Collaborative Research: Controlling Metal-Oxide Interface Chemistry for New C-H Activation Catalysts

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The world will continue to rely on hydrocarbon resources during the Clean Energy Transition to renewable and sustainable net-zero carbon fuels and chemicals. Thus, there is a substantial opportunity to reduce the carbon footprint of our Nation’s tremendous natural gas reserves through efficient chemical processing to manufacture readily transportable liquid fuels and chemicals. The project addresses a key challenge in natural gas chemical processing – breaking the carbon-hydrogen bonds as needed to manufacture higher-value fuels and chemicals. Catalytic technology is critical to energy efficient natural gas upgrading. To that end, the project investigates a new class of hydrocarbon conversion catalysts that can potentially enable natural gas to be converted to liquid fuels and chemicals in the field, thereby generating enormous boosts in energy efficiency and a significant reduction in greenhouse gas emissions. The project also will invest in research experiences targeted to economically disadvantaged undergraduate students, who are disproportionately women and under-represented minorities. The undergraduates will be engaged in a peer-mentoring network, which can be particularly effