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The development of efficient metal catalysts for in situ hydrogenation of CO2 in water under mild conditions has gained considerable attention. Three Al alloys (Al/Fe, Al/Fe/Cu, Al/Cu) and three Zn/Cu alloys for in situ hydrogenation of CO2 in aqueous bicarbonate solutions were investigated. Hydrogen was generated by reaction of Al, Fe, and Zn in the alloys with water. In situ hydrogenation of CO2 was likely to be catalyzed by intermetallic compounds and generated metal oxides. Al alloys catalyzed the hydrogenation to methane while Zn/Cu alloys produced CO and formic acid. Zn/Cu5 possessed the highest catalytic activity, which was attributed to the CuZn5 crystal planes in the alloys. Insights are provided into the importance of compositions and structures of alloys for the selectivity for in situ hydrogenation of CO2 in aqueous bicarbonate solutions. [ABSTRACT FROM AUTHOR]

Hierarchical Cu−Sn core/shell nanowire arrays were built on 3‐dimensional macroporous Ni foams through a two‐step deposition, annealing, and electroreduction treatment. Cu was electroplated on Ni foam substrates and the sample was annealed at 500 °C followed by electroreduction, producing Cu nanowires of 150 nm diameter in arrays on the skeleton of Ni foams. Sn nanoparticles of 14–80 nm were then chemically deposited on Cu nanowires in clusters and a second annealing treatment at 200 °C followed by electroreduction re‐organized the clusters into a SnxO/Sn shell of 8 nm thickness. Creating such a Sn shell on Cu nanowires suppressed faradaic efficiencies for H2 evolution from 55.7 to 10.1 % and for HCOOH formation from 13.2 to 2.0 % and enhanced CO generation from 32.0 to 90.0 % at an applied potential of −0.8 V (vs. RHE). The faradaic efficiency for CO production remained almost constant at 90.0–91.4 % with total current densities of −13.2 to −19.3 mA cm−2 between −0.8 and −1.2 V (vs. RHE). Array of sunshine: A double deposition‐annealing treatment created hierarchical Cu−Sn core/shell nanowire arrays on Ni foam, which can achieve high‐performance CO2 reduction. [ABSTRACT FROM AUTHOR]

Nanostructure and crystallinity of transition metals play an important role in catalyzing carbon dioxide electroreduction (CO2ER) where Cu is a typical electrocatalyst with a wide variety of products and Pb has a high overpotential for H2 evolution and is selective toward formic acid. In this study, 3D hierarchical nanostructures of Cu–Pb catalyst are prepared by a two‐step electrodepositing–annealing–electroreduction approach (EAE). Cu nanowires (Cu NWs) of 200–400 nm diameter are built on the surface of commercial nickel foam substrates through an EAE step. Then, Pb nanoparticles with diameter of 5–10 nm are uniformly created on the surface of Cu NWs by a second EAE step. The nanostructural Cu–Pb electrodes catalyze CO2ER at a current density of −9.35 mA cm−2 (at −0.93 V vs reversible hydrogen electrode (RHE)). The H2 evolution is suppressed by 35.6% and CO and HCOOH are enhanced by 29.6% and 9.2%, respectively, as compared with Cu NWs. The protocol proposed in this study provides a simple and straightforward approach for preparing high‐performance, hierarchical nanostructures of Cu–Pb bimetal catalyst for CO2ER. A two‐step electrodepositing‐annealing‐electroreduction approach is developed to synthesize 3‐dimensional hierarchical nanostructures of Cu‐Pb catalysts, which catalyze CO2 electroreduction in bicarbonate solutions at a current density of –9.35 mA cm–2 and faradaic efficiencies of 50.64% and 21.97% for CO and HCOOH. The protocol provides a straightforward approach for preparing high‐performance, hierarchical nanostructures of Cu‐Pb bimetal catalyst for CO2 electroreduction. [ABSTRACT FROM AUTHOR]

Background: Neuroimaging studies have demonstrated alterations in hippocampal volume and hippocampal subfields among individuals with amnestic mild cognitive impairment (aMCI). However, research on using hippocampal subfield volume modeling to differentiate aMCI from normal controls (NCs) is limited, and the relationship between hippocampal volume and overall cognitive scores remains unclear., Methods: We enrolled 50 subjects with aMCI and 44 NCs for this study. Initially, a univariate general linear model was employed to analyze differences in the volumes of hippocampal subfields. Subsequently, two sets of dimensionality reduction methods and four machine learning techniques were applied to distinguish aMCI from NCs based on hippocampal subfield volumes. Finally, we assessed the correlation between the relative volumes of hippocampal subfields and cognitive test variables (Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA))., Results: Significant volume differences were observed in several hippocampal subfields, notably in the left hippocampus. Specifically, the volumes of the hippocampal tail, subiculum, CA1, presubiculum, molecular layer, GC-ML-DG, CA3, CA4, and fimbria differed significantly between the two groups. The highest area under the curve (AUC) values for left and right hippocampal machine learning classifiers were 0.678 and 0.701, respectively. Moreover, the volumes of the left subiculum, left molecular layer, right subiculum, right CA1, right molecular layer, right GC-ML-DG, and right CA4 exhibited the strongest and most consistent correlations with MoCA scores., Conclusion: Hippocampal subfield volume may serve as a predictive marker for aMCI. These findings underscore the sensitivity of hippocampal subfield volume to overall cognitive performance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Feng, Wang, Tang, Ge, Hu, Liao and Ding.)