VASE: A High-Entropy Alloy Short-Range Order Structural Descriptor for Machine Learning.

The short-range order (SRO) structure in high-entropy alloys (HEAs) is closely associated with many properties, which can be studied through density functional theory (DFT) calculations. Atomic-scale modeling and calculations require substantial computational resources, and machine learning can provide rapid estimations of DFT results. To describe SRO information in HEAs, a new descriptor based on Voronoi Analysis and Shannon Entropy (VASE) is proposed. Based on Voronoi analysis, the Shannon entropy is introduced to directly characterize atomic spatial arrangement information except for composition and atomic interactions, which is necessary for describing the disorder atomic occupancy in HEAs. The new descriptor is used for predicting the formation energy of FeCoNiAlTiCu system based on machine learning model, which is more accurate than other descriptors (Coulomb matrices, partial radial distribution functions, and Voronoi analysis). Moreover, the model trained based on VASE descriptors exhibits the best predictive performance for unrelaxed structures (24.06 meV/atom). The introduction of Shannon entropy provides an effective representation of atomic arrangement information in HEAs, which is a powerful tool for investigating the SRO phenomena.

A surface strategy boosting the ethylene selectivity for CO2 reduction and in situ mechanistic insights.

Electrochemical reduction of carbon dioxide into ethylene, as opposed to traditional industrial methods, represents a more environmentally friendly and promising technical approach. However, achieving high activity of ethylene remains a huge challenge due to the numerous possible reaction pathways. Here, we construct a hierarchical nanoelectrode composed of CuO treated with dodecanethiol to achieve elevated ethylene activity with a Faradaic efficiency reaching 79.5%. Through on in situ investigations, it is observed that dodecanethiol modification not only facilitates CO2 transfer and enhances *CO coverage on the catalyst surfaces, but also stabilizes Cu(100) facet. Density functional theory calculations of activation energy barriers of the asymmetrical C-C coupling between *CO and *CHO further support that the greatly increased selectivity of ethylene is attributed to the thiol-stabilized Cu(100). Our findings not only provide an effective strategy to design and construct Cu-based catalysts for highly selective CO2 to ethylene, but also offer deep insights into the mechanism of CO2 to ethylene.

A Study of Silver Decoration on Carbon Nanotubes via Ultrasonic Chemical Synthesis and Their Reinforced Copper Matrix Composites.

The homogeneous distribution of carbon nanotubes (CNTs) in the Cu matrix and good interfacial bonding are the key factors to obtain excellent properties of carbon nanotube-reinforced Cu-based composites (CNT/Cu). In this work, silver-modified carbon nanotubes (Ag-CNTs) were prepared by a simple, efficient and reducer-free method (ultrasonic chemical synthesis), and Ag-CNTs-reinforced copper matrix composites (Ag-CNTs/Cu) were fabricated by powder metallurgy. The dispersion and interfacial bonding of CNTs were effectively improved by Ag modification. Compared to CNTs/Cu counterparts, the properties of Ag-CNTs/Cu samples were significantly improved, with the electrical conductivity of 94.9% IACS (International Annealed Copper Standard), thermal conductivity of 416 W/m·k and tensile strength (315 MPa). The strengthening mechanisms are also discussed.