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.

Kitchin，1506770 合金催化剂由两种或多种金属的混合物组成，广泛用于商业应用，比单一金属更有效地促进反应。 然而，在设计新催化剂时，很难预测两种或多种金属的哪种组合对于给定的反应最有效，特别是因为气固催化反应中的活性表面位点在结构和组成上通常与传统催化剂不同。块体合金。 这项工作将开发一种改进的模型，用于预测合金催化剂在实际工作条件下的催化反应性，从而能够发现新的和改进的合金催化剂，而无需对大量潜在金属组合进行昂贵且耗时的合成和测试。 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：表面偏析和吸附物引起的表面成分变化。 具体来说，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通过密度泛函理论计算）与表面偏析模型（包括吸附物诱导的效应）相结合，以产生统计加权平均特性合金表面。 新方法将应用于优化银钯合金的设计，用于在乙烯存在下对乙炔进行选择性加氢——这是一种重要的商业反应。本研究开发的理论方法将对催化行业产生广泛的影响，通过比简单的试错方法更有效地设计催化剂，并生产比目前使用的催化剂更具活性、选择性和能源效率的催化剂。使用。 此外，研究人员将向催化界公开他们的方法，作为研究工具和教育工具。