CAREER: Computational Design of Single-Atom Sites in Alloy Hosts as Stable and Efficient Catalysts

职业：合金主体中单原子位点的计算设计作为稳定和高效的催化剂

• 批准号: 2340356
• 负责人: Matthew Montemore
• 金额: $ 62.35万
• 依托单位: Tulane University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2029-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340356&HistoricalAwards=false
• 关键词: CAREER; Computational; Design; Single; Atom

Oxidation and alkane conversion reactions are widely used in the chemical process industry to produce a broad range of products. Collectively, those products amount to over $100B scale and produce hundreds of megatons CO2-equivalent of greenhouse gases (GHGs). The project focuses on the discovery and design of improved catalysts for these reactions, which translates to improvements in process efficiency, more favorable economics, and reduction in GHG emissions. Recently, single-atom alloy (SAA) catalysts have shown great promise for a number of reactions; however, conventional SAAs—which consist of one metal doped as isolated atoms into a second metal—comprise a fairly small design space, which limits our ability to tailor them for a given reaction. The project will address this limitation by employing computational and machine learning tools to theoretically screen alloy-host SAAs to identify stable and active catalysts for oxidation and alkane conversion reactions. The most promising candidates will be synthesized, characterized, and tested experimentally, thus avoiding tedious trial-and-error catalyst design, and opening the door to widespread application of alloy-host SAAs across a broad range of chemical reactions. Educational benefits include the development of learning modules that will enhance the technical writing skills of engineering students. In this project, machine learning and density functional theory will be used to screen alloy-host SAAs to identify stable and active catalysts for oxidation and alkane conversion reactions. This will be followed by collaborative surface-science studies with well-defined materials, and finally translation of the most promising candidates to nanoparticle catalysts. Notably, alloy-host SAAs can give both facile activation of reactants and weak binding of downstream intermediates; this desirable combination of properties is not achievable by many traditional metal catalysts. Therefore, developing and applying effective design strategies for achieving both attributes, as well as stability, can aid in developing improved catalysts for a wide variety of different reactions. The learning modules that will be developed for technical writing are critical because many surveys of engineering employers have clearly shown that technical communications skills (including technical writing) are lacking in recent engineering graduates. In particular, the modules will provide multiple levels and sources of feedback on drafts of technical writing, leveraging well-established principles from research on skill improvement.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

氧化和烷烃转化反应广泛应用于化学加工行业，生产各种产品，这些产品的总规模超过 100B 美元，并产生数百万吨二氧化碳当量（GHG）。发现和设计用于这些反应的改进催化剂，这意味着工艺效率的提高、更有利的经济性以及温室气体排放的减少。最近，单原子合金（SAA）催化剂显示出巨大的应用前景。然而，传统的SAA（由一种金属作为孤立的原子掺杂到另一种金属中）包含相当小的设计空间，这限制了我们针对给定反应定制它们的能力，该项目将通过以下方式解决这一限制。利用计算和机器学习工具从理论上筛选合金主体SAA，以确定用于氧化和烷烃转化反应的稳定和活性催化剂，将合成、表征和实验测试最有希望的候选催化剂，从而避免繁琐的试错催化剂设计。 ， 和为合金主体 SAA 在广泛的化学反应中的广泛应用打开了大门。教育效益包括开发学习模块，以提高工程学生的技术写作技能。在这个项目中，机器学习和功能密度理论将得到应用。用于筛选合金主体 SAA，以确定氧化和烷烃转化反应的稳定和活性催化剂，随后将与明确的材料进行协作表面科学研究，最后将最有希望的候选材料转化为纳米颗粒催化剂。合金主体SAA可以实现反应物的轻松活化和下游中间体的弱结合；这种理想的性能组合是许多传统金属催化剂无法实现的，因此，开发和应用有效的设计策略来实现这两种属性以及稳定性可以帮助开发。为各种不同反应开发的学习模块至关重要，因为许多对工程雇主的调查清楚地表明，最近的工程毕业生缺乏技术沟通技能（包括技术写作）。这些模块将提供多个级别和来源对技术写作草稿的反馈，利用技能改进研究中的既定原则。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。