Switching Xe/Kr adsorption selectivity in modified SBMOF-1: a theoretical study

Qian, Jiao-jiao; Chen, Guanghui; Xiao, Songtao; Li, Hui-bo; Ouyang, Yu; Wang, Qiang

doi:10.1039/d0ra02212h

Cited by 21 publications

(12 citation statements)

References 44 publications

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“…Note that the Lennard-Jones parameters for all simulations are collected in Table S1. Moreover, the equilibrium selectivity S Kr/Xe was calculated according to eq where x i and y i denote the mole fractions of species i in the adsorbed and bulk phases.…”

Section: Computational Detailsmentioning

confidence: 99%

“…In addition, all the GCMC simulations were carried out with the RASPA program . We consider that 100,000 equilibration and production cycles in the current work used in the GCMC method with the RASPA program are reasonable, since the equilibration and production cycles used in many reports ,,, are equal to or less than those of the current work. Moreover, the fluctuation method was implemented to calculate adsorption heat Q st using eq where R , T , U , N , and brackets are the gas constant, temperature, energy, number of particles, and ensemble average, respectively.…”

Section: Computational Detailsmentioning

confidence: 99%

“…Note that the Lennard-Jones parameters for all simulations are collected in Table S1. Moreover, the equilibrium selectivity S Kr/Xe was calculated according to eq 1 33…”

Section: Computational Detailsmentioning

confidence: 99%

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Multiscale Computational Screening of Metal–Organic Frameworks for Kr/Xe Adsorption Separation: A Structure–Property Relationship-Based Screening Strategy

Lin

Xue-lian

Heng

et al. 2021

ACS Appl. Mater. Interfaces

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The separation of radioactive noble gases, such as Xe and Kr, has attracted special attention in the context of used nuclear fuel (UNF). In this study, 180 metal–organic frameworks (MOFs) formally used for selective adsorptions of ethane and ethylene, with a similar kinetic diameter to Kr and Xe, were initially screened for the Kr/Xe separation using the grand canonical Monte Carlo (GCMC) method. Then, the structure–adsorption property relationships were generalized, that is, the MOFs of higher Kr/Xe selectivity are with the porosity at 0.2–0.4 and the ratio of the largest cavity diameter/pore limiting diameter at 1.0–2.4. Based on the relationships, six reported MOFs with large Kr uptakes and Kr/Xe selectivities were experimentally screened out and validated by GCMC simulations within the CoRE-MOF database, which are higher than most reported MOFs under conditions pertinent to nuclear fuel reprocessing of an 80/20 v/v mixture of Kr/Xe at normal temperature and pressure. Further simulations reveal that higher Kr uptakes and Kr/Xe selectivities of six MOFs result from the confinement effect of the pores. Molecular dynamic simulations showed that the six MOFs are ideal membrane separation materials of Kr from Xe, which are driven by adsorption and diffusion. Analyses of electronic structure-based density functional theory calculations showed that the main interaction between Kr and the six MOFs is van der Waals force dominated by dispersion and induction interactions. Therefore, the generalized structure–adsorption property relationships may assist the screening of MOFs for the separation and production of Kr/Xe from UNF industrially.

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Section: Computational Detailsmentioning

confidence: 99%

Section: Computational Detailsmentioning

confidence: 99%

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Multiscale Computational Screening of Metal–Organic Frameworks for Kr/Xe Adsorption Separation: A Structure–Property Relationship-Based Screening Strategy

Lin

Xue-lian

Heng

et al. 2021

ACS Appl. Mater. Interfaces

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“…used a computational screening approach to predict that calcium-based nanoporous MOF, SBMOF-1, was one of the most effective MOFs for Xe adsorption. Column breakthrough experiments conducted at room temperature showed a high uptake capacity of 11.5 mmol kg –1 even in the presence of water vapour [ 70 ]. The high uptake capacity of this MOF was attributed to the optimal pore size and the dense wall of the MOF structure.…”

Section: Sequestration Of Radionuclides By Mofsmentioning

confidence: 99%

Metal-organic frameworks as effective sensors and scavengers for toxic environmental pollutants

