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The as-obtained NiCo2O4@CoMoO4/NF-7 electrode exhibits superior electrocatalytic performance and extraordinary durability for OER, HER, and overall water splitting.

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Engineering the reaction interface is necessary for advancing various electrocatalytic processes. However, most designed catalysts tend to be ineffective due to the inevitable structural reconstruction. Here we utilize that operando electrocatalysis variations (i.e., chalcogen leaching) manipulate the reactant interface toward amine electrooxidation. Taking chalcogen-doped Ni(OH) 2 as an example, operando techniques uncover that chalcogens leach from the matrix and then adsorb on the surface of NiOOH as chalcogenates during the electrooxidation process. The charged chalcogenates will induce the local electric field that pushes the polar amines through the inner Helmholtz plane to enrich on the catalyst surface. Meanwhile, the polarization effect of chalcogenates and amines boost amino C−N bond activation for dehydrogenation into nitrile CN bonds. Under the promotion effect of surface-adsorbed chalcogenate ions, our catalysts display over 99.5% propionitrile selectivity at the low potential of 1.317 V with an ultrahigh current density. This finding highlights the use of operando changes of catalysts to rationally design efficient catalysts and further clarifies the underlying role of chalcogen atoms in the electrooxidation process.

Abstract-Adaptive Random Testing (ART) is a testing technique which is based on an observation that a test input usually has the same potential as its neighbors in detection of a specific program defect. ART helps to improve the efficiency of random testing in that test inputs are selected evenly across the input spaces. However, the application of ART to objectoriented programs (e.g., C++ and Java) still faces a strong challenge in that the input spaces of object-oriented programs are usually high dimensional, and therefore an even distribution of test inputs in a space as such is difficult to achieve. In this paper, we propose a divergence-oriented approach to adaptive random testing of Java programs to address this challenge. The essential idea of this approach is to prepare for the tested program a pool of test inputs each of which is of significant difference from the others, and then to use the ART technique to select test inputs from the pool for the tested program. We also develop a tool called ARTGen to support this testing approach, and conduct experiment to test several popular opensource Java packages to assess the effectiveness of the approach. The experimental result shows that our approach can generate test cases with high quality.