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Abstract The hydrogen produced by Al‐doped SrTiO₃/TiO₂core‒shell catalysts with a range of Al‐doped SrTiO₃cores and the same TiO₂shell are compared. The study included SrTiO₃cores doped with different amounts of Al (0, 1, 2, or 3 mol%) added at different points in the synthesis (prior to or during the molten salt treatment) and at different temperatures (900°C, 1000°C, and 1100°C). It was found that core‒shell catalysts with different cores had hydrogen generation rates that varied by a factor of more than 40 and varied with the processing parameters in the same way as the hydrogen generation rates of the cores alone. The best catalysts had 2 or 3 mol% added Al, added during treatment in a SrCl₂molten salt at 1000°C or 1100°C. Because the core absorbs most of the light, its ability to separate and transport photogenerated charge carriers dominates the properties of the core‒shell catalyst. This indicates that, to optimize the properties of core‒shell catalysts, it is essential to optimize the properties of the core. While the shell can be important to protect the core from degradation, it is not as important to the overall reactivity as the core. 

In this article, we conduct a measurement study to comprehensively compare the accuracy impacts of multiple embedding options in cryptographic API completion tasks. Embedding is the process of automatically learning vector representations of program elements. Our measurement focuses on design choices of three important aspects,program analysis preprocessing,token-level embedding, andsequence-level embedding. Our findings show that program analysis is necessary even under advanced embedding. The results show 36.20% accuracy improvement, on average, when program analysis preprocessing is applied to transfer bytecode sequences into API dependence paths. With program analysis and the token-level embedding training, the embeddingdep2vecimproves the task accuracy from 55.80% to 92.04%. Moreover, only a slight accuracy advantage (0.55%, on average) is observed by training the expensive sequence-level embedding compared with the token-level embedding. Our experiments also suggest the differences made by the data. In the cross-app learning setup and a data scarcity scenario, sequence-level embedding is more necessary and results in a more obvious accuracy improvement (5.10%).