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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 487 records · Page 27

Materials Data on Pt(SCl4)2 by Materials Project

PtCl4(SCl2)2 crystallizes in the monoclinic P2_1/c space group. The structure is zero-dimensional and consists of two platinum(iv) chloride molecules and four sulfur dichloride molecules.

36 MATERIALS SCIENCE↗

Materials Data on Pt(SCl3)2 by Materials Project

PtCl2(SCl2)2 is Cyanogen Chloride-derived structured and crystallizes in the triclinic P-1 space group. The structure is zero-dimensional and consists of one platinum(ii) chloride molecule and two sulfur dichloride molecules.

36 MATERIALS SCIENCE↗

Materials Data on Pt(NCl)2 by Materials Project

PtN4PtCl4 crystallizes in the monoclinic Pm space group. The structure is zero-dimensional and consists of two platinum(iv) chloride molecules and two tetraaminoplatinum molecules.

36 MATERIALS SCIENCE↗

Materials Data on Pt(NO)6 by Materials Project

PtN2N2(NO3)2 crystallizes in the monoclinic C2/c space group. The structure is zero-dimensional and consists of sixteen ammonia molecules, eight cis-platinum-(nh3)2 molecules, and sixteen nitric acid molecules.

36 MATERIALS SCIENCE↗

Materials Data on Pt(N2O3)2 by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗

Electrochemical Degradation of Pt 3 Co Nanoparticles Investigated by Off-Lattice Kinetic Monte Carlo Simulations with Machine-Learned Potentials

In fuel cell applications, the durability of catalysts is critical for large-scale industrial implementation. However, limited synthesis controllability and spectroscopic resolution impede a comprehensive understanding of degradation mechanisms at the atomic level. In this study, we develop a machine-learned potential (MLP) to simulate the degradation processes for Pt3Co nanoparticles. The precision of MLP is determined to be comparable to that of density functional theory calculations. Using off-lattice kinetic Monte Carlo simulations with MLP, we successfully replicate established experimental trends and offer a logical resolution to ongoing debates regarding atomic orderings. Based on the simulation results, we suggest design principles for Pt3Co nanoparticles that combine high activity and durability. Finally, we validate the wide applicability of our method by successfully applying it to Pt3Ni and Pt3Co0.5Ni0.5 nanoparticles. The research serves as a guideline for developing MLPs for alloy electrochemical catalysts and lays the foundation for designing more durable and active fuel-cell catalysts.

36 MATERIALS SCIENCE↗