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

氧化反应和烷烃转化反应在化学加工工业中被广泛应用，以生产各种各样的产品。这些产品的总规模超过1000亿美元，并产生数亿吨二氧化碳当量的温室气体。该项目的重点是发现和设计用于这些反应的改进催化剂，从而提高工艺效率，提高经济效益，减少温室气体排放。最近，单原子合金（SAA）催化剂在许多反应中显示出很大的前景；然而，传统的saas——将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，这限制了我们为特定反应量身定制它们的能力。该项目将利用计算和机器学习工具从理论上筛选合金宿主SAAs，以确定氧化和烷烃转化反应的稳定和活性催化剂，从而解决这一限制。最有希望的候选材料将通过实验合成、表征和测试，从而避免繁琐的试错催化剂设计，并为合金宿主SAAs在广泛化学反应中的广泛应用打开大门。教育方面的好处包括学习模块的发展，将提高工程专业学生的技术写作技能。在本项目中，机器学习和密度泛函理论将用于筛选合金宿主SAAs，以确定氧化和烷烃转化反应的稳定和活性催化剂。接下来将是对材料进行表面科学的合作研究，最后将最有希望的候选材料转化为纳米颗粒催化剂。值得注意的是，合金宿主SAAs既能使反应物容易活化，又能使下游中间体弱结合；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个属性，以及稳定性，可以帮助开发用于各种不同反应的改进催化剂。为技术写作开发的学习模块是至关重要的，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）。特别是，这些模块将提供技术写作草稿的多个层次和反馈来源，利用从技能改进研究中得到的成熟原则。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.