Computational screening and design of nanoporous membranes for efficient carbon isotope separation

Wang, Jingqi; Zhou, Musen; Lu, Diannan; Fei, Weiyang; Wu, Jianzhong

doi:10.1016/j.gee.2020.07.025

Cited by 12 publications

(9 citation statements)

References 57 publications

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“…In contrast to our previous work in the separation of isotopic molecules, 48 the adsorption selectivity increases with the adsorption capacity of the noble gas as measured by Henry's constant for the heavier species in both MOFs and COFs. We calculated Henry's constants for different pairs of noble gases in carbon slit pores in which the interaction of each gas molecule with the surface is represented by the Steele 10-4-3 wall potential.…”

Section: Selectivity Of Mofs and Cofs As Adsorbentscontrasting

confidence: 92%

Virtual Screening of Nanoporous Materials for Noble Gas Separation

Wang

Zhou

et al. 2022

ACS Appl. Nano Mater.

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Noble gases are vital industrial chemicals widely used in various applications, such as cryogenics and optical devices. Compared with conventional technologies for the enrichment and separation of noble gases from the atmosphere, emerging methods based on advanced nanoporous materials have advantages in terms of energy and separation efficiency due to their tunable pore structure and chemical affinities. In this work, both sorption and transport properties are calculated via efficient theoretical models to screen large experimental libraries of nanoporous materials to enrich argon, krypton, and xenon from various mixtures. The theoretical predictions are validated by Monte Carlo simulation and molecular dynamics simulation. Promising candidates are identified for both adsorption separation and membrane separation of Ar/Kr, Kr/Xe, and Xe/Ar. The structure−property relation identified in this work provides insights for the design of nanoporous materials in noble gas separation.

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Section: Selectivity Of Mofs and Cofs As Adsorbentscontrasting

confidence: 92%

Virtual Screening of Nanoporous Materials for Noble Gas Separation

Wang

Zhou

et al. 2022

ACS Appl. Nano Mater.

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“…Both confirm the conclusion by the univariate analysis that the permeance performance of gas molecules with smaller kinetic diameters places a greater demand on the channel and geometry area of MOFMs. On the contrary, the order of RI of PLD for the permeability of three gas molecules is the same as the order of RI of kinetic diameter ( Dia ), Dia N 2 > Dia O 2 > Dia CO 2 , because PLD plays an important role in the confirmation of diffusion barrier and determines the gas molecule diffusion barrier in porous materials [ 51 , 52 ]. This is also explained by the phenomenon that the importance of PLD to S perm (CO 2 /N 2 ) is larger than S perm (CO 2 /O 2 ) .…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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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.

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“…52 Compared with the LCD distribution of MOFs in the background, which follows approximately a normal distribution with the mean between 4.5 and 5 Å, MOFs with top 0.5% diffusion selectivity has a slightly higher mean, between 5 and 5.5 Å, in the LCD distribution. According to our previous work, 28,29,55 a nanoporous material with the LCD larger than the molecular size would impose more attraction along the MEP, which is beneficial to achieve the diffusivity coefficient at the scale of practical interest.…”

Section: Resultsmentioning

confidence: 99%

Massively Parallel GPU-Accelerated String Method for Fast and Accurate Prediction of Molecular Diffusivity in Nanoporous Materials

Zhou

2021

ACS Appl. Nano Mater.

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The diffusivity of guest molecules in nanoporous materials is instrumental for practical applications ranging from gas separation to catalysis and energy storage. Conventional methods to predict diffusion coefficients are computationally demanding, in particular for polyatomic molecules with small diffusivity in nanoporous materials. In this work, we have implemented a massively parallel graphic processing unit (GPU)-accelerated string method to calculate the minimum energy path for the diffusion of polyatomic molecules in nanoporous materials. The GPU parallelization enables fast prediction of molecular diffusivity in nanoporous materials, speeding up the computation by a factor of over 500 in comparison with serial CPU calculations. The massively parallel GPUaccelerated string method yields diffusion coefficients in excellent agreement with results from molecular dynamics while reducing the computational cost by several orders of magnitude. It will thus open up opportunities for high-throughput screening and inverse design of nanoporous materials.

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Computational screening and design of nanoporous membranes for efficient carbon isotope separation

Cited by 12 publications

References 57 publications

Virtual Screening of Nanoporous Materials for Noble Gas Separation

Virtual Screening of Nanoporous Materials for Noble Gas Separation

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

Massively Parallel GPU-Accelerated String Method for Fast and Accurate Prediction of Molecular Diffusivity in Nanoporous Materials

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