Mixed matrix membranes containing well-designed composite microcapsules for CO2 separation

Bin Zhu, Jindun Liu, Shaofei Wang, Jingtao Wang, Min Liu, Zhikun Yan, Feng Shi, Jiahao Li, Yifan Li

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

Hollow fillers with tailored nanostructures and functionalities have become promising candidates for advanced mixed matrix membranes (MMMs). Herein, polydopamine/poly (ethylene glycol) (PEG) composite microcapsules are synthesized by hard template method and embedded into the Pebax matrix to fabricate MMMs for CO2 capture. As a well-known biomimetic adhesive, polydopamine in the capsule wall renders adequate polymer-filler interfacial adhesion. The template removal process produces through-wall mesopores, which allow rapid gas diffusion into the lumen, further significantly reducing the trans-membrane mass transfer resistance. The remaining PEG in the capsule wall not only increases CO2 affinity, but also avoids excessive chain rigidification at polymer-filler interface. In this way, the composite capsules, compared with those without PEG, confer significantly enhanced separation performance on membranes. The optimal gas transport property of the resultant membranes is obtained with a CO2 permeability of 510 Barrer and an ideal selectivity of 84.6 for CO2/N2 at humidified state, i.e., 108%, 98% higher than those of neat Pebax membrane, respectively. In addition, owing to dopamine-enabled strong adhesion, the MMMs exhibit better stability than Pebax membrane in the long-term test at 85°C.
Original languageEnglish (US)
Pages (from-to)650-657
Number of pages8
JournalJournal of Membrane Science
Volume572
DOIs
StatePublished - Nov 20 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The study was financially supported by National Natural Science Foundation of China (21506196 and 21878277), Natural Science Foundation of Henan province (182300410268), China Postdoctoral Science Foundation (2015M570633 and 2017T100538), and Outstanding Young Talent Research Fund of Zhengzhou University (1521324002). We also gratefully acknowledge the instrument support from Center of Advanced Analysis & Computational Science, Zhengzhou University.

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