180 related articles for article (PubMed ID: 35877903)
1. Large-Scale Screening and Machine Learning for Metal-Organic Framework Membranes to Capture CO
Situ Y; Yuan X; Bai X; Li S; Liang H; Zhu X; Wang B; Qiao Z
Membranes (Basel); 2022 Jul; 12(7):. PubMed ID: 35877903
[TBL] [Abstract][Full Text] [Related]
2. High-Throughput Screening of the CoRE-MOF-2019 Database for CO
Kancharlapalli S; Snurr RQ
ACS Appl Mater Interfaces; 2023 Jun; 15(23):28084-28092. PubMed ID: 37262369
[TBL] [Abstract][Full Text] [Related]
3. Computational Screening of Metal-Organic Frameworks for Membrane-Based CO
Daglar H; Keskin S
J Phys Chem C Nanomater Interfaces; 2018 Aug; 122(30):17347-17357. PubMed ID: 30093931
[TBL] [Abstract][Full Text] [Related]
4. Combining Computational Screening and Machine Learning to Predict Metal-Organic Framework Adsorbents and Membranes for Removing CH
Li H; Wang C; Zeng Y; Li D; Yan Y; Zhu X; Qiao Z
Membranes (Basel); 2022 Aug; 12(9):. PubMed ID: 36135849
[TBL] [Abstract][Full Text] [Related]
5. 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; 14(28):32134-32148. PubMed ID: 35818710
[TBL] [Abstract][Full Text] [Related]
6. Computational Screening of Metal⁻Organic Framework Membranes for the Separation of 15 Gas Mixtures.
Yang W; Liang H; Peng F; Liu Z; Liu J; Qiao Z
Nanomaterials (Basel); 2019 Mar; 9(3):. PubMed ID: 30897779
[TBL] [Abstract][Full Text] [Related]
7. Deep learning and big data mining for Metal-Organic frameworks with high performance for simultaneous desulfurization and carbon capture.
Guan K; Xu F; Huang X; Li Y; Guo S; Situ Y; Chen Y; Hu J; Liu Z; Liang H; Zhu X; Wu Y; Qiao Z
J Colloid Interface Sci; 2024 May; 662():941-952. PubMed ID: 38382377
[TBL] [Abstract][Full Text] [Related]
8. Multi-Scale Computer-Aided Design of Covalent Organic Frameworks for CO
Yang S; Zhu W; Zhu L; Ma X; Yan T; Gu N; Lan Y; Huang Y; Yuan M; Tong M
ACS Appl Mater Interfaces; 2022 Dec; 14(50):56353-56362. PubMed ID: 36511382
[TBL] [Abstract][Full Text] [Related]
9. High-Throughput Screening of MOF Adsorbents and Membranes for H
Avci G; Velioglu S; Keskin S
ACS Appl Mater Interfaces; 2018 Oct; 10(39):33693-33706. PubMed ID: 30193065
[TBL] [Abstract][Full Text] [Related]
10. Dual-Channel, Molecular-Sieving Core/Shell ZIF@MOF Architectures as Engineered Fillers in Hybrid Membranes for Highly Selective CO
Song Z; Qiu F; Zaia EW; Wang Z; Kunz M; Guo J; Brady M; Mi B; Urban JJ
Nano Lett; 2017 Nov; 17(11):6752-6758. PubMed ID: 29072837
[TBL] [Abstract][Full Text] [Related]
11. 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; 6(13):9066-9076. PubMed ID: 33842776
[TBL] [Abstract][Full Text] [Related]
12. Large-Scale Computational Screening of Metal Organic Framework (MOF) Membranes and MOF-Based Polymer Membranes for H
Azar ANV; Velioglu S; Keskin S
ACS Sustain Chem Eng; 2019 May; 7(10):9525-9536. PubMed ID: 31157127
[TBL] [Abstract][Full Text] [Related]
13. Selective Adsorption-Based Separation of Flue Gas and Natural Gas in Zirconium Metal-Organic Frameworks Nanocrystals.
Li P; Shen Y; Wang D; Chen Y; Zhao Y
Molecules; 2019 May; 24(9):. PubMed ID: 31083563
[TBL] [Abstract][Full Text] [Related]
14. Synergistic enhancement of CO
Katare A; Sikha S; Mandal B
Environ Sci Pollut Res Int; 2024 Apr; ():. PubMed ID: 38561537
[TBL] [Abstract][Full Text] [Related]
15. Molecular Simulations of MOF Membranes and Performance Predictions of MOF/Polymer Mixed Matrix Membranes for CO
Altintas C; Keskin S
ACS Sustain Chem Eng; 2019 Jan; 7(2):2739-2750. PubMed ID: 30701144
[TBL] [Abstract][Full Text] [Related]
16. Do New MOFs Perform Better for CO
Avci G; Erucar I; Keskin S
ACS Appl Mater Interfaces; 2020 Sep; 12(37):41567-41579. PubMed ID: 32818375
[TBL] [Abstract][Full Text] [Related]
17. Simulation of the Membrane Process of CO
Miroshnichenko D; Shalygin M; Bazhenov S
Membranes (Basel); 2023 Jul; 13(8):. PubMed ID: 37623753
[TBL] [Abstract][Full Text] [Related]
18. Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BF
Daglar H; Gulbalkan HC; Habib N; Durak O; Uzun A; Keskin S
ACS Appl Mater Interfaces; 2023 Apr; 15(13):17421-17431. PubMed ID: 36972354
[TBL] [Abstract][Full Text] [Related]
19. Data-driven design of metal-organic frameworks for wet flue gas CO
Boyd PG; Chidambaram A; García-Díez E; Ireland CP; Daff TD; Bounds R; Gładysiak A; Schouwink P; Moosavi SM; Maroto-Valer MM; Reimer JA; Navarro JAR; Woo TK; Garcia S; Stylianou KC; Smit B
Nature; 2019 Dec; 576(7786):253-256. PubMed ID: 31827290
[TBL] [Abstract][Full Text] [Related]
20. Revealing the structure-property relationships of metal-organic frameworks for CO2 capture from flue gas.
Wu D; Yang Q; Zhong C; Liu D; Huang H; Zhang W; Maurin G
Langmuir; 2012 Aug; 28(33):12094-9. PubMed ID: 22827840
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
