摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。据新闻报道研究称，NewsRx记者源于中华人民共和国厦门的报道，“过渡金属™双原子催化剂(DACs)有望用于二氧化碳还原反应(CO2RR)通过d-d轨道协作相互作用，但它们的有效性往往受到*CO和*CHO在过渡离子金属位置上的线性标度关系，通常导致CO作为主要产品。具体而言，p-d轨道耦合可能会进一步影响分子轨道的运动，从而调节分子轨道的运动DACs的介电性能和催化活性对提高DACs的催化性能具有重要意义CO2RR。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting originating in Xiamen, People ’s Republic of China, by NewsRx journalists, research stated,“Transition metal ™ dual-atom catalysts (DACs) show promise for carbon dioxide reduction reaction(CO2RR) through d-d orbital cooperative interactions, but their effectiveness is often curtailed by thelinear scaling relations between *CO and *CHO on transit ion metal sites, typically resulting in CO as thepredominant product. Specifica lly, the p-d orbital coupling may exert further influence to regulate theelectr onic properties and catalytic activity of DACs, which will be of great significa nce for promotingCO2RR.”
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