Understanding hydrogen diffusion mechanisms in doped α-Fe through DFT calculations

Hydrogen embrittlement is detrimental to structural metals during applications. Herein, we explore the hydrogen diffusion mechanisms in doped α-Fe using first-principles calculations. We prove that the hydrogen trap is a thermodynamically spontaneous process, and doping will decrease the hydrogen adsorption energy due to the change of adsorption sites. Furthermore, hydrogen diffusion from surface to subsurface will determine the diffusion rate. Mo, Mn and C are beneficial to the increase of the energy barrier of hydrogen diffusion from the surface to subsurface and in the bulk. The current work provides a promising path towards enhancing the hydrogen diffusion barrier in α-Fe.     

Achieving a thermally stable Eu2+-doped Sr2Si5N8 phosphor via adjusting the lattice defects of oxygen atoms and protection by the graphene-like multilayer

Sr₂Si₅N₈:Eu²⁺ (258) phosphor has attracted much attention due to its excellent red emission properties. However, its poor thermal stability hinders its further development in electronic display devices. Therefore, it is important to solve this problem in order to produce stable and long life-time WLED devices. In this work, a 258 phosphor was synthesized by a simple solid-phase reaction method, which was then coated by an ultrathin carbon layer by chemical vapor deposition. The carbon-coated powders were further annealed in N₂ atmosphere at a high temperature to trigger the carbothermal reaction. On the one hand, the carbothermal reaction resulted in the removal of oxygen in the phosphor particle by a part of the carbon on the surface, which reduced the oxidation of both the luminescent centers and the host lattice. On the other hand, the remaining carbon crystallized, forming a graphene-like multilayer that protected the phosphor particle from the penetration of the external oxygen. This eventually resulted in a significant improvement of the thermal stability and oxidation resistance of the phosphor.     

Predicting oxidation damage of ultra high-temperature carbide ceramics in extreme environments using machine learning

Determining the oxidation resistance of UHTC carbides in extreme environments is challenging theoretically and experimentally due to the high dimensional complexity of influencing variables and intricate testing setups. Herein we demonstrate the use of machine learning (ML) models trained with experimental literature data to predict the oxide thickness of UHTC carbides exposed to air based on composition, mean grain size, relative densification, holding time, and temperature. A multi-dimensional database with 76 occurrences is created containing experimental results of Hf, Zr, and Ta carbides plus additives. The preprocessed database is then used to train ML models to predict their oxidation behavior. The trained model predicts the oxidation damage in the form of an average oxide thickness in UHTC carbides with a Mean Absolute Error (MAE) of ±65.45 μm for samples in the testing set that developed thicknesses up to 1000 μm. The model successfully predicted oxidation damage for a recession rate lower than 60 μm/min. It is noticed that the ensemble method MAE is increased to ±134.34 μm while forecasting the oxidation of samples with a recession rate higher than the threshold. The unprecedented approach is a novel way to predict the damage through the oxidation of carbide compounds before processing for a smarter design with room for improvement.     

Ameliorative effect of the oil components derived from sweet potato shochu on impaired short-term memory in mice after amyloid β25-35 injection

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