The reaction mechanism and properties of a catalytic process are primarily determined by the interactions between reacting species and catalysts. However, the interactions are often challenging to be experimentally measured, especially for unstable intermediates. Therefore, it is of significant importance to establish an exact relationship between chemical-catalyst interactions and catalyst parameters, which will allow calculation of these interactions and thus advance their mechanistic understanding. Herein we report the description of adsorption energy on transition metals by considering both ionic bonding and covalent bonding contributions and introduce the work function as one additional responsible parameter. We find that the adsorption energy can be more accurately described using a two-dimensional (2D) polynomial model, which shows a significant improvement compared with the current adsorption energy-d-band center linear correlation. We also demonstrate the utilization of this new 2D polynomial model to calculate oxygen binding energy of different transition metals to help understand their catalytic properties in oxygen reduction reactions.

A heterogeneous Pt single-atom catalyst promotes E-selective synthesis of hydrazones en route to bioactive 1H-indazoles.

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Test case prioritization (TCP) has been considerably utilized to arrange the implementation order of test cases, which contributes to improve the efficiency and resource allocation of software regression testing. Traditional coverage-based TCP techniques, such as statement-level, method/function-level and class-level, only leverages program code coverage to prioritize test cases without considering the probable distribution of defects. However, software defect data tends to be imbalanced following Pareto principle. Instinctively, the more vulnerable the code covered by the test case is, the higher the priority it is. Besides, statement-level coverage is a more fine-grained method than function-level coverage or class-level coverage, which can more accurately formulate test strategies. Therefore, we present a test case prioritization approach based on statement software defect prediction to tame the limitations of current coverage-based techniques in this paper. Statement metrics in the source code are extracted and data pre-processing is implemented to train the defect predictor. And then the defect detection rate of test cases is calculated by combining the prioritization strategy and prediction results. Finally, the prioritization performance is evaluated in terms of average percentage faults detected in four open source datasets. We comprehensively compare the performance of the proposed method under different prioritization strategies and predictors. The experimental results show it is a promising technique to improve the prevailing coverage-based TCP methods by incorporating statement-level defect-proneness. Moreover, it is also concluded that the performance of the additional strategy is better than that of max and total, and the choice of the defect predictor affects the efficiency of the strategy.