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Large-Scale Screening and Machine Learning for Metal-Organic Framework Membranes to Capture CO₂ from Flue Gas.

ABSTRACT: To combat global warming, as an energy-saving technology, membrane separation can be applied to capture CO₂ from flue gas. Metal-organic frameworks (MOFs) with characteristics like high porosity have great potential as membrane materials for gas mixture separation. In this work, through a combination of grand canonical Monte Carlo and molecular dynamics simulations, the permeability of three gases (CO₂, N₂, and O₂) was calculated and estimated in 6013 computation-ready experimental MOF membranes (CoRE-MOFMs). Then, the relationship between structural descriptors and permeance performance, and the importance of available permeance area to permeance performance of gas molecules with smaller kinetic diameters were found by univariate analysis. Furth

SUBMITTER: Situ Y

PROVIDER: S-EPMC9321510 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Large-Scale Screening and Machine Learning for Metal-Organic Framework Membranes to Capture CO₂ from Flue Gas.

OmicsDI is part of the ELIXIR infrastructure

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Dataset Information

Large-Scale Screening and Machine Learning for Metal-Organic Framework Membranes to Capture CO2 from Flue Gas.

OmicsDI is part of the ELIXIR infrastructure

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Large-Scale Screening and Machine Learning for Metal-Organic Framework Membranes to Capture CO₂ from Flue Gas.