Ensemble Learning of Partition Functions for the Prediction of Thermodynamic Properties of Adsorption in Metal–Organic and Covalent Organic Frameworks-Reference-Cited by-同舟云学术

Ensemble Learning of Partition Functions for the Prediction of Thermodynamic Properties of Adsorption in Metal–Organic and Covalent Organic Frameworks

Published:2019-12-31 Issue:3 Volume:124 Page:1907-1917
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Desgranges Caroline¹²^ORCID,Delhommelle Jerome¹²^ORCID

Affiliation:

1. Department of Chemistry, New York University, New York, New York 10003, United States

2. Department of Chemistry, University of North Dakota, Grand Forks, North Dakota 58202, United States

Publisher

American Chemical Society (ACS)

Subject

Surfaces, Coatings and Films,Physical and Theoretical Chemistry,General Energy,Electronic, Optical and Magnetic Materials

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.9b07936

Reference80 articles.

1. Design and synthesis of an exceptionally stable and highly porous metal-organic framework

2. Systematic Design of Pore Size and Functionality in Isoreticular MOFs and Their Application in Methane Storage

3. Storage of Hydrogen, Methane, and Carbon Dioxide in Highly Porous Covalent Organic Frameworks for Clean Energy Applications

4. Assessment of a Metal−Organic Framework Membrane for Gas Separations Using Atomically Detailed Calculations: CO2, CH4, N2, H2 Mixtures in MOF-5

5. Unprecedentedly High Selective Adsorption of Gas Mixtures in rho Zeolite-like Metal−Organic Framework: A Molecular Simulation Study

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