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Get Free AccessA promising way to address modern environmental and energy supply challenges is via rapid implementation of decarbonization and hydrogen production technologies. Development of gas separation membranes with high selectivity and permeability is essential for these processes but is still a bottleneck. Our research focuses on achieving precise control of gas diffusion pathways through on-demand regulation of material interactions in thin composite membranes. We combine 2D covalent organic frameworks (COFs) and graphene oxide (GO) to create COF-GO composite membranes with desirable nanosheet stacking, controllable thicknesses and pathways for gases. By pH-assisted self-assembly, we fine-tune material interactions and achieve simultaneous enhancement of permeability and selectivity by increasing membrane thickness and regulating the interactions between COF and GO nanosheets by pH. At a thickness of 1.3 μm, the COF-GO membrane, assembled under pH 4, demonstrates impressive performance in H2/CO2 equimolar mixed gas conditions (at room temperature and 1 bar), with a H2 permeability of 366 Barrer, selectivity of 15.6, and long-term stability exceeding 200 h. This work paves the way for tailored, performing gas separation with superior long-term stability. It guides the unique 2D transport mechanism to be utilized under practical conditions. Our research offers a novel strategy for the design of composite membranes from two-dimensional (2D) materials for gas separation technologies. It contributes to sustainable decarbonization and hydrogen production solutions, bringing us closer to a greener, more environmentally friendly future.
Musen Chen, Maxim M. Trubyanov, Pengxiang Zhang, David Rodríguez‐San‐Miguel, Félix Zamora, Konstantin ‘kostya’ Novoselov, Daria V. Andreeva (2024). Control of gas selectivity and permeability through COF-GO composite membranes for sustainable decarbonization and hydrogen production. Carbon, 219, pp. 118855-118855, DOI: 10.1016/j.carbon.2024.118855.
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Type
Article
Year
2024
Authors
7
Datasets
0
Total Files
0
Language
English
Journal
Carbon
DOI
10.1016/j.carbon.2024.118855
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