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Get Free AccessGraphene oxide (GO) based multi-layered membranes have shown outstanding molecular-sieving properties for gas separation, surpassing the upper bound for polymeric membranes especially for hydrogen decarbonization. At the same time, the mechanism of gas permeation through such 2D GO membranes is very different in comparison to the traditional polymeric membranes due to multilayer, laminated nature of the former. For strategical design of novel membranes based on two-dimensional materials, it is important to understand the mechanism and to be able to measure two key parameters for gas transport: diffusivity and solubility. Such measurements are well established for the characterization of transport properties of bulk polymeric membranes. However, it is still a challenge to measure gas diffusion coefficients directly and accurately in ultra-thin multi-layered membranes. The lack of characterization limits our understanding of the mechanisms of gas transport though such membranes. In this work, we applied a time-lag method to determine the diffusivities for He, H2, O2, N2, CH4, CO2 and H2/CO2 equimolar mixture by on-line mass spectrometry. In contrast to polymeric membranes, the diffusivity and diffusion activation energy for all gases in 2D membranes are exponentially dependent on pathway length. Thus, in 2D membrane we can use an easy strategy to precisely regulate permeability and selectivity by the adjustment of the number of nanolayers and the size of 2D flakes, which is not possible using traditional polymeric membranes. This study is important for both the characterization and the standardization of gas transport properties of multi-layered membranes, and the design of novel membranes based on 2D materials.
Musen Chen, Maxim M. Trubyanov, Pengxiang Zhang, Qian Wang, Zelong Li, Konstantin ‘kostya’ Novoselov, Daria V. Andreeva (2023). Comprehensive characterization of gas diffusion through graphene oxide membranes. Journal of Membrane Science, 676, pp. 121583-121583, DOI: 10.1016/j.memsci.2023.121583.
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Type
Article
Year
2023
Authors
7
Datasets
0
Total Files
0
Language
English
Journal
Journal of Membrane Science
DOI
10.1016/j.memsci.2023.121583
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