journal article·2023-12-22

Perovskite-Structured BiFeO3 Nanoparticles with Abundant Oxygen Vacancies for Selective n-Butanol Gas Sensing

Zhan Cheng, Hongmin Zhu, Hanyang Ji, Zhenyu Yuan, Fanli Meng

ACS Applied Nano Materials· Volume 6· Issue 24
Publisher page

Abstract

N -butanol, a standard volatile organic compound (VOC) gas with toxicity and flammability, seriously endangers human life and health. It is imperative to achieve rapid and trace detection of n-butanol. Oxygen vacancies have been proven to be an essential factor for VOC gas adsorption at the surface of metal oxide semiconductor (MOS) gas sensors. In view of the above, BiFeO 3 (BFO) nanoparticles with abundant oxygen vacancies were prepared via simple sol–gel and annealing. Crystal structure, morphology, and compositions were systematically analyzed by a series of characterizations (XRD, SEM, HRTEM, EPR, and XPS). The response of the synthesized BFO-2 sensor reached 19.37 to 100 ppm n -butanol with a detection limit of 0.28 ppm at an optimal operating temperature of 280 °C. In addition, BFO-2 also has a fast response time, good stability and repeatability, and favorable selectivity to n -butanol. It is crucial to systematically explore the excellent selectivity of BFO sensors due to their abundant oxygen vacancies and large intrinsic crystal defects. Herein, adjusting the calcination temperature is one constructive strategy to tune the oxygen vacancy, which would prompt the gas sensing performance of BFO sensors to n -butanol.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Gas Sensing Nanomaterials and Sensors · Analytical Chemistry and Sensors · Advanced Chemical Sensor Technologies

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

Affiliations

College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

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

National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang 110819, China

Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang 110819, China

Funding

Natural Science Foundation of Hebei Province · Fundamental Research Funds for the Central Universities · National Natural Science Foundation of China · Higher Education Discipline Innovation Project

References (56)

Show source-provided references
1. 10.1021/acsami.1c1005410.1021/acsami.1c10054 ↗
2. 10.1021/acsami.2c1744410.1021/acsami.2c17444 ↗
3. 10.1016/j.snb.2020.12838410.1016/j.snb.2020.128384 ↗
4. 10.1109/JSEN.2022.318451110.1109/JSEN.2022.3184511 ↗
5. 10.1016/j.snb.2021.12958410.1016/j.snb.2021.129584 ↗
6. 10.1021/acssensors.9b0107310.1021/acssensors.9b01073 ↗
7. 10.1021/acsanm.3c0171310.1021/acsanm.3c01713 ↗
8. 10.1021/acsami.2c2116910.1021/acsami.2c21169 ↗
9. 10.1021/acsanm.0c0002510.1021/acsanm.0c00025 ↗
10. 10.1016/j.snb.2020.12784610.1016/j.snb.2020.127846 ↗
11. 10.1016/j.snb.2020.12879610.1016/j.snb.2020.128796 ↗
12. 10.1016/j.jhazmat.2019.02.09810.1016/j.jhazmat.2019.02.098 ↗
13. 10.1016/j.snb.2019.01.00710.1016/j.snb.2019.01.007 ↗
14. 10.1016/j.snb.2013.11.00510.1016/j.snb.2013.11.005 ↗
15. 10.1016/j.snb.2020.12874310.1016/j.snb.2020.128743 ↗
16. 10.1016/j.bios.2017.10.02710.1016/j.bios.2017.10.027 ↗
17. 10.1016/j.cej.2020.12410110.1016/j.cej.2020.124101 ↗
18. 10.1109/TIE.2022.316714910.1109/TIE.2022.3167149 ↗
19. 10.1002/adma.20100467610.1002/adma.201004676 ↗
20. 10.1002/adma.20050010910.1002/adma.200500109 ↗
21. 10.1007/s40820-020-00420-610.1007/s40820-020-00420-6 ↗
22. 10.1016/j.ijhydene.2019.05.07610.1016/j.ijhydene.2019.05.076 ↗
23. 10.1111/j.1551-2916.2009.03325.x10.1111/j.1551-2916.2009.03325.x ↗
24. 10.1021/acs.analchem.8b0176810.1021/acs.analchem.8b01768 ↗
25. 10.1021/acsanm.9b0207210.1021/acsanm.9b02072 ↗
26. 10.1021/acsami.1c2339010.1021/acsami.1c23390 ↗
27. 10.1016/j.snb.2019.12684510.1016/j.snb.2019.126845 ↗
28. 10.1016/j.snb.2022.13240010.1016/j.snb.2022.132400 ↗
29. 10.1016/j.jallcom.2016.07.07610.1016/j.jallcom.2016.07.076 ↗
30. 10.1016/j.jallcom.2021.16192510.1016/j.jallcom.2021.161925 ↗
31. 10.1016/j.ceramint.2016.01.12910.1016/j.ceramint.2016.01.129 ↗
32. 10.1109/TIM.2022.316957010.1109/TIM.2022.3169570 ↗
33. 10.1016/j.snb.2022.13147710.1016/j.snb.2022.131477 ↗
34. 10.1016/j.snb.2020.12768910.1016/j.snb.2020.127689 ↗
35. 10.1016/j.cej.2023.14470710.1016/j.cej.2023.144707 ↗
36. 10.1039/D2TA02764J10.1039/D2TA02764J ↗
37. 10.1016/j.cej.2021.13007610.1016/j.cej.2021.130076 ↗
38. 10.1021/acsanm.8b0197510.1021/acsanm.8b01975 ↗
39. 10.1109/JSEN.2021.306040110.1109/JSEN.2021.3060401 ↗
40. 10.1016/j.snb.2023.13412910.1016/j.snb.2023.134129 ↗
41. 10.1021/acsanm.2c0196310.1021/acsanm.2c01963 ↗
42. 10.1016/j.snb.2022.13235410.1016/j.snb.2022.132354 ↗
43. 10.1016/j.snb.2022.13242210.1016/j.snb.2022.132422 ↗
44. 10.1016/j.cej.2021.13428010.1016/j.cej.2021.134280 ↗
45. 10.1039/c3ra47467d10.1039/c3ra47467d ↗
46. 10.1016/j.trac.2023.11719910.1016/j.trac.2023.117199 ↗
47. 10.1016/j.mcat.2022.11222610.1016/j.mcat.2022.112226 ↗
48. 10.1039/C7TC03345A10.1039/C7TC03345A ↗
49. 10.1016/j.jcis.2023.06.15710.1016/j.jcis.2023.06.157 ↗
50. 10.1021/acsami.6b0449710.1021/acsami.6b04497 ↗
51. 10.1016/j.jallcom.2010.10.08410.1016/j.jallcom.2010.10.084 ↗
52. 10.1016/j.jallcom.2020.15820510.1016/j.jallcom.2020.158205 ↗
53. 10.1016/j.inoche.2023.11049110.1016/j.inoche.2023.110491 ↗
54. 10.1109/JSEN.2022.317972510.1109/JSEN.2022.3179725 ↗
55. 10.1016/j.jallcom.2019.02.15310.1016/j.jallcom.2019.02.153 ↗
56. 10.1016/j.apsusc.2022.15434010.1016/j.apsusc.2022.154340 ↗