journal article·2023-05-12

Birnessite-Type Manganese Dioxide Nanosheets on Metal–Organic Frameworks with High Catalytic Activity in Ozone Decomposition

Yu-Hui Liu, Jia-Ao Lv, Ming-Ming Xu, Chen Dong, Xiao-Min Liu, Jian-Rong Li, Lin-Hua Xie

ACS Applied Nano Materials· Volume 6· Issue 9
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Abstract

Ground-level ozone (O 3 ) poses a threat to both human health and ecosystems; the development of O 3 decomposition catalysts is thus of fundamental and practical significance. In the past decades, metal oxides have been widely investigated for O 3 elimination due to their low cost and high catalytic activity. Recently, it has been demonstrated that metal–organic frameworks (MOFs) could also show high performance in the removal of O 3 and even other airborne gaseous pollutants at the same time. Herein, we report a facile synthetic method to prepare the composite of the well-known MOF, UiO-66-NH 2, and birnessite-type manganese dioxide, namely, δ-MnO 2, where the nanosheets of δ-MnO 2 are vertically aligned and densely grown on the crystal surfaces of UiO-66-NH 2 . The δ-MnO 2 /UiO-66-NH 2 composite could completely remove low-concentration O 3 (5 ppm) in both dry and highly humid (RH = 80%) air at ambient temperature and pressure with a space velocity of 2000 L h –1 g –1 . In contrast, a single-phase sample of δ-MnO 2 or the typical O 3 decomposition catalyst, α-MnO 2, showed a much lower O 3 removal efficiency under the same experimental conditions. Due to the ultrathin morphology (high surface area) and well-separated distribution (no aggregation) of δ-MnO 2 nanosheets in the δ-MnO 2 /UiO-66-NH 2 composite, there are rich Mn 2+ /Mn 3+ atoms and oxygen vacancies on its surface, contributing to its high performance in catalytic O 3 decomposition. This work demonstrated a facile method to prepare MOF-based composites with fascinating nanostructures and high catalytic activity in O 3 decomposition, which are also potentially useful in other catalytic reactions.
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Research context

OpenAlex topics: Metal-Organic Frameworks: Synthesis and Applications · Catalytic Processes in Materials Science · Gas Sensing Nanomaterials and Sensors

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

Affiliations

Beijing Key Laboratory for Green Catalysis and Separation and Department of Environmental Chemical Engineering, Beijing University of Technology, Beijing 100124, P. R. China

Institute of Circular Economy, Beijing University of Technology, Beijing 100124, P. R. China

Funding

Beijing Municipal Commission of Education · National Natural Science Foundation of China

References (49)

Show source-provided references
1. 10.1016/j.envres.2003.07.00210.1016/j.envres.2003.07.002 ↗
2. 10.1017/S204579601900062310.1017/S2045796019000623 ↗
3. 10.1016/j.scitotenv.2019.13618810.1016/j.scitotenv.2019.136188 ↗
4. 10.1016/j.jes.2020.03.05810.1016/j.jes.2020.03.058 ↗
5. 10.1016/S1010-6030(03)00287-910.1016/S1010-6030(03)00287-9 ↗
6. 10.1007/s11144-005-0299-x10.1007/s11144-005-0299-x ↗
7. 10.1016/S0926-3373(01)00172-210.1016/S0926-3373(01)00172-2 ↗
8. 10.1016/j.cattod.2006.08.04810.1016/j.cattod.2006.08.048 ↗
9. 10.1016/j.cattod.2007.11.02010.1016/j.cattod.2007.11.020 ↗
10. 10.1081/CR-10010026310.1081/CR-100100263 ↗
11. 10.1039/C6CY00301J10.1039/C6CY00301J ↗
12. 10.1016/j.apcatb.2016.02.05510.1016/j.apcatb.2016.02.055 ↗
13. 10.1016/j.jes.2019.12.00410.1016/j.jes.2019.12.004 ↗
14. 10.1021/acs.jpcc.5b0809510.1021/acs.jpcc.5b08095 ↗
15. 10.1080/0191951920855227310.1080/01919519208552273 ↗
16. 10.1016/S0926-3373(96)00044-610.1016/S0926-3373(96)00044-6 ↗
17. 10.1016/S0926-3373(97)00017-910.1016/S0926-3373(97)00017-9 ↗
18. 10.1016/j.apcatb.2020.11908810.1016/j.apcatb.2020.119088 ↗
19. 10.1016/j.apcatb.2016.08.05010.1016/j.apcatb.2016.08.050 ↗
20. 10.1021/acs.jpcc.7b0793110.1021/acs.jpcc.7b07931 ↗
21. 10.1039/C6CY01663D10.1039/C6CY01663D ↗
22. 10.1021/acs.est.8b0429410.1021/acs.est.8b04294 ↗
23. 10.1038/s41467-021-24424-x10.1038/s41467-021-24424-x ↗
24. 10.1021/acs.est.9b0368910.1021/acs.est.9b03689 ↗
25. 10.1016/j.apcatb.2017.02.06810.1016/j.apcatb.2017.02.068 ↗
26. 10.1126/science.123044410.1126/science.1230444 ↗
27. 10.1002/anie.20181026810.1002/anie.201810268 ↗
28. 10.1021/acscatal.1c0150610.1021/acscatal.1c01506 ↗
29. 10.1021/jacs.1c0102710.1021/jacs.1c01027 ↗
30. 10.1038/s41467-022-32678-210.1038/s41467-022-32678-2 ↗
31. 10.1021/acsami.1c0221310.1021/acsami.1c02213 ↗
32. 10.1002/anie.20080549410.1002/anie.200805494 ↗
33. 10.1039/C2CS35320B10.1039/C2CS35320B ↗
34. 10.1002/anie.20046140810.1002/anie.200461408 ↗
35. 10.1038/s41586-020-2738-210.1038/s41586-020-2738-2 ↗
36. 10.1016/j.jcis.2022.12.02610.1016/j.jcis.2022.12.026 ↗
37. 10.1016/j.jhazmat.2023.13122810.1016/j.jhazmat.2023.131228 ↗
38. 10.1016/j.apcatb.2017.01.04010.1016/j.apcatb.2017.01.040 ↗
39. 10.1039/c3cc46105j10.1039/c3cc46105j ↗
40. 10.1039/C8TA11300A10.1039/C8TA11300A ↗
41. 10.1021/cm900166h10.1021/cm900166h ↗
42. 10.1021/cm102601v10.1021/cm102601v ↗
43. 10.1073/pnas.170683611410.1073/pnas.1706836114 ↗
44. 10.1021/acsomega.2c0012010.1021/acsomega.2c00120 ↗
45. 10.1039/C8NJ00240A10.1039/C8NJ00240A ↗
46. 10.1021/acsnano.8b0062110.1021/acsnano.8b00621 ↗
47. 10.1016/j.apcatb.2021.12046610.1016/j.apcatb.2021.120466 ↗
48. 10.1021/es100253c10.1021/es100253c ↗
49. 10.1016/S0926-3373(99)00111-310.1016/S0926-3373(99)00111-3 ↗