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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. Furthermore, comparing the prediction accuracy of seven classification machine learning algorithms, XGBoost was selected to analyze the order of importance of six structural descriptors to permeance performance, through which the conclusion of the univariate analysis was demonstrated one more time. Finally, seven promising CoRE-MOFMs were selected, and their structural characteristics were analyzed. This work provides explicit directions and powerful guidelines to experimenters to accelerate the research on membrane separation for the purification of flue gas.

SUBMITTER: Situ Y

PROVIDER: S-EPMC9321510 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Supported by increasingly available reserves, natural gas is achieving greater adoption as a cleaner-burning alternative to coal in the power sector. As a result, carbon capture and sequestration from natural gas-fired power plants is an attractive strategy to mitigate global anthropogenic CO2 emissions. However, the separation of CO2 from other components in the flue streams of gas-fired power plants is particularly challenging due to the low CO2 partial pressure (∼40 mbar), which necessitates that candidate separation materials bind CO2 strongly at low partial pressures (≤4 mbar) to capture ≥90% of the emitted CO2. High partial pressures of O2 (120 mbar) and water (80 mbar) in these flue streams have also presented significant barriers to the deployment of new technologies for CO2 capture from gas-fired power plants. Here, we demonstrate that functionalization of the metal-organic framework Mg2(dobpdc) (dobpdc4- = 4,4'-dioxidobiphenyl-3,3'-dicarboxylate) with the cyclic diamine 2-(aminomethyl)piperidine (2-ampd) produces an adsorbent that is capable of ≥90% CO2 capture from a humid natural gas flue emission stream, as confirmed by breakthrough measurements. This material captures CO2 by a cooperative mechanism that enables access to a large CO2 cycling capacity with a small temperature swing (2.4 mmol CO2/g with ΔT = 100 °C). Significantly, multicomponent adsorption experiments, infrared spectroscopy, magic angle spinning solid-state NMR spectroscopy, and van der Waals-corrected density functional theory studies suggest that water enhances CO2 capture in 2-ampd-Mg2(dobpdc) through hydrogen-bonding interactions with the carbamate groups of the ammonium carbamate chains formed upon CO2 adsorption, thereby increasing the thermodynamic driving force for CO2 binding. In light of the exceptional thermal and oxidative stability of 2-ampd-Mg2(dobpdc), its high CO2 adsorption capacity, and its high CO2 capture rate from a simulated natural gas flue emission stream, this material is one of the most promising adsorbents to date for this important separation.

Project description:Several thousands of metal organic frameworks (MOFs) have been reported to date, but the information on H2/N2 separation performances of MOF membranes is currently very limited in the literature. We report the first large-scale computational screening study that combines state-of-the-art molecular simulations, grand canonical Monte Carlo (GCMC) and molecular dynamics (MD), to predict H2 permeability and H2/N2 selectivity of 3765 different types of MOF membranes. Results showed that MOF membranes offer very high H2 permeabilities, 2.5 × 103 to 1.7 × 106 Barrer, and moderate H2/N2 membrane selectivities up to 7. The top 20 MOF membranes that exceed the polymeric membranes' upper bound for H2/N2 separation were identified based on the results of initial screening performed at infinite dilution condition. Molecular simulations were then carried out considering binary H2/N2 and quaternary H2/N2/CO2/CO mixtures to evaluate the separation performance of MOF membranes under industrial operating conditions. Lower H2 permeabilities and higher N2 permeabilities were obtained at binary mixture conditions compared to the ones obtained at infinite dilution due to the absence of multicomponent mixture effects in the latter. Structure-performance relations of MOFs were also explored to provide molecular-level insights into the development of new MOF membranes that can offer both high H2 permeability and high H2/N2 selectivity. Results showed that the most promising MOF membranes generally have large pore sizes (>6 Å) as well as high surface areas (>3500 m2/g) and high pore volumes (>1 cm3/g). We finally examined H2/N2 separation potentials of the mixed matrix membranes (MMMs) in which the best MOF materials identified from our high-throughput screening were used as fillers in various polymers. Results showed that incorporation of MOFs into polymers almost doubles H2 permeabilities and slightly enhances H2/N2 selectivities of polymer membranes, which can advance the current membrane technology for efficient H2 purification.

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

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Dataset Information

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

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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.