Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162577
Title: Symbiosis-inspired de novo synthesis of ultrahigh MOF growth mixed matrix membranes for sustainable carbon capture
Authors: He, Shanshan
Zhu, Bin
Jiang, Xu
Han, Gang
Li, Songwei
Lau, Cher Hon
Wu, Yadong
Zhang, Yanqiu
Shao, Lu
Keywords: Engineering::Environmental engineering
Issue Date: 2022
Source: He, S., Zhu, B., Jiang, X., Han, G., Li, S., Lau, C. H., Wu, Y., Zhang, Y. & Shao, L. (2022). Symbiosis-inspired de novo synthesis of ultrahigh MOF growth mixed matrix membranes for sustainable carbon capture. Proceedings of the National Academy of Sciences of the United States of America, 119(1). https://dx.doi.org/10.1073/pnas.2114964119
Journal: Proceedings of the National Academy of Sciences of the United States of America 
Abstract: Mixed matrix membranes (MMMs) are one of the most promising solutions for energy-efficient gas separation. However, conventional MMM synthesis methods inevitably lead to poor filler-polymer interfacial compatibility, filler agglomeration, and limited loading. Herein, inspired by symbiotic relationships in nature, we designed a universal bottom-up method for in situ nanosized metal organic framework (MOF) assembly within polymer matrices. Consequently, our method eliminating the traditional postsynthetic step significantly enhanced MOF dispersion, interfacial compatibility, and loading to an unprecedented 67.2 wt % in synthesized MMMs. Utilizing experimental techniques and complementary density functional theory (DFT) simulation, we validated that these enhancements synergistically ameliorated CO2 solubility, which was significantly different from other works where MOF typically promoted gas diffusion. Our approach simultaneously improves CO2 permeability and selectivity, and superior carbon capture performance is maintained even during long-term tests; the mechanical strength is retained even with ultrahigh MOF loadings. This symbiosis-inspired de novo strategy can potentially pave the way for next-generation MMMs that can fully exploit the unique characteristics of both MOFs and matrices.
URI: https://hdl.handle.net/10356/162577
ISSN: 0027-8424
DOI: 10.1073/pnas.2114964119
Rights: © The Authors. This article is distributed under Creative Commons Attribution-NonCommercialNoDerivatives License 4.0 (CC BY-NC-ND).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:NEWRI Journal Articles

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