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Copper has attracted significant interest as a catalyst for thermal and electrochemical CO2or CO reduction into valuable compounds. These reactions may involve significant quantities of CO adsorbed on the surface of Cu catalysts, whose properties are typically affected by nanostructuring. Here, we applied density functional theory (DFT) calculations to study the effect of CO coverage on the catalytic properties of 0.8 nm large Cu38and 1.1 nm large Cu79nanocrystallites. Even under mild conditions, CO is calculated to saturate the surface of Cu nanoparticles at an unusually high coverage of 1 ML. At this coverage, CO is calculated to adsorb predominantly on top sites, with some CO molecules occupying bridge sites on the {111} facets. Such high quantities of adsorbed CO led to profound changes in the electronic structure of surface Cu atoms, whose d-band center positions shift to lower energies by more than 0.7 eV. CO coverage was also calculated to have a profound effect on the catalytic properties of Cu particles in the exemplary reaction of CO dimerization into OCCO. Unexpectedly, the barriers for OCCO formation were calculated to increase by ∼0.6 eV at high CO coverage on Cu nanoparticles due to the intricate effect of coadsorbates on OCCO binding to Cu. Thus, the interplay between nanostructuring and adsorbate coverage is shown to have significant implications for heterogeneous catalysis and electrochemistry that are hard to predict based on chemical intuition.

Purpose: The study examined the impact of feedback types through a learning management system (LMS) on employees’ training performance. The purpose of this study is to establish effective feedback on advanced technologies for promoting corporate training. Design/methodology/approach: A total of 148 trainees were recruited from a multinational medical company. Employees were randomly assigned to receive feedback from shallow to more constructive details on their learning performance with LMS. Data sources included are employees’ goal setting (GS) performance evaluated by the experts and their posttest scores obtained from the LMS. A series of statistical analyses were performed to investigate the impact of feedback intervention on employees’ GS and their impacts on corporate training results. Findings: GS has a significant impact on learning outcomes. Employees who set greater specific goals attained higher scores. Furthermore, feedback with more formative evaluation and constructive developmental advice resulted in the most significant positive influence on the relationship between participants’ GS and learning outcomes. Practical implications: Organizations can benefit from delivering appropriate feedback using LMS to enhance employees’ GS and learning efficacy in corporate training. Originality/value: This study is one of the first to examine the moderating effect of feedback provided by LMS on GS and online learning performance in corporate training. This study contributes to GS theory for practical application and proposes a viable method for remote learning. The current study’s findings can be used to provide educational psychological insights for training and learning in industrial contexts.

The point absorber is one of the most popular types of ocean wave energy converter (WEC) that harvests energy from the ocean. Often such a WEC is deployed in an ocean location with tidal currents or ocean streams, or serves as a mobile platform to power the blue economy. The shape of the floating body, or buoy, of the point absorber type WEC is important for the wave energy capture ratio and for the current drag force. In this paper, a new approach to optimize the shape of the point absorber buoy is developed to reduce the ocean current drag force on the buoy while capturing more energy from ocean waves. A specific parametric modelling is constructed to define the shape of the buoy with 12 parameters. The implantation of neural networks significantly reduces the computational time compared with solving hydrodynamics equations for each iteration. And the optimal shape of the buoy is solved using a genetic algorithm with multiple self-defined functions. The final optimal shape of the buoy in a case study reduces 68.7% of current drag force compared to a cylinder-shaped buoy, while maintaining the same level of energy capture ratio from ocean waves. The method presented in this paper has the capability to define and optimize a complex buoy shape, and solve for a multi-objective optimization problem.