063/1.2717576 ↗
41. 10.1063/1.165871510.1063/1.1658715 ↗
42. 10.1002/cem.297710.1002/cem.2977 ↗
43. a)K.He X.Zhang S.Ren J.Sun inEuropean Conference on Computer Vision (ECCV) Springer Cham2016 pp.630–645;10.1007/978-3-319-46493-0_38 ↗
44. b)K.He X.Zhang S.Ren J.Sun in2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Piscataway NJ2016 pp.770–778.10.1109/CVPR.2016.90 ↗