Investigating the Influence of Metal–Organic Framework Loading on the Filtration Performance of Electrospun Nanofiber Air Filters
Source abstract
Abstract
Integrating metal–organic frameworks (MOFs) into electrospun nanofiber filters has become an effective method for improving particle filtration efficiency. This study hypothesized that there is an optimal amount of MOFs that can be integrated into electrospun nanofiber filters to achieve the maximum particle removal efficiency while minimizing the corresponding MOF synthesis time. To test the hypothesis, this study systematically explored the influence of the time-dependent in situ growing process of zeolitic imidazolate framework-67 (ZIF-67), a typical type of MOFs, on the filtration performance of polyacrylonitrile (PAN) electrospun nanofibers. The results show that the surface morphology and chemical composition of the PAN/ZIF-67 hybrid nanofiber filters gradually changed with the reaction time. For PAN/ZIF-67 hybrid nanofiber filters with relatively low initial PM0.3–0.4 filtration efficiency, a reaction time of only 5 min was sufficient for the synthesis of the amount of ZIF-67 that maximized the PM0.3–0.4 filtration efficiency. However, for thick filters with high original PM0.3–0.4 filtration efficiency (>90%), the integration of ZIF-67 was not necessary, because the efficiency enhancement would not be significant. In addition, the enhancement of filtration efficiency for ultrafine particles was positively correlated with the amount of incorporated ZIF-67. In summary, this study shortened the synthesis time of the in situ incorporation of MOFs into electrospun nanofiber filters from more than 10 h (reported in the literature) to only 5 min.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Aerosol Filtration and Electrostatic Precipitation · High voltage insulation and dielectric phenomena · Electrospun Nanofibers in Biomedical Applications
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. 999077, Hong Kong SAR, China
Department of Building Science, Tsinghua University, Beijing 100084, China
Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518057, China
Funding
Research Grants Council, University Grants Committee · Hong Kong Centre For Logistics Robotic
References (62)
Show source-provided references
1. Air Pollution. World Health Organization. https://www.who.int/health-topics/air-pollution (accessed 2022-02-23).
2. 10.1016/j.apmt.2020.10065310.1016/j.apmt.2020.100653 ↗
3. 10.1016/j.envint.2014.10.00510.1016/j.envint.2014.10.005 ↗
4. 10.1021/acs.estlett.8b0036010.1021/acs.estlett.8b00360 ↗
5. 10.1016/S2542-5196(18)30261-410.1016/S2542-5196(18)30261-4 ↗
6. 10.1007/s00038-019-01202-710.1007/s00038-019-01202-7 ↗
7. 10.1289/ehp.903010.1289/ehp.9030 ↗
8. 10.1038/s41586-020-2271-310.1038/s41586-020-2271-3 ↗
9. 10.1016/j.atmosenv.2010.09.04810.1016/j.atmosenv.2010.09.048 ↗
10. 10.1080/0278682820895858410.1080/02786828208958584 ↗
