Interface segregation of solute atoms has a profound effect on properties of engineering alloys. The occurrence of solute segregation in coherent twin boundaries (CTBs) in Mg alloys is commonly considered to be induced by atomic size effect where solute atoms larger than Mg take extension sites and those smaller ones take compression sites in CTBs. Here we report an unusual solute segregation phenomenon in a group of Mg alloys—solute atoms larger than Mg unexpectedly segregate to compression sites of {10$$\overline 1$$ 1 ¯ 1} fully coherent twin boundary and do not segregate to the extension or compression site of {10$$\overline 1$$ 1 ¯ 2} fully coherent twin boundary. We propose that such segregation is dominated by chemical bonding (coordination and solute electronic configuration) rather than elastic strain minimization. We further demonstrate that the chemical bonding factor can also predict the solute segregation phenomena reported previously. Our findings advance the atomic-level understanding of the role of electronic structure in solute segregation in fully coherent twin boundaries, and more broadly grain boundaries, in Mg alloys. They are likely to provide insights into interface boundaries in other metals and alloys of different structures.
In recent years, various types of power theft incidents have occurred frequently, and the training of the power-stealing detection model is susceptible to the influence of the imbalanced data set and the data noise, which leads to errors in power-stealing detection. Therefore, a power-stealing detection model is proposed, which is based on Improved Conditional Generation Adversarial Network (CWGAN), Stacked Convolution Noise Reduction Autoencoder (SCDAE) and Lightweight Gradient Boosting Decision Machine (LightGBM). The model performs Generation- Adversarial operations on the original unbalanced power consumption data to achieve the balance of electricity data, and avoids the interference of the imbalanced data set on classifier training. In addition, the convolution method is used to stack the noise reduction auto-encoder to achieve dimension reduction of power consumption data, extract data features and reduce the impact of random noise. Finally, LightGBM is used for power theft detection. The experiments show that CWGAN can effectively balance the distribution of power consumption data. Comparing the detection indicators of the power-stealing model with various advanced power-stealing models on the same data set, it is finally proved that the proposed model is superior to other models in the detection of power stealing.
Carbon (C) is of great importance to realize semi-insulating gallium nitride (GaN) for power electronic devices. We demonstrate that C can migrate from Ga sites to N sites after high temperature annealing of C doped GaN. The migration process is revealed through the observation of the generated Ga vacancies-related defects after annealing by positron annihilation spectroscopy. The activation energy of this migration process is estimated to be 2.5–2.8 eV from the temperature dependent annealing experiments, which is well consistent with the theoretical results from first-principles calculations.
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