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Journal Abstract Search

191 related items for PubMed ID: 38082488

  • 1. 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
    [Abstract] [Full Text] [Related]

  • 2. Advancing CH4/H2 separation with covalent organic frameworks by combining molecular simulations and machine learning.
    Aksu GO, Keskin S.
    J Mater Chem A Mater; 2023 Jul 11; 11(27):14788-14799. PubMed ID: 37441278
    [Abstract] [Full Text] [Related]

  • 3. Database for CO2 Separation Performances of MOFs Based on Computational Materials Screening.
    Altintas C, Avci G, Daglar H, Nemati Vesali Azar A, Velioglu S, Erucar I, Keskin S.
    ACS Appl Mater Interfaces; 2018 May 23; 10(20):17257-17268. PubMed ID: 29722965
    [Abstract] [Full Text] [Related]

  • 4. Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs.
    Daglar H, Keskin S.
    ACS Appl Mater Interfaces; 2022 Jul 20; 14(28):32134-32148. PubMed ID: 35818710
    [Abstract] [Full Text] [Related]

  • 5. Unlocking the Effect of H2O on CO2 Separation Performance of Promising MOFs Using Atomically Detailed Simulations.
    Erucar I, Keskin S.
    Ind Eng Chem Res; 2020 Feb 19; 59(7):3141-3152. PubMed ID: 32201455
    [Abstract] [Full Text] [Related]

  • 6. High-throughput computational screening of MOF adsorbents for efficient propane capture from air and natural gas mixtures.
    Ercakir G, Aksu GO, Keskin S.
    J Chem Phys; 2024 Feb 28; 160(8):. PubMed ID: 38415834
    [Abstract] [Full Text] [Related]

  • 7. Zr-MOFs for CF4/CH4, CH4/H2, and CH4/N2 separation: towards the goal of discovering stable and effective adsorbents.
    Demir H, Keskin S.
    Mol Syst Des Eng; 2021 Aug 02; 6(8):627-642. PubMed ID: 34381619
    [Abstract] [Full Text] [Related]

  • 8. Rapid and Accurate Screening of the COF Space for Natural Gas Purification: COFInformatics.
    Aksu GO, Keskin S.
    ACS Appl Mater Interfaces; 2024 Apr 17; 16(15):19806-19818. PubMed ID: 38588323
    [Abstract] [Full Text] [Related]

  • 9. Computational investigation of multifunctional MOFs for adsorption and membrane-based separation of CF4/CH4, CH4/H2, CH4/N2, and N2/H2 mixtures.
    Demir H, Keskin S.
    Mol Syst Des Eng; 2022 Nov 28; 7(12):1707-1721. PubMed ID: 36561661
    [Abstract] [Full Text] [Related]

  • 10. High-Throughput Computational Screening of the Metal Organic Framework Database for CH4/H2 Separations.
    Altintas C, Erucar I, Keskin S.
    ACS Appl Mater Interfaces; 2018 Jan 31; 10(4):3668-3679. PubMed ID: 29313343
    [Abstract] [Full Text] [Related]

  • 11. Combined GCMC, MD, and DFT Approach for Unlocking the Performances of COFs for Methane Purification.
    Altundal OF, Haslak ZP, Keskin S.
    Ind Eng Chem Res; 2021 Sep 08; 60(35):12999-13012. PubMed ID: 34526735
    [Abstract] [Full Text] [Related]

  • 12. High-Throughput Screening of MOF Adsorbents and Membranes for H2 Purification and CO2 Capture.
    Avci G, Velioglu S, Keskin S.
    ACS Appl Mater Interfaces; 2018 Oct 03; 10(39):33693-33706. PubMed ID: 30193065
    [Abstract] [Full Text] [Related]

  • 13. Computational Screening of MOFs for Acetylene Separation.
    Nemati Vesali Azar A, Keskin S.
    Front Chem; 2018 Oct 03; 6():36. PubMed ID: 29536004
    [Abstract] [Full Text] [Related]

  • 14. Tuning the Pore Environment of MOFs toward Efficient CH4/N2 Separation under Humid Conditions.
    Li T, Jia X, Chen H, Chang Z, Li L, Wang Y, Li J.
    ACS Appl Mater Interfaces; 2022 Apr 06; 14(13):15830-15839. PubMed ID: 35319192
    [Abstract] [Full Text] [Related]

  • 15. High-throughput computational screening of hypothetical metal-organic frameworks with open copper sites for CO2/H2 separation.
    Li M, Cai W, Wang C, Wu X.
    Phys Chem Chem Phys; 2022 Aug 10; 24(31):18764-18776. PubMed ID: 35903942
    [Abstract] [Full Text] [Related]

  • 16. Effect of Metal-Organic Framework (MOF) Database Selection on the Assessment of Gas Storage and Separation Potentials of MOFs.
    Daglar H, Gulbalkan HC, Avci G, Aksu GO, Altundal OF, Altintas C, Erucar I, Keskin S.
    Angew Chem Int Ed Engl; 2021 Mar 29; 60(14):7828-7837. PubMed ID: 33443312
    [Abstract] [Full Text] [Related]

  • 17. Metal Exchange Boosts the CO2 Selectivity of Metal Organic Frameworks Having Zn-Oxide Nodes.
    Avci G, Altintas C, Keskin S.
    J Phys Chem C Nanomater Interfaces; 2021 Aug 12; 125(31):17311-17322. PubMed ID: 34413923
    [Abstract] [Full Text] [Related]

  • 18. Multi-Level Computational Screening of in Silico Designed MOFs for Efficient SO2 Capture.
    Demir H, Keskin S.
    J Phys Chem C Nanomater Interfaces; 2022 Jun 16; 126(23):9875-9888. PubMed ID: 35747510
    [Abstract] [Full Text] [Related]

  • 19. Data-Driven and Machine Learning to Screen Metal-Organic Frameworks for the Efficient Separation of Methane.
    Guan Y, Huang X, Xu F, Wang W, Li H, Gong L, Zhao Y, Guo S, Liang H, Qiao Z.
    Nanomaterials (Basel); 2024 Jun 24; 14(13):. PubMed ID: 38998680
    [Abstract] [Full Text] [Related]

  • 20. Effects of Force Field Selection on the Computational Ranking of MOFs for CO2 Separations.
    Dokur D, Keskin S.
    Ind Eng Chem Res; 2018 Feb 14; 57(6):2298-2309. PubMed ID: 29503503
    [Abstract] [Full Text] [Related]

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