11. 10.1039/C9NR08851B10.1039/C9NR08851B ↗
12. 10.1016/j.atmosenv.2010.09.04810.1016/j.atmosenv.2010.09.048 ↗
13. 10.1111/ina.1256610.1111/ina.12566 ↗
14. 10.1016/j.enbuild.2012.01.00110.1016/j.enbuild.2012.01.001 ↗
15. 10.1016/j.buildenv.2017.10.02510.1016/j.buildenv.2017.10.025 ↗
16. 10.1016/j.enbuild.2021.11077310.1016/j.enbuild.2021.110773 ↗
17. 10.1021/acs.accounts.7b0021810.1021/acs.accounts.7b00218 ↗
18. 10.1021/acs.chemrev.8b0059310.1021/acs.chemrev.8b00593 ↗
19. 10.1038/srep3547210.1038/srep35472 ↗
20. 10.1002/mame.20160035310.1002/mame.201600353 ↗
21. 10.1016/B978-0-323-51270-1.00012-110.1016/B978-0-323-51270-1.00012-1 ↗
22. 10.1016/j.apmt.2022.10136910.1016/j.apmt.2022.101369 ↗
23. 10.1038/s41578-021-00291-210.1038/s41578-021-00291-2 ↗
24. 10.1021/acsami.1c0445010.1021/acsami.1c04450 ↗
25. 10.1016/j.jcis.2017.09.10110.1016/j.jcis.2017.09.101 ↗
26. 10.1007/s12221-015-4624-310.1007/s12221-015-4624-3 ↗
27. 10.1002/advs.20190259010.1002/advs.201902590 ↗
28. 10.1016/j.ica.2018.12.02110.1016/j.ica.2018.12.021 ↗
29. 10.1016/j.molliq.2019.02.13910.1016/j.molliq.2019.02.139 ↗
30. 10.1021/jacs.6b0255310.1021/jacs.6b02553 ↗
31. 10.1002/anie.20160665610.1002/anie.201606656 ↗
32. 10.1021/acs.chemmater.7b0094910.1021/acs.chemmater.7b00949 ↗
33. 10.1021/jacs.6b0255310.1021/jacs.6b02553 ↗
34. 10.1038/nature1377410.1038/nature13774 ↗
35. 10.1021/acs.nanolett.6b0077110.1021/acs.nanolett.6b00771 ↗
36. 10.1021/acsami.1c1179610.1021/acsami.1c11796 ↗
37. 10.1039/C8TA04539A10.1039/C8TA04539A ↗
38. 10.1039/C7TA08210J10.1039/C7TA08210J ↗
39. 10.1016/j.jhazmat.2020.12216410.1016/j.jhazmat.2020.122164 ↗
40. 10.1039/C6TA07860E10.1039/C6TA07860E ↗
41. 10.1016/j.memsci.2019.11746710.1016/j.memsci.2019.117467 ↗
42. 10.1021/ie303122u10.1021/ie303122u ↗
43. 10.1016/0022-2860(95)08877-X10.1016/0022-2860(95)08877-X ↗
44. 10.1039/C7TA06924C10.1039/C7TA06924C ↗
45. 10.1016/j.matlet.2012.05.07710.1016/j.matlet.2012.05.077 ↗
46. 10.1007/s10965-007-9130-x10.1007/s10965-007-9130-x ↗
47. 10.1016/j.enbuild.2017.10.07310.1016/j.enbuild.2017.10.073 ↗
48. 10.1016/j.buildenv.2018.06.02110.1016/j.buildenv.2018.06.021 ↗
49. 10.1080/00022470.1980.1046459210.1080/00022470.1980.10464592 ↗
50. 10.1002/adfm.20190955410.1002/adfm.201909554 ↗
51. 10.1016/j.cej.2021.13406910.1016/j.cej.2021.134069 ↗
52. 10.1002/adfm.20170668010.1002/adfm.201706680 ↗
53. 10.1002/adma.20160622110.1002/adma.201606221 ↗
54. 10.1039/C8EN01084F10.1039/C8EN01084F ↗
55. 10.1016/j.memsci.2019.11746710.1016/j.memsci.2019.117467 ↗
56. 10.1016/j.carbpol.2018.09.03910.1016/j.carbpol.2018.09.039 ↗
57. 10.1021/acsami.0c0888110.1021/acsami.0c08881 ↗
58. 10.1016/j.cej.2015.02.09410.1016/j.cej.2015.02.094 ↗
59. 10.1021/cm103571y10.1021/cm103571y ↗
60. 10.1016/j.seppur.2014.02.01710.1016/j.seppur.2014.02.017 ↗
61. 10.1016/j.powtec.2011.06.02510.1016/j.powtec.2011.06.025 ↗
62. 10.1016/j.seppur.2010.06.00110.1016/j.seppur.2010.06.001 ↗