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被广泛用于化学过程行业，以生产广泛的产品。这些产品总计超过100B美元，并生产数百种兆吨二氧化碳的温室气体（GHG）。该项目着重于这些反应的改进催化剂的发现和设计，这转化为过程效率的提高，更有利的经济学以及温室气体排放的减少。最近，单原子合金（SAA）催化剂对许多反应表现出了巨大的希望。但是，传统的SaaS（由一种金属组成，将一种掺杂的原子掺杂到第二种金属中），这是一个相当小的设计空间，这限制了我们为给定反应量身定制它们的能力。该项目将通过在理论屏幕合金宿主SaaS中使用计算和机器学习工具来解决这一限制，以识别氧化和烷烃转化反应的稳定且活跃的催化剂。最有希望的候选人将在实验中合成，表征和测试，从而避免乏味的反复试验催化剂设计，并为在广泛的化学反应中的合金 - 宿主SaaS宽宽大的范围打开大门。教育益处包括开发学习模块，这些模块将提高工程学生的技术写作技巧。在这个项目中，机器学习和密度功能理论将用于筛选合金宿主SaaS，以识别氧化和烷烃转化反应的稳定和活性催化剂。之后，将使用定义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物转换为纳米颗粒催化剂。值得注意的是，合金宿主SaaS可以使反应物的轻松激活和下游中间体的弱结合。许多传统的金属催化剂无法实现这种理想的特性组合。因此，制定并应用有效的设计策略来实现属性以及稳定性，可以帮助开发改进的催化剂，以实现各种不同的反应。为技术写作开发的学习模块至关重要，因为许多工程员工的调查清楚地表明，最近的工程毕业生缺乏技术沟通技巧（包括技术写作）。特别是，这些模块将提供有关技术写作草案的多个层次和反馈来源，利用了有关技能改进研究的良好原则。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来通过评估来获得的支持。