UNS:Modeling Bulk Composition Dependent Alloy Surface Properties Under Reaction Conditions

UNS：在反应条件下模拟与块体成分相关的合金表面特性

• 批准号: 1506770
• 负责人: John Kitchin
• 金额: $ 32.71万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1506770&HistoricalAwards=false
• 关键词: UNS; Modeling; Bulk; Composition; Dependent

Kitchin, 1506770Alloy catalysts, consisting of mixtures of two or more metals, are widely used in commercial applications to promote reactions more effectively than possible with a single metal. However, in designing new catalysts, it is difficult to predict which combinations of two or more metals will be the most effective for a given reaction, especially because the active surface sites in gas-solid catalytic reactions typically are different in structure and composition than those of the bulk alloy. This work will develop an improved model for predicting the catalytic reactivity of alloy catalysts under actual working conditions, thereby enabling the discovery of new and improved alloy catalysts without the need for costly and time-consuming synthesis and testing of large arrays of potential metal combinations. The work addresses two major factors that hinder direct correlation of bulk alloy composition with catalytic reactivity: surface segregation and adsorbate-induced changes in surface composition. Specifically, the novel aspect of the study is to combine a surface site distribution function (where the reactivity of each site is calculated by density functional theory) with a surface segregation model (that includes adsorbate-induced effects) to produce a statistically weighted average property of the alloy surface. The new approach will be applied to the design of optimized Ag-Pd alloys for the selective hydrogenation of acetylene in the presence of ethylene - a commercially important reaction. The theoretical methods developed in this study will have broad impact to the catalysis industry by enabling more efficient design of catalysts than simple trial-and-error methods, and by producing catalysts that are more active, selective, and energy-efficient than catalysts currently in use. In addition, the researchers will make their methods openly available to the catalysis community, both as a research tool and as an educational tool.

合金催化剂由两种或两种以上金属的混合物组成，在商业应用中被广泛用于比单一金属更有效地促进反应。然而，在设计新的催化剂时，很难预测哪种两种或两种以上金属的组合对给定的反应最有效，特别是因为气固催化反应中的活性表面位点通常与大块合金的结构和组成不同。这项工作将开发一个改进的模型来预测合金催化剂在实际工作条件下的催化反应性，从而使新的和改进的合金催化剂的发现不需要昂贵和耗时的合成和测试大量潜在的金属组合。这项工作解决了阻碍大块合金成分与催化反应性直接相关的两个主要因素：表面偏析和吸附引起的表面成分变化。具体而言，该研究的新颖之处是将表面位点分布函数（其中每个位点的反应性由密度泛函理论计算）与表面偏析模型（包括吸附诱导效应）相结合，以产生合金表面的统计加权平均性能。新方法将应用于设计优化的Ag-Pd合金，用于乙炔在乙烯存在下的选择性加氢反应，这是一个重要的商业反应。本研究中发展的理论方法将对催化工业产生广泛的影响，因为它使催化剂的设计比简单的试错方法更有效，并且通过生产比目前使用的催化剂更活跃、更有选择性和更节能的催化剂。此外，研究人员将把他们的方法作为研究工具和教育工具公开提供给催化社区。