The hypothesis that bioactive glass particulate increases the rate of bone proliferation over that of synthetic hydroxyapatite and bioactive glass-ceramic was tested in these experiments. Three types of bioactive particles-45S5 Bioglass(R), synthetic hydroxyapatite, and A-W glass-ceramic-were implanted in 6-mm-diameter holes drilled in the femoral condyles of mature rabbits. Bone growth rate was measured using an image processor. 45S5 Bioglass(R) produced bone more rapidly than either A-W glass-ceramic or hydroxyapatite. At the later time periods, 45S5 Bioglass(R) was resorbed more quickly than A-W glass-ceramic. Synthetic hydroxyapatite was not resorbed at all. Backscattered electron imaging suggested that the resorption process occurred by solution-mediated dissolution, which produced chemical changes in the enclosed particulate. It was concluded that the rate of bone growth correlates with the rate of dissolution of silica as the particles resorb.

A public data-analytics competition was organized by the Novel Materials Discovery (NOMAD) Centre of Excellence and hosted by the online platform Kaggle by using a dataset of 3,000 (Al x Ga y In 1-x-y) 2 O 3 compounds. Its aim was to identify the best machinelearning (ML) model for the prediction of two key physical properties that are relevant for optoelectronic applications: the electronic bandgap energy and the crystalline formation energy. Here, we present a summary of the top-three ranked ML approaches. The first-place solution was based on a crystal-graph representation that is novel for the ML of properties of materials. The second-place model combined many candidate descriptors from a set of compositional, atomic-environment-based, and average structural properties with the light gradient-boosting machine regression model. The third-place model employed the smooth overlap of atomic position representation with a neural network. The Pearson correlation among the prediction errors of nine ML models (obtained by combining the top-three ranked representations with all three employed regression models) was examined by using the Pearson correlation to gain insight into whether the representation or the regression model determines the overall model performance. Ensembling relatively decorrelated models (based on the Pearson correlation) leads to an even higher prediction accuracy.

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We present a theoretical study on electron and hole trap states in the bulk and (001) surface of anatase titanium dioxide using screened hybrid density functional calculations. In both the bulk and surface, calculations suggest that the neutral and ionized oxygen vacancies are possible electron traps. The doubly ionized oxygen vacancy is the most stable in the bulk, and is a candidate for a shallow donor in colorless anatase crystals. The hole trap states are localized at oxygen anions in both the bulk and surface. The self-trapped electron centered at a titanium cation cannot be produced in the bulk, but can be formed at the surface. The electron trap level at the surface oxygen vacancy is consistent with observations by photoelectron spectroscopy. The optical absorptions and luminescence in UV-irradiated anatase nanoparticles are found to come from the surface self-trapped hole and the surface oxygen vacancy.

Thermal decomposition of hydrazoic acid HN3 diluted to less than 0.5 mol % in argon was studied behind incident shock waves in the argon pressure range 600-2200 torr at temperatures between 1200 and 1350 K. The course of decomposition was followed by monitoring the absorptions of HN3 and NH(32') at 206 and 336 nm, respectively. Analysis of the initial portion of the HN3 absorption has shown that the primary decomposition step is first order in both HN3 and Ar, with the apparent second-order rate constant ftapp = io14-88±a2s exp[-(36.2 ± 1.6) kcal mo\~ljRT] cm3 moT1 s'1. In the temperature range studied, the reaction HN3 + Ar -* NH(S2") + N2 + Ar, AH°0 = 17.5 kcal/mol, is dominant over the concurrent process, HN3 + Ar -NH(XA) + N2 + Ar, AH°0 = 53.6 kcal/mol. RRKM calculations suggest that the former pathway should have a singlet-triplet crossing probability of 10"s-10"2. The overall mechanism of decomposition was inferred, and its validity was confirmed by computer integration of a set of relevant rate equations.

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Ge x Si 1−x are characterized by Raman microspectroscopy. The strain of the 17.5-nm-thick Si layer was examined through deep UV Raman measurements. The depth profile of the GexSi1−x alloy composition and crystallinity was determined by visible Raman image measurement of the sample cross section. These measurements give results consistent with transmission electron microscopy and secondary ion mass spectrometry analyses.

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