UNS:Modeling Bulk Composition Dependent Alloy Surface Properties Under Reaction Conditions
UNS:Modeling Bulk Composition Dependent Alloy Surface Properties Under Reaction Conditions
批准号:
1506770
负责人:
John Kitchin
金额:
$32.71万
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
项目状态:
已结题
起止时间:
2015-09-01 至 2019-08-31
中文摘要
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英文摘要
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.
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IUCRC Planning Grant Carnegie Mellon University: Center for Materials Data Science for Reliability and Degradation (MDS-Rely)
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批准号:2310663
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项目类别:Standard Grant
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资助金额:$2.0万
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财政年份:2023
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负责人:John Kitchin
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依托单位:
国内基金
海外基金
Galaxy Analytical Modeling
Evolution (GAME) and cosmological
hydrodynamic simulations.
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批准号:
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项目类别:省市级项目
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资助金额:10.0万元
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批准年份:2025
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负责人:Antonios Katsianis
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依托单位: