Engineering MOF surface defects in mixed matrix membranes: An effective strategy to enhance MOF/polymer adhesion and control interfacial gas transport

Dong Fan, Aydin Ozcan, Osama Shekhah, Rocio Semino, Mohamed Eddaoudi, Guillaume Maurin

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

MOF/polymer adhesion in Mixed Matrix Membranes (MMMs) has been mainly enhanced so far via MOF and/or polymer functionalization to strengthen the interactions between the two components. This strategy, albeit effective, is generally accompanied by a drop in the permeability and/or selectivity performance of the MMMs. In this contribution, engineering structure defects at the MOF surfaces is proposed as an effective route to create pockets that immobilize part of the polymer chain, which is of crucial importance both to avoid plasticization issues and to enhance the MOF/polymer affinity while overcoming the adhesion/performance trade-off in MMMs. This engineered interfacial interlocking structure also serves as a bridge to accelerate the gas transport from the polymeric region towards the MOF pore entrance. This concept is showcased with a model MMM made of the prototypical UiO-66 MOF and the glassy Polymer of Intrinsic Microporosity-1 (PIM-1) and tested using CO2, CH4 and, N2 as guest species. Our computational findings reveal that a defective UiO-66 MOF surface improves the MOF/PIM-1 adhesion and contributes to accelerate the interfacial gas transport of the slender molecules CO2 and N2 and in a lesser extent of the spherical molecule CH4. This translates into a selective enhancement of the CO2 transport once combined with CH4 which paves the ways toward promising perspective for pre-combustion CO2 capture.
Original languageEnglish (US)
Pages (from-to)100029
JournalJournal of Membrane Science Letters
Volume2
Issue number2
DOIs
StatePublished - Jul 15 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-09-14
Acknowledgements: The research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST) (CCF 1972 project). The computational work was performed using HPC resources from GENCI-CINES (Grant A0120907613).

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