BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

169 related articles for article (PubMed ID: 35524582)

  • 1. Discovery of High-Performing Metal-Organic Frameworks for On-Board Methane Storage and Delivery via LNG-ANG Coupling: High-Throughput Screening, Machine Learning, and Experimental Validation.
    Kim SY; Han S; Lee S; Kang JH; Yoon S; Park W; Shin MW; Kim J; Chung YG; Bae YS
    Adv Sci (Weinh); 2022 Jul; 9(21):e2201559. PubMed ID: 35524582
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Accelerating Discovery of Metal-Organic Frameworks for Methane Adsorption with Hierarchical Screening and Deep Learning.
    Wang R; Zhong Y; Bi L; Yang M; Xu D
    ACS Appl Mater Interfaces; 2020 Nov; 12(47):52797-52807. PubMed ID: 33175490
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation.
    Altintas C; Altundal OF; Keskin S; Yildirim R
    J Chem Inf Model; 2021 May; 61(5):2131-2146. PubMed ID: 33914526
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Adsorption of Natural Gas in Metal-Organic Frameworks: Selectivity, Cyclability, and Comparison to Methane Adsorption.
    Nath K; Wright KR; Ahmed A; Siegel DJ; Matzger AJ
    J Am Chem Soc; 2024 Apr; 146(15):10517-10523. PubMed ID: 38569048
    [TBL] [Abstract][Full Text] [Related]  

  • 5. In silico design of porous polymer networks: high-throughput screening for methane storage materials.
    Martin RL; Simon CM; Smit B; Haranczyk M
    J Am Chem Soc; 2014 Apr; 136(13):5006-22. PubMed ID: 24611543
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Computational Screening of Trillions of Metal-Organic Frameworks for High-Performance Methane Storage.
    Lee S; Kim B; Cho H; Lee H; Lee SY; Cho ES; Kim J
    ACS Appl Mater Interfaces; 2021 May; 13(20):23647-23654. PubMed ID: 33988362
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Computational prediction of high methane storage capacity in V-MOF-74.
    Hyeon S; Kim YC; Kim J
    Phys Chem Chem Phys; 2017 Aug; 19(31):21132-21139. PubMed ID: 28749516
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A Self-Evolutionary Methodology for Reverse Design of Novel MOFs.
    Yan T; Bi Z; Liu D; Zhang X; Lu G; Yang Q
    J Phys Chem A; 2022 Nov; 126(45):8476-8486. PubMed ID: 36343215
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs).
    Pardakhti M; Moharreri E; Wanik D; Suib SL; Srivastava R
    ACS Comb Sci; 2017 Oct; 19(10):640-645. PubMed ID: 28800219
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Insights into hydrogen and methane storage capacities: Grand canonical Monte Carlo simulations of SIGSUA.
    Granja-DelRío A; Cabria I
    J Chem Phys; 2024 Apr; 160(15):. PubMed ID: 38634495
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Heat and Mass Transfer in an Adsorbed Natural Gas Storage System Filled with Monolithic Carbon Adsorbent during Circulating Gas Charging.
    Strizhenov EM; Chugaev SS; Men'shchikov IE; Shkolin AV; Zherdev AA
    Nanomaterials (Basel); 2021 Dec; 11(12):. PubMed ID: 34947623
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Machine learning insights into predicting biogas separation in metal-organic frameworks.
    Cooley I; Boobier S; Hirst JD; Besley E
    Commun Chem; 2024 May; 7(1):102. PubMed ID: 38720065
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Ligand Tailoring Strategy of a Metal-Organic Framework for Optimizing Methane Storage Working Capacities.
    Chen JR; Luo YQ; He S; Zhou HL; Huang XC
    Inorg Chem; 2022 Jul; 61(27):10417-10424. PubMed ID: 35767723
    [TBL] [Abstract][Full Text] [Related]  

  • 14. In Silico Evolution of High-Performing Metal Organic Frameworks for Methane Adsorption.
    Beauregard N; Pardakhti M; Srivastava R
    J Chem Inf Model; 2021 Jul; 61(7):3232-3239. PubMed ID: 34264660
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Design of covalent organic frameworks for methane storage.
    Mendoza-Cortes JL; Pascal TA; Goddard WA
    J Phys Chem A; 2011 Dec; 115(47):13852-7. PubMed ID: 21992457
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Tuning Open Metal Site-Free
    Zhang ZH; Fang H; Xue DX; Bai J
    ACS Appl Mater Interfaces; 2021 Sep; 13(37):44956-44963. PubMed ID: 34498839
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Reticular Chemistry for Highly Porous Metal-Organic Frameworks: The Chemistry and Applications.
    Chen Z; Kirlikovali KO; Li P; Farha OK
    Acc Chem Res; 2022 Feb; 55(4):579-591. PubMed ID: 35112832
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Using Low-Pressure Methane Adsorption Isotherms for Higher-Throughput Screening of Methane Storage Materials.
    Korman KJ; Decker GE; Dworzak MR; Deegan MM; Antonio AM; Taggart GA; Bloch ED
    ACS Appl Mater Interfaces; 2020 Sep; 12(36):40318-40327. PubMed ID: 32786240
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Screening metal-organic frameworks for adsorption-driven osmotic heat engines via grand canonical Monte Carlo simulations and machine learning.
    Long R; Xia X; Zhao Y; Li S; Liu Z; Liu W
    iScience; 2021 Jan; 24(1):101914. PubMed ID: 33385115
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Computational Identification and Experimental Demonstration of High-Performance Methane Sorbents.
    Nath K; Ahmed A; Siegel DJ; Matzger AJ
    Angew Chem Int Ed Engl; 2022 Jun; 61(25):e202203575. PubMed ID: 35478372
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.