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Nanoporous copper (np-Cu) has attracted much more attention due to its lower cost compared to other noble metals and high functionality in practical use. Herein, Al100−xCux(x = 13–88 at.%) precursor films with thicknesses of 0.16–1.1 μm were fabricated by varying magnetron co-sputtering parameters. Subsequently, utilizing a one-step dealloying strategy, a series of np-Cu films with ligament sizes ranging from 11.4–19.0 nm were synthesized. The effects of precursor composition and substrate temperature on the microstructure of np-Cu films were investigated. As the atomic ratio of Cu increases from 15 to 34, the np-Cu film detached from the substrate gradually transforms into a bi-continuous ligament-channel structure that is well bonded to the substrate. Furthermore, the novel bi-layer hierarchical np-Cu films were successfully prepared based on single-layer nanoporous films. Our findings not only contribute to the systematic understanding of the modification of the morphology and structure of np-Cu films but also offer a valuable framework for the design and fabrication of other non-noble nanoporous metals with tailored properties. [ABSTRACT FROM AUTHOR]

The Beetle Swarm Optimization (BSO) algorithm is a high-performance swarm intelligent algorithm based on beetle behaviors. However, it suffers from poor search speeds and is prone to local optimization due to the size of the step length. To address this further, a novel improved opposition-based learning mechanism is utilized, and an adaptive beetle swarm optimization algorithm with novel opposition-based learning (NOBBSO) is proposed. In the proposed NOBBSO algorithm, the novel opposition-based learning is designed as follows. Firstly, according to the characteristics of the swarm intelligence algorithms, a new opposite solution is obtained to generate the current optimal solution by iterations in the current population. The novel opposition-based learning strategy is easy to converge quickly. Secondly, an adaptive strategy is used to make NOBBSO parameters self-adaptive, which makes the results tend to converge more easily. Finally, 27 CEC2017 benchmark functions are tested to verify its effectiveness. Comprehensive numerical experiment outcomes demonstrate that the NOBBSO algorithm has obtained faster convergent speed and higher convergent accuracy in comparison with other outstanding competitors. [ABSTRACT FROM AUTHOR]

Ag-Zn alloys are identified as highly active and selective electrocatalysts for CO2 reduction reaction (CO2RR), while how the phase composition of the alloy affects the catalytic performances has not been systematically studied yet. In this study, we fabricated a series of Ag-Zn alloy catalysts by magnetron co-sputtering and further explored their activity and selectivity towards CO2 electroreduction in an aqueous KHCO3 electrolyte. The different Ag-Zn alloys involve one or more phases of Ag, AgZn, Ag5Zn8, AgZn3, and Zn. For all the catalysts, CO is the main product, likely due to the weak CO binding energy on the catalyst surface. The Ag5Zn8 and AgZn3 catalysts show a higher CO selectivity than that of pure Zn due to the synergistic effect of Ag and Zn, while the pure Ag catalyst exhibits the highest CO selectivity. Zn alloying improves the catalytic activity and reaction kinetics of CO2RR, and the AgZn3 catalyst shows the highest apparent electrocatalytic activity. This work found that the activity and selectivity of CO2RR are highly dependent on the element concentrations and phase compositions, which is inspiring to explore Ag-Zn alloy catalysts with promising CO2RR properties. [ABSTRACT FROM AUTHOR]