Monte Carlo Simulations of Water Adsorption in Aluminum Oxide Rod-Based Metal–Organic Frameworks
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Engineering topics
Jeong, WooSeok
Publications and source records attributed to Jeong, WooSeok.
Leveraging Nitrogen Linkages in the Formation of a Porous Thorium–Organic Nanotube Suitable for Iodine Capture
We report the synthesis, characterization, and iodine capture application of a novel thorium–organic nanotube, TSN-626, [Th 6 O 4 (OH) 4 (C 6 H 4 NO 2 ) 7 (CHO 2 ) 5 (H 2 O) 3 ]·3H 2 O. The classification as a metal–organic nanotube (MONT) distinguishes it as a rare and reduced dimensionality subset of metal–organic frameworks (MOFs); the structure is additionally hallmarked by low node connectivity. TSN-626 is composed of hexameric thorium secondary building units and mixed O/N-donor isonicotinate ligands that demonstrate selective ditopicity, yielding both terminating and bridging moieties. Because hard Lewis acid tetravalent metals have a propensity to bind with electron donors of rival hardness (e.g., carboxylate groups), such Th–N coordination in a MOF is uncommon. However, the formation of key structural Th–N bonds in TSN-626 cap some of the square antiprismatic metal centers, a position usually occupied by terminal water ligands. TSN-626 was characterized by using complementary analytical and computational techniques: X-ray diffraction, vibrational spectroscopy, N 2 physisorption isotherms, and density functional theory. TSN-626 satisfies design aspects for the chemisorption of iodine. The synergy between accessibility through pores, vacancies at the metal–oxo nodes, and pendent N-donor sites allowed a saturated iodine loading of 955 mg g –1 by vapor methods. Furthermore, the crystallization of TSN-626 diversifies actinide–MOF linker selection to include soft electron donors, and these Th–N linkages can be leveraged for the investigation of metal-to-ligand bonding and unconventional topological expressions.
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Active Learning Configuration Interaction for Excited-State Calculations of Polycyclic Aromatic Hydrocarbons
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Evolution of water structures in metal-organic frameworks for improved atmospheric water harvesting
Designing water uptake Although the locations of water molecules in some porous materials have been determined with diffraction techniques, determining the filling sequence of water sites has been challenging. Hanikel et al . used single-crystal x-ray diffraction to locate all of the water molecules in pores of the metal-organic framework MOF-303 at different water loadings (see the Perspective by Öhrström and Amombo Noa). They used this information on the water molecule adsorption sequence to modify the linkers of this MOF and control the water-harvesting properties from humid air for different temperature regimes. —PDS
Science & Technology - Other Topics↗
Advancement of Actinide Metal–Organic Framework Chemistry via Synthesis of Pu-UiO-66
We report the synthesis and characterization of the first plutonium metal–organic framework (MOF). Pu-UiO-66 expands the established UiO-66 series, which includes transition metal, lanthanide, and early actinide elements in the hexanuclear nodes. The thermal stability and porosity of Pu-UiO-66 were experimentally determined, and multifaceted computational methods were used to corroborate experimental values, examine inherent defects in the framework, decipher spectroscopic signatures, and elucidate the electronic structure. The crystallization of a plutonium chain side product provides direct evidence of the competition that occurs between modulator and linker in MOF syntheses. Ultimately, the synthesis of Pu-UiO-66 demonstrates adept control of Pu(IV) coordination under hydrolysis-prone conditions, provides an opportunity to extend trends across isostructural UiO-66 frameworks, and serves as the foundation for future plutonium MOF chemistry.
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Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation
Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn–Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structures and chemical bonds of such systems. In the complete active space self-consistent field (CASSCF) method, one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. Here, we present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a “black-box” mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.
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