High-throughput screening of metal–organic frameworks for hydrogen purification

Wang, Shihui; Cheng, Min; Li, Luo; Ji, Xu; Liu, Chong; Bi, Kexin; Zhou, Li

doi:10.1016/j.cej.2022.138436

Cited by 4 publications

(2 citation statements)

References 50 publications

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“…With the rapid development of computer technology, high-throughput computational screening and assembly adsorbents have become research hotspots in the discovery of novel MOFs. For example, Wang et al first performed GCMC simulations of CH 4 /H 2 adsorption separation within 200 MOFs, and then the structure–adsorption property relationships (SAPRs) were established with 4 Å ≤ LCD ≤ 11 Å and 600 m 2 /cm 3 ≤ VSA ≤ 1800 m 2 /cm 3 ; finally, TAKTAD and XILLEL were successfully sieved from 5096 MOFs with large CH 4 working capacities and selectivities; in addition, by employing a hybrid approach combining machine learning algorithm of random forest (RF) with molecular simulations, Simon et al predicted that JAVTAC and KAXQIL have better Xe/Kr separation performance with Xe uptakes and selectivities up to 2.82 mmol/g and 18.88, respectively. Furthermore, our group adopted feature engineering of machine learning technique to data-mine the key “genes” (important metal nodes and links) from different MOF databases, then crossly assembled novel MOFs based on the idea of material genomics, and found Al 2 O 6 -fum_B-hmof8_No1 and FASFUD with substantial Xe/Kr separation performance.…”

Section: Introductionmentioning

confidence: 99%

Combination of High-Throughput Screening and Assembly to Discover Efficient Metal–Organic Frameworks on Kr/Xe Adsorption Separation

Du,

Xiao,

Wang

et al. 2023

J. Phys. Chem. B

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Recycling Kr and Xe from used nuclear fuel (UNF) is conducive to regenerating economy and protecting the environment, and it is urgent to screen or design high-performance cutting-edge metal−organic framework (MOF) materials for Kr/ Xe adsorption separation. After grand canonical Monte Carlo (GCMC) simulations of Kr/Xe adsorption separation on 11,000 frameworks in CoRE MOFs (2019), the important structure− adsorption property relationship (SAPR) was induced; that is, the porosity (φ) at 0.30−0.40, LCD/PLD at 1.00−1.49, density (ρ) range between 1.20 and 2.30 g/cm 3 , and PLD at 2.40−3.38 Å can be utilized to screen for high-performance G-MOFs and hMOFs. In addition, the key "genes" (metal nodes and linkers) of MOFs determining the Kr/Xe adsorption separation were data-mined by a machine learning technique, which were assembled into novel MOFs. After comprehensive consideration of thermal stability and the adsorbent performance score (APS), eight promising MOFs on Kr/Xe separation with the APS more than 1290.89 were screened out and assembled, which are better than most of the reported frameworks. Note that the adsorption isotherms of these MOFs on Kr and Xe belong to type I curve with the thermodynamic equilibrium mechanism on Kr/Xe based on the confinement effect. Furthermore, according to the electronic structure calculations of the independent gradient model based on Hirshfeld partition (IGMH) and energy decomposition analysis, it is found that the interactions between guests and frameworks are vdW forces with dominant induction energy (Eind). In addition, the electrostatic potential gradients of frameworks are generally linearly negative correlated with Kr uptakes. Therefore, both the geometrical and electronic structures dominate the adsorption separation performance on Kr/Xe. Interestingly, these eight MOFs are also suitable for the separation of CH 4 /H 2 with considerable selectivities and CH 4 uptakes of up to 2566.67 and 3.04 mmol/g, respectively. Herein, the accurately constructed SAPR and material genomics strategy should be helpful for the experimental discovery of novel MOFs on Kr/Xe separation experimentally.

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Section: Introductionmentioning

confidence: 99%

Combination of High-Throughput Screening and Assembly to Discover Efficient Metal–Organic Frameworks on Kr/Xe Adsorption Separation

Du,

Xiao,

Wang

et al. 2023

J. Phys. Chem. B

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“…The performance of PSA technology is mainly determined by the adsorbent. In comparison to traditional nanoporous adsorbents, metal-organic frameworks (MOFs), which are assembled by organic linkers and metal nodes, have been widely used throughout several applications, especially in the field of gas adsorption and separation, owing to their high surface area, large pore volume, tunable structure, and rich functionality [5][6][7][8][9][10]. It has been confirmed that MOFs can be a promising platform for adsorption and separation of Xe/Kr mixture with considerable capacity and selectivity [11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Xe/Kr Separation in Metal-Organic Frameworks by a Precursor-Based Neural Network Synergistic with a Polarizable Adsorbate Model

Liu,

Xia,

Huang

et al. 2023

Molecules

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Adsorption and separation of Xe/Kr are significant for making high-density nuclear energy environmentally friendly and for meeting the requirements of the gas industry. Enhancing the accuracy of the adsorbate model for describing the adsorption behaviors of Xe and Kr in MOFs and the efficiency of the model for predicting the separation potential (SP) value of Xe/Kr separation in MOFs helps in searching for promising MOFs for Xe/Kr adsorption and separation within a short time and at a low cost. In this work, polarizable and transferable models for mimic Xe and Kr adsorption behaviors in MOFs were constructed. Using these models, SP values of 38 MOFs at various temperatures and pressures were calculated. An optimal neural network model called BPNN-SP was designed to predict SP value based on physical parameters of metal center (electronegativity and radius) and organic linker (three-dimensional size and polarizability) combined with temperature and pressure. The regression coefficient value of the BPNN-SP model for each data set is higher than 0.995. MAE, MBE, and RMSE of BPNN-SP are only 0.331, −0.002, and 0.505 mmol/g, respectively. Finally, BPNN-SP was validated by experiment data from six MOFs. The transferable adsorbate model combined with the BPNN-SP model would highly improve the efficiency for designing MOFs with high performance for Xe/Kr adsorption and separation.

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Hydrogen Separation and Purification

Cavaliere

2023

Water Electrolysis for Hydrogen Production

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High-throughput screening of metal–organic frameworks for hydrogen purification

Cited by 4 publications

References 50 publications

Combination of High-Throughput Screening and Assembly to Discover Efficient Metal–Organic Frameworks on Kr/Xe Adsorption Separation

Combination of High-Throughput Screening and Assembly to Discover Efficient Metal–Organic Frameworks on Kr/Xe Adsorption Separation

Prediction of Xe/Kr Separation in Metal-Organic Frameworks by a Precursor-Based Neural Network Synergistic with a Polarizable Adsorbate Model

Hydrogen Separation and Purification

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