CAREER: Solution Catalysis Containing Seemingly Incompatible Steps

在化学系化学催化(CAT)计划的支持下，加州大学洛杉矶分校(UCLA)的刘创(Chong Liu)正在开发新的催化转化方法，以使反应周期中包含通常不相容的步骤。通过使用电化学、纳米材料和机器学习，刘团队寻求建立一般设计原则，并开发用于重要化学反应的新催化剂，包括将二氧化碳和甲烷转化为商品化学品。刘博士还努力促进对来自不同背景的研究生和本科生进行包容性、跨学科的培训。刘博士正在应用机器学习和自然语言处理来帮助加州大学洛杉矶分校普通化学课程的课程开发。如果成功，这样的努力有可能改善数万名本科生的学习经历和结果。成功完成这一项目将提高社会对STEM(科学、技术、工程和数学)教育的欣赏程度，并有助于培训下一代劳动力。在这项研究项目中，加州大学洛杉矶分校的刘冲和他的团队正在开发包含竞争/不相容反应步骤的溶液催化的设计原则，并将这些原则应用于各种应用。在这些努力中，基于纳米材料的电化学作为一个平台，在微观层面上控制反应物物种的空间分布。在基于机器学习的逆向设计的辅助下，该研究将侧重于这些概念在低碳烷烃活化、有机污染物还原脱氯以及二氧化碳电化学还原与氢甲酰化反应耦合的催化级联反应中的机理研究和实际应用。从长远来看，这样的研究将回答催化领域的两个重要问题：(1)人们能否开发一个通用的平台，适用于具有可翻译技术的各种空间控制的溶液催化？(2)如何基于靶向反应步骤的内在反应性来定量地定制化学反应的空间控制？通过在微观水平上控制溶液催化的空间分布来模拟生物学，为开发新的化学转化提供了潜力，这些新的化学转化即使不是不可能，也很难在均相溶液中开发。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

With the support of the Chemical Catalysis (CAT)program in the Division of Chemistry, Chong Liu of the University of California, Los Angeles (UCLA) is developing new catalytic transformations in contain normally incompatible steps within the reaction cycles. With the use of electrochemistry, nanomaterials, and machine learning, the Liu team seeks to establish general design principles and develop new catalysts for important chemical reactions including the conversion of carbon dioxide and methane into commodity chemicals. Dr. Liu also strives to facilitate inclusive, interdisciplinary training of graduate and undergraduate students from different backgrounds. Dr. Liu is applying machine learning and natural language processing to aid the curriculum development of general chemistry courses at the University of California, Los Angeles. If successful, such efforts have the potential to improve learning experiences and outcomes for tens of thousands of undergraduates. Successful completion of such a project will boost society’s appreciation of STEM (science, technology, engineering and mathematics) education and help train the next-generation workforce. In this research project, Chong Liu and his team at UCLA are developing design principles for solution catalysis containing competing/incompatible reaction steps and applying such principles to a variety of applications. In these endeavors, nanomaterials-based electrochemistry serves as a platform to control the spatial distribution of reactant species at the microscopic level. Aided by machine-learning-based inverse design, the research will focus on the mechanistic study and practical application of such concepts to the activation of light alkanes, reductive dechlorination of organic pollutants, and the catalytic cascade that couples the electrochemical reduction of CO2 with the hydroformylation reaction. In the long run, such research will answer two important questions in the field of catalysis: (1) Can one develop a general platform applicable to a variety of spatially controlled solution catalysis with translatable know-how? (2) How can one quantitatively tailor the spatial control of chemical reactions based on the intrinsic reactivity of a targeted reaction step? Mimicking biology by controlling the spatial distribution of solution catalysis at the microscopic level offers to potential for the development of new chemical transformations that would be difficult, if not impossible, to develop in homogenous solution.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.