Surface restructuring and predictive design of heterogeneous catalysts

• 成果类型: Review
• 署名作者: Tao, Franklin; Salmeron, Miquel
• 署名单位: University of Kansas; University of Kansas; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; University of California System; University of California Berkeley
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-8127
• DOI: 10.1126/science.adq0102
• 发表日期: 2024-11-22
• 关键词: fischer-tropsch synthesis; sum-frequency generation; machine-learning-methods; in-situ; ambient-pressure; photoelectron-spectroscopy; nanoparticle catalysts; metal nanoparticles; electron-microscopy; bimetallic sites

摘要

Heterogeneous catalysts, which often consist of metal nanoparticles loaded on supports such as metal oxides, can undergo several types of restructuring under reaction and catalytic conditions. Advanced characterizations now allow the surface structure of a catalyst in a gas phase to be determined to some extent. Metal nanoparticles may experience changes in shape, surface structure and composition, and atomic packing in response to the pressure of a reactant or product gas, temperature, and reaction between the catalyst surface and gas. Metal oxide supports can partially or fully encapsulate metal nanoparticles, altering their electronic structure and reactivity. Restructuring is a main approach to creating active catalytic sites. The rational design of catalysts must anticipate that such restructurings can occur under reaction or catalytic conditions and gain knowledge of how they occur. It is expected that computational studies can predict such restructurings and that advanced synthesis may be used to prepare pristine catalysts with enhanced resistance to restructurings.