Cs2AgBiBr6 Quantum Dots Supported on Co3O4 Nanocages for Acetone Detection at Room Temperature
Source abstract
Abstract
High operating temperatures have been one of the most serious problems restricting the development of metal oxide gas sensors. The development of room temperature (RT) sensing is significant in practical applications. Herein, for the first time, we combined environmentally friendly lead-free double perovskite photovoltaic material Cs 2 AgBiBr 6 quantum dots (CABB QDs) (9–13 nm) with MOF-derived Co 3 O 4, and mesoporous heterogeneous composite materials (average pore diameter about 17.58 nm) with a series of weight percentage (wt %) CABB QDs supported on Co 3 O 4 nanocages were prepared by the template and heat treatment method. The gas sensing test results indicate that adding photovoltaic material CABB QDs can achieve high-performance acetone sensing at RT (25 °C). Especially, the sensor based on 2.0 wt % QDs-Co 3 O 4 exhibited the highest gas-sensitive response to acetone, with a response value of 13 at 60 ppm and a fast response and recovery time of 7 and 27 s, respectively. Meanwhile, the 2.0 wt % QDs-Co 3 O 4 sensor showed good selectivity, repeatability, and long-term stability. In addition, we delved into the sensing mechanism of acetone’s high response and selectivity at RT. This work provides a constructive idea for the detection of acetone at RT and has significant implications for accelerating the practical application of MOS-based gas sensors.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Gas Sensing Nanomaterials and Sensors · Advanced Chemical Sensor Technologies · Analytical Chemistry and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Physical Science and Technology, Tiangong University, Tianjin 300387, People’s Republic of China
Funding
Natural Science Foundation of Tianjin Municipality
References (66)
Show source-provided references
1. 10.1016/j.snb.2022.13218910.1016/j.snb.2022.132189 ↗
2. 10.1007/s10853-022-08072-010.1007/s10853-022-08072-0 ↗
3. 10.1021/acsomega.9b0077310.1021/acsomega.9b00773 ↗
4. 10.1016/j.matlet.2015.03.11210.1016/j.matlet.2015.03.112 ↗
5. 10.1016/j.snb.2023.13379010.1016/j.snb.2023.133790 ↗
6. 10.1088/1361-6528/acbbd210.1088/1361-6528/acbbd2 ↗
7. 10.1016/j.snb.2017.01.17910.1016/j.snb.2017.01.179 ↗
8. 10.1007/s12598-020-01647-310.1007/s12598-020-01647-3 ↗
9. 10.1016/j.jallcom.2019.15273010.1016/j.jallcom.2019.152730 ↗
10. 10.1016/j.jallcom.2014.12.16310.1016/j.jallcom.2014.12.163 ↗
11. 10.1016/j.snb.2013.06.07010.1016/j.snb.2013.06.070 ↗
12. 10.1016/j.snb.2020.12872910.1016/j.snb.2020.128729 ↗
13. 10.1002/adfm.20180257510.1002/adfm.201802575 ↗
14. 10.3390/mi1404072510.3390/mi14040725 ↗
15. 10.1016/j.ceramint.2022.10.35310.1016/j.ceramint.2022.10.353 ↗
16. 10.3390/nano1206098210.3390/nano12060982 ↗
17. 10.1002/adfm.20160010910.1002/adfm.201600109 ↗
18. 10.1002/pssr.20220001710.1002/pssr.202200017 ↗
19. 10.1021/acsnano.0c0890310.1021/acsnano.0c08903 ↗
20. 10.1016/j.mtchem.2022.10115510.1016/j.mtchem.2022.101155 ↗
21. 10.1016/j.snb.2022.13136610.1016/j.snb.2022.131366 ↗
22. 10.1021/acsanm.2c0322710.1021/acsanm.2c03227 ↗
23. 10.1021/acsami.0c2056610.1021/acsami.0c20566 ↗
24. 10.1021/acssensors.2c0158110.1021/acssensors.2c01581 ↗
25. 10.1016/j.cej.2022.13710210.1016/j.cej.2022.137102 ↗
26. 10.1039/C7TA06816F10.1039/C7TA06816F ↗
27. 10.3390/diagnostics801001210.3390/diagnostics8010012 ↗
28. 10.1016/j.nanoen.2019.10442010.1016/j.nanoen.2019.104420 ↗
29. 10.1016/j.snb.2021.12982310.1016/j.snb.2021.129823 ↗
30. 10.1002/smll.20160286810.1002/smll.201602868 ↗
31. 10.1016/j.snb.2022.13208710.1016/j.snb.2022.132087 ↗
32. 10.1016/j.cej.2018.01.07210.1016/j.cej.2018.01.072 ↗
33. 10.1021/am507131710.1021/am5071317 ↗
34. 10.1021/acsami.5b1206210.1021/acsami.5b12062 ↗
35. 10.1149/2.0291910jes10.1149/2.0291910jes ↗
36. 10.1016/j.snb.2019.04.05810.1016/j.snb.2019.04.058 ↗
37. 10.1016/j.ccr.2020.21327210.1016/j.ccr.2020.213272 ↗
38. 10.1016/j.poly.2018.06.03710.1016/j.poly.2018.06.037 ↗
39. 10.1016/j.apsusc.2017.10.08010.1016/j.apsusc.2017.10.080 ↗
40. 10.1016/j.snb.2018.07.13710.1016/j.snb.2018.07.137 ↗
41. 10.1016/j.matlet.2018.03.17410.1016/j.matlet.2018.03.174 ↗
42. 10.1016/j.jhazmat.2021.12590610.1016/j.jhazmat.2021.125906 ↗
43. 10.1016/j.mssp.2019.05.01410.1016/j.mssp.2019.05.014 ↗
44. 10.1016/j.snb.2021.13018210.1016/j.snb.2021.130182 ↗
45. 10.1016/j.snb.2019.03.04310.1016/j.snb.2019.03.043 ↗
46. Yang, J.; Jiang, B.; Wang, X.; Wang, C.; Sun, Y.; Zhang, H.; Shimanoe, K.; Lu, G., MOF-derived porous NiO/NiFe2O4 nanocubes for improving the acetone detection. Sens. Actuator B Chem. 2022, 366.10.1016/j.snb.2022.131985 ↗
47. 10.1016/j.jhazmat.2019.02.09810.1016/j.jhazmat.2019.02.098 ↗
48. 10.1039/D2DT03248A10.1039/D2DT03248A ↗
49. 10.3390/en1223456510.3390/en12234565 ↗
50. 10.1016/j.snb.2018.04.11910.1016/j.snb.2018.04.119 ↗
51. 10.1039/C9NR07699A10.1039/C9NR07699A ↗
52. 10.1016/j.snb.2019.12668710.1016/j.snb.2019.126687 ↗
53. 10.1021/acsanm.3c0102910.1021/acsanm.3c01029 ↗
54. 10.1016/j.snb.2022.13155010.1016/j.snb.2022.131550 ↗
55. 10.1016/j.snb.2019.12701110.1016/j.snb.2019.127011 ↗
56. 10.1016/j.apsusc.2022.15476910.1016/j.apsusc.2022.154769 ↗
57. 10.1021/acsami.0c1308910.1021/acsami.0c13089 ↗
58. 10.1021/acssensors.2c0196910.1021/acssensors.2c01969 ↗
59. 10.1002/admt.20220025510.1002/admt.202200255 ↗
60. 10.1016/j.snb.2022.13170510.1016/j.snb.2022.131705 ↗
61. 10.1016/j.snb.2018.08.05910.1016/j.snb.2018.08.059 ↗
62. 10.1016/j.matlet.2013.08.11810.1016/j.matlet.2013.08.118 ↗
63. 10.1016/j.snb.2022.13220210.1016/j.snb.2022.132202 ↗
64. 10.1016/0584-8539(94)01216-410.1016/0584-8539(94)01216-4 ↗
65. 10.1016/0039-6028(79)90411-410.1016/0039-6028(79)90411-4 ↗
66. 10.1007/s40089-019-0280-810.1007/s40089-019-0280-8 ↗