Ammonia is important, both as a fertilizer and as a carrier of clean energy, mainly produced by the Haber-Bosch process, which consumes hydrogen and emits large amounts of carbon dioxide. The ENRR (Electronchemical Nitrogen Reduction Reaction) is considered a promising method for nitrogen fixation owing to their low energy consumption, green and mild. However, the ammonia yield and Faraday efficiency of the ENRR catalysts are low due to the competitive reaction between HER and NRR, the weak adsorption of N2 andthe strong N≡N triple bond. Oxygen vacancy engineering is the most important method to improve NRR performance, not only for fast electron transport but also for effective breaking of the N≡N bond by capturing metastable electrons in the antibonding orbitals of nitrogen molecules. In this review, the recent progress of OVs (oxygen vacancies) in ENRR has been summarized. First, the mechanism of NRR is briefly introduced, and then the generation methods of OVs and their applicationin NRR are discussed, including vacuum annealing, hydrothermal method, hydrogen reduction, wet chemical reduction, plasma treatment and heterogeneous ion doping. Finally, the development and challenges of OVs in the field of electrochemical nitrogen fixation are presented. This review shows the important areas of development of catalysts to achieve industrially viable NRR.

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The work analyzes the key impact factors on the performances of rare-earth element doped oxide thin film transistors (TFTs), which are potentially used for high performance displays, by comparatively using a Bayesian Neural Network (BNN) method and Artificial Neural Network (ANN) method based on published and self-experimental data which was exhaustively collected. Both BNN and ANN methods can effectively identify the primary impact factors among rare-earth element type, doping concentration, thin film thickness, channel length and width, which are key factors to determine the TFTs performances. Comparisons between the ANN and BNN methods, the BNN approach offers more reliable and robust predictions on the dataset. Accordingly, the efficient neural network models tailored to the data features were accurately established. A key outcome from the BNN models is the relative importance ranking of the influence factors and relationship between the carrier mobility and element type, concentration as well. To the TFT mobility, rare-earth element concentration is the most critical factor, suggesting lower concentration exhibited higher mobility, followed by the rare-earth element type. To the sub-threshold swing performance of TFTs, the rare-earth element type is the most significant influence factor, suggesting higher valence rare-earth is superior to lower valence one, followed by the element concentration. The results are basically consistent with experimental tendency. These insights could effectively guide the design of oxide semiconductor materials and TFT device structure, to achieve high-performance (high mobility and high stability) oxide TFT devices for displays.