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Journal Abstract Search

206 related items for PubMed ID: 37791097

  • 1. Efficient Exploration of Adsorption Space for Separations in Metal-Organic Frameworks Combining the Use of Molecular Simulations, Machine Learning, and Ideal Adsorbed Solution Theory.
    Yu X, Tang D, Chng JY, Sholl DS.
    J Phys Chem C Nanomater Interfaces; 2023 Sep 28; 127(38):19229-19239. PubMed ID: 37791097
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  • 2. Adsorption-Based Separation of Near-Azeotropic Mixtures-A Challenging Example for High-Throughput Development of Adsorbents.
    Tang D, Gharagheizi F, Sholl DS.
    J Phys Chem B; 2021 Jan 28; 125(3):926-936. PubMed ID: 33448857
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  • 3. Modeling adsorption in metal-organic frameworks with open metal sites: propane/propylene separations.
    Fischer M, Gomes JR, Fröba M, Jorge M.
    Langmuir; 2012 Jun 05; 28(22):8537-49. PubMed ID: 22574969
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  • 5. Acetylene Storage and Separation Using Metal-Organic Frameworks with Open Metal Sites.
    Luna-Triguero A, Vicent-Luna JM, Madero-Castro RM, Gómez-Álvarez P, Calero S.
    ACS Appl Mater Interfaces; 2019 Aug 28; 11(34):31499-31507. PubMed ID: 31368697
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  • 6. Machine learning potential for modelling H2 adsorption/diffusion in MOFs with open metal sites.
    Liu S, Dupuis R, Fan D, Benzaria S, Bonneau M, Bhatt P, Eddaoudi M, Maurin G.
    Chem Sci; 2024 Apr 03; 15(14):5294-5302. PubMed ID: 38577379
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  • 8. Testing the accuracy of correlations for multicomponent mass transport of adsorbed gases in metal-organic frameworks: diffusion of H2/CH4 mixtures in CuBTC.
    Keskin S, Liu J, Johnson JK, Sholl DS.
    Langmuir; 2008 Aug 05; 24(15):8254-61. PubMed ID: 18613712
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  • 9. Efficiently Exploring Adsorption Space to Identify Privileged Adsorbents for Chemical Separations of a Diverse Set of Molecules.
    Tang D, Wu Y, Verploegh RJ, Sholl DS.
    ChemSusChem; 2018 May 09; 11(9):1567-1575. PubMed ID: 29624911
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  • 10. Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model.
    Anderson R, Biong A, Gómez-Gualdrón DA.
    J Chem Theory Comput; 2020 Feb 11; 16(2):1271-1283. PubMed ID: 31922755
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  • 16. Data Driven Discovery of MOFs for Hydrogen Gas Adsorption.
    Singh SK, Sose AT, Wang F, Bejagam KK, Deshmukh SA.
    J Chem Theory Comput; 2023 Oct 10; 19(19):6686-6703. PubMed ID: 37756641
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  • 17. Machine Learning Models for Predicting Molecular Diffusion in Metal-Organic Frameworks Accounting for the Impact of Framework Flexibility.
    Yang Y, Yu Z, Sholl DS.
    Chem Mater; 2023 Dec 12; 35(23):10156-10168. PubMed ID: 38107189
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  • 18. XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr.
    Liang H, Jiang K, Yan TA, Chen GH.
    ACS Omega; 2021 Apr 06; 6(13):9066-9076. PubMed ID: 33842776
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  • 19. Accelerated screening and assembly of promising MOFs with open Cu sites for isobutene/isobutane separation using a data-driven approach.
    Sun X, Lin W, Jiang K, Liang H, Chen G.
    Phys Chem Chem Phys; 2023 Mar 22; 25(12):8608-8623. PubMed ID: 36891889
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  • 20. Evaluating CH4/N2 Separation Performances of Hundreds of Thousands of Real and Hypothetical MOFs by Harnessing Molecular Modeling and Machine Learning.
    Gulbalkan HC, Uzun A, Keskin S.
    ACS Appl Mater Interfaces; 2023 Dec 11. PubMed ID: 38082488
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