Karmakar

Velasco

2022

National Science Review

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Metal-organic Frameworks (MOFs) constructed from a rich library of organic struts and metal ions/clusters represent promising candidates for a wide range of applications. The unique structural feature, porous nature, easy tunability and processability of these materials make them an outstanding class of materials for tackling some cutthroat global problems related to energy and environment. Among them, environmental pollution is one aspect which has increased at an alarming rate of the past decade or so. With rapid urbanization and industrialization, toxic environmental pollutants are constantly released and accumulated leading to serious contaminations in water bodies and thereby having adverse effects on human health. Recent studies have shown that many toxic pollutants as enlisted by World Health Organization (WHO) and the U.S. Environmental Protection Agency (US EPA) can be selectively detected, captured, sequestered, and removed by MOFs from air and aquatic systems. Most of these sensing/capture processes in MOFs are quantifiable and effective for even trace amount of targeted chemical species. The functional sites (ligands and metals) play a critical rule in such recognition processes and offer an extensive scope of structural tunability for guest (pollutants, toxic entities) recognition. Whereas on hand, the underlying mechanisms governing such sensing and capture are important, it is also crucial to identify MOFs which are best suited for commercial applications for the future. In this review article, we provide an overview on the most recent progress in the sensing, capture and removal of various common toxic pollutants, including neutral and ionic, inorganic and organic species, with brief discussions on the mechanism and the efficacy of selected MOFs.

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“…The UFF force field was applied to describe the atoms of adsorbent, while the TraPPE force field was used for the krypton and xenon atoms, which have been successfully employed to describe the adsorption of Kr and Xe on MOF materials . Our group , also utilized such force fields to describe UTSA-280 and Mg-SBMOF-1 on the Xe/Kr selective adsorption. The high-throughput GCMC simulations in this work were performed with the HT-CADSS program.…”

Section: Computational Detailsmentioning

confidence: 99%

XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr

Heng

Jiang²,

Yan

et al. 2021

ACS Omega

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The inert gases Xe and Kr mainly exist in the used nuclear fuel (UNF) with the Xe/Kr ratio of 20:80, which it is difficult to separate. In this work, based on the G-MOFs database, high-throughput computational screening for metal–organic frameworks (MOFs) with high Xe/Kr adsorption selectivity was performed by combining grand canonical Monte Carlo (GCMC) simulations and machine learning (ML) technique for the first time. From the comparison of eight classical ML models, it is found that the XGBoost model with seven structural descriptors has superior accuracy in predicting the adsorption and separation performance of MOFs to Xe/Kr. Compared with energetic or electronic descriptors, structural descriptors are easier to obtain. Note that the determination coefficients R 2 of the generalized model for the Xe adsorption and Xe/Kr selectivity are very close to 1, at 0.951 and 0.973, respectively. In addition, 888 and 896 MOFs have been successfully predicted by the XGBoost model among the top 1000 MOFs in adsorption capacity and selectivity by GCMC simulation, respectively. According to the feature engineering of the XGBoost model, it is shown that the density (ρ), porosity (ϕ), pore volume (Vol), and pore limiting diameter (PLD) of MOFs are the key features that affect the Xe/Kr adsorption property. To test the generalization ability of the XGBoost model, we also tried to screen MOF adsorbents on the CO 2 /CH 4 mixture, it is found that the prediction performance of XGBoost is also much better than that of the traditional machine learning models although with the unbalanced data. Note that the dimension of features of MOFs is low while the quantity of MOF samples in database is very large, which is suitable for the prediction by model such as XGBoost to search the global minimum of cost function rather than the model involving feature creation. The present study represents the first report using the XGBoost algorithm to discover the MOF adsorbates.

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Switching Xe/Kr adsorption selectivity in modified SBMOF-1: a theoretical study

Abstract: The separation of Xe/Kr mixtures in used nuclear fuel (UNF) has attracted lots of attention, but no report on the adsorption and separation of Kr from mixed Kr/Xe at room temperature can be found.

Cited by 21 publications

References 44 publications

Multiscale Computational Screening of Metal–Organic Frameworks for Kr/Xe Adsorption Separation: A Structure–Property Relationship-Based Screening Strategy

Multiscale Computational Screening of Metal–Organic Frameworks for Kr/Xe Adsorption Separation: A Structure–Property Relationship-Based Screening Strategy

Metal-organic frameworks as effective sensors and scavengers for toxic environmental pollutants

XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr

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