CAREER: Solution Catalysis Containing Seemingly Incompatible Steps

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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.