Metal hexafluorides hydrolyze at ambient temperature to deposit compounds having fluorine-to-oxygen ratios that depend upon the identity of the metal. Uranium-hexafluoride hydrolysis, for example, deposits uranyl fluoride (UO2F2), whereas molybdenum hexafluoride (MoF6) and tungsten hexafluoride deposit trioxides. Here, we pursue general strategies enabling the prediction of depositing compounds resulting from multi-step gas-phase reactions. To compare among the three metal-hexafluoride hydrolyses, we first investigate the mechanism of MoF6 hydrolysis using hybrid density functional theory (DFT). Intermediates are then validated by performing anharmonic vibrational simulations and comparing with infrared spectra [McNamara et al., Phys. Chem. Chem. Phys. 25, 2990 (2023)]. Conceptual DFT, which is leveraged here to quantitatively evaluate site-specific electrophilicity and nucleophilicity metrics, is found to reliably predict qualitative deposition propensities for each intermediate. In addition to the nucleophilic potential of the oxygen ligands, several other contributing characteristics are discussed, including amphoterism, polyvalency, fluxionality, steric hindrance, dipolar strength, and solubility. To investigate the structure and composition of pre-nucleation clusters, an automated workflow is presented for the simulation of particle growth. The workflow entails a conformer search at the density functional tight-binding level, structural refinement at the hybrid DFT level, and computation of a composite free-energy profile. Such profiles can be used to estimate particle nucleation kinetics. Droplet formation is also considered, which helps to rationalize the different UO2F2 particle morphologies observed under varying levels of humidity. Development of predictive methods for simulating physical and chemical deposition processes is important for the advancement of material manufacturing involving coatings and thin films.
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http://dx.doi.org/10.1063/5.0176552 | DOI Listing |
Sci Technol Adv Mater
December 2024
JST-CREST, Saitama, Japan.
In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Technology Agency. We focus on the constituents of polycrystalline materials (i.e.
View Article and Find Full Text PDFJ Mol Model
December 2024
Physics Education Department, Faculty of Education, Tishk International University, 44001, Erbil, Kurdistan Region, Iraq.
Context: This research investigates two critical areas, providing valuable insights into the properties and interactions of boron nitride nanotubes (BNNTs). Initially, a variety of BNNT structures (BNNT(m,n)_x, where m = 3, 5, 7; n = 0, 3, 5, 7; x = 3-9) with different lengths and diameters are explored to understand their electronic properties. The study then examines the interactions between these nanotubes and several gases (CO, CO, CSO, HO, NO, NO, NO, O, ONH, and SO) to identify the most stable molecular configurations using the bee colony algorithm for global optimization.
View Article and Find Full Text PDFPhys Chem Chem Phys
January 2025
Engineering Research Center of Metallurgical Energy Conservation and Emission Reduction, Ministry of Education, Kunming University of Science and Technology, Kunming 650093, China.
ACS Org Inorg Au
December 2024
Department of Chemistry, Vanderbilt University, PO Box 1822 Nashville, Tennessee 37235, United States.
Metal complexes with -Bu-substituted allyl ligands are relatively rare, especially compared to their conceptually similar trimethylsilyl-substituted analogs. The scarcity partially stems from the few general synthetic entry points for the -Bu versions. This situation was studied through a modified synthesis for the allyl ligand itself and by forming several mono(allyl)nickel derivatives.
View Article and Find Full Text PDFJ Inorg Biochem
March 2025
Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, 07100 Sassari, Italy; Department of Physics and Astronomy, University of Padova, Via F. Marzolo 8, 35131 Padova, Italy.
It is a challenging task to develop uranyl-chelating agents based on peptide chemistry. A recently developed cationic dummy atom model of uranyl in conjunction with the classical molecular dynamics simulation presents a helpful utility to study the chelation of uranyl by peptides with a low computational cost. In the present study, it was used to describe the chelation of uranyl by the cyclic decapeptide with 4 Glu residues cyc-GluArgGluProGlyGluTrpGluProGly and its derivatives containing two phosphorylated serines in place of two Glu, termed pS16, pS18, pS38, and pS68.
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