predictive hierarchical modeling of chemical separations and transformations in functional nanoporous materials: synergy of electronic structure theory, molecular simulation, machine learning, and experiment
September 17, 2020 | The American Chemical Society (ACS) has named Ilja Siepmann editor-in-chief of the Journal of Chemical & Engineering Data.
August 19, 2020 | Dr. Coray Colina of the University of Florida has received Fulbright U.S. Scholar Program award to Mexico.
July 24, 2020 | Jenny Vitillo received a poster competition award for her poster “H-bonds and redox reactions make Co2(OH)2BBTA the first extended framework with a negative cooperativity behavior for O2” at the Porous Materials Group Poster Conference organized on Twitter by the Royal Society of Chemistry (July 23-24, 2020). The research presented in the poster is the result of a collaboration between the groups of Jeff Long and Laura Gagliardi.
MOFs can mimic biological systems in the way they interact with molecular oxygen. Drawing inspiration from biological O2 carriers, hydroxo species have been introduced in the Co(OH)2(BBTA) MOF to stabilize cobalt(III)-superoxo species by hydrogen bonding. Additionally, O2-binding weakens in this material as a function of loading, a property called negative cooperativity. This property is typical of enzymes, but it had never been observed in extended framework materials before this study. This unprecedented behavior extends the tunable properties that can be used to design metal–organic frameworks for adsorption-based applications.
The revM11 functional is an improved version of the range-separated parameterization originally used in the M11 functional to obtain a parametrization.
Researchers developed a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules.
This research is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362 (Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Electronic Structure Theory, Molecular Simulations, Machine Learning, and Experiment) and was previously supported by DE-FG02-12ER16362 (Nanoporous Materials Genome: Methods and Software to Optimize Gas Storage, Separations, and Catalysis).
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