journal article·2024-08Open access

Advancing Membrane Technology: Ordered Macroporous ZIF‐67 as a Filler in Mixed Matrix Membranes for Enhanced Propylene/Propane Separation

Daria Poloneeva, Shuvo Jit Datta, Ronell Sicat, Rushana Khairova, Luis Garzon‐Tovar, Anastasiya Bavykina, Mohamed Eddaoudi, Jorge Gascon

Small· Volume 20· Issue 32

Abstract

Abstract Conventional separation technologies for valuable commodities require substantial energy, accounting for 10%–15% of global consumption. Mixed‐matrix membranes (MMMs) offer a promising solution by combining processable polymers with selective inorganic fillers. Here, the potential of using ordered microporous structured materials is demonstrated as MMM fillers. The use of ordered macroporous ZIF‐67 in combination with the well‐known 6FDA‐DAM polymer leads to superior performance in the important separation of propylene from propane. The enhanced performance can be rationalized with the help of advanced microscopy, which demonstrates that the polymer is able to penetrate the macroporous network around which the MOF (Metal‐Organic Framework) is synthesized, resulting in a much better interphase between the two components and the homogeneous distribution of the filler, even at high loadings.
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Research context

OpenAlex topics: Metal-Organic Frameworks: Synthesis and Applications · Membrane Separation and Gas Transport · Zeolite Catalysis and Synthesis

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

Affiliations

Advanced Catalytic Materials (ACM) KAUST Catalysis Center (KCC) Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia

Functional Materials Design Discovery and Development (FMD3) Advanced Membranes & Porous Materials Center (AMPMC) Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia

KAUST Visualization Core Lab (KVL) King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia

Funding

King Abdullah University of Science and Technology

References (30)

Show source-provided references
1. 10.1038/532435a10.1038/532435a ↗
2. 10.1039/c2cc35418g10.1039/c2cc35418g ↗
3. 10.1039/C8CC03841D10.1039/C8CC03841D ↗
4. 10.1039/C4TA05225K10.1039/C4TA05225K ↗
5. 10.1016/j.memsci.2012.04.00310.1016/j.memsci.2012.04.003 ↗
6. 10.1021/acsami.6b1107410.1021/acsami.6b11074 ↗
7. 10.1016/j.seppur.2022.12076810.1016/j.seppur.2022.120768 ↗
8. 10.1016/j.ces.2023.11899810.1016/j.ces.2023.118998 ↗
9. 10.1016/j.memsci.2020.11810110.1016/j.memsci.2020.118101 ↗
10. 10.1039/c3ta11131h10.1039/c3ta11131h ↗
11. 10.1021/acs.iecr.6b0281110.1021/acs.iecr.6b02811 ↗
12. 10.1126/science.abe019210.1126/science.abe0192 ↗
13. 10.1038/nmat411310.1038/nmat4113 ↗
14. 10.1016/j.memsci.2015.01.03810.1016/j.memsci.2015.01.038 ↗
15. 10.1021/accountsmr.1c0010010.1021/accountsmr.1c00100 ↗
16. 10.1038/s41563-020-0764-y10.1038/s41563-020-0764-y ↗
17. 10.1021/jacs.9b0695710.1021/jacs.9b06957 ↗
18. pp.Thermo Fisher Scientific Machine Learning and Deep Learning for image processing segmentation and classification https://www.thermofisher/home/electron‐microscopy/products/software‐em‐3d‐vis/3d‐visualization‐analysis‐software/artificial‐intelligence.html(accessed: March 2024).
19. 10.1016/j.cageo.2018.03.00710.1016/j.cageo.2018.03.007 ↗
20. 10.1002/adma.20230029610.1002/adma.202300296 ↗
21. 10.1016/j.memsci.2016.12.05310.1016/j.memsci.2016.12.053 ↗
22. 10.1002/adma.20180751310.1002/adma.201807513 ↗
23. 10.1016/j.ces.2019.04.03210.1016/j.ces.2019.04.032 ↗
24. 10.1016/j.ces.2017.12.05110.1016/j.ces.2017.12.051 ↗
25. 10.1126/sciadv.abl684110.1126/sciadv.abl6841 ↗
26. 10.1016/j.memsci.2011.10.00310.1016/j.memsci.2011.10.003 ↗
27. 10.1016/j.memsci.2019.11768910.1016/j.memsci.2019.117689 ↗
28. 10.1021/acsami.9b0710610.1021/acsami.9b07106 ↗
29. 10.1002/adma.20230029610.1002/adma.202300296 ↗
30. 10.1016/j.cej.2023.14304810.1016/j.cej.2023.143048 ↗