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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
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
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
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
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
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
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
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
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
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
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
Comprehensive molecular dynamics tensile test simulations have been performed to study the delamination processes of seven different grain boundaries / cleavage planes (Σ1{111}, Σ3{111}, Σ5{100}, Σ7{111}, Σ9{411}, Σ11{311}, and R{100}/{411}) containing a helium bubble. Combinations of a variety of conditions are explored including different strain rates, system dimensions, bubble density, bubble radius, bubble pressure, and temperature. We found that in general, grain boundaries absorb less energies with decreasing strain rate but increasing bubble areal density, bubble pressure, bubble radius, and temperature. The propensity of grain boundary delamination is sensitive to grain boundary type: The random grain boundary R{100}/{411} is one of the most brittle boundaries whereas the Σ1{111} cleavage plane and the Σ3{111} twin boundary are two of the toughest boundaries. The sorted list of grain boundary fracture vulnerability obtained from our dynamic tensile test simulations differs from the one obtained from our decohesion energy calculations, confirming the important role of plastic deformation during fracture. Finally, detailed mechanistic analyses are performed to interpret the simulated results.
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The growth of helium bubbles impacts structural integrity of materials in nuclear applications.
A nanocrystalline chromium-doped ferrite (FeCr) catalyst was shown to coproduce H2 and multiwalled carbon nanotubes (MWCNTs) during water gas shift (WGS) reaction in a H2-permselective zeolite membrane reactor (MR) at reaction pressures of ~20 bar. The FeCr catalyst was further demonstrated in the synthesis of highly crystalline and dimensionally uniform MWCNTs from a dry gas mixture of CO and CH4, which were the apparent sources for MWCNT growth in the WGS MR. In both the WGS MR and dry gas reactions, the operating temperature was 500 °C, which is significantly lower than those commonly used in MWCNT production by chemical vapor deposition (CVD) method from CO, CH4, or any other precursor gases. Extensive ex situ characterizations of the reaction products revealed that the FeCr catalyst remained in partially reduced states of Fe3+/Fe2+ and Cr6+/Cr3+ in WGS membrane reaction while further reduction of Fe2+ to Fe0 occurred in the CO/CH4 dry gas environments. The formation of the metallic Fe nanoparticles or catalyst surface dramatically improved the crystallinity and dimensional uniformity of the MWCNTs from dry gas reaction as compared to that from WGS reaction in the MR. Reaction of the CO/CH4 mixture containing 500 ppmv H2S also resulted in high-quality MWCNTs similar to those from the H2S-free feed gas, demonstrating excellent sulfur tolerance of the FeCr catalyst that is practically meaningful for utilization of biogas and cheap coal-derived syngas.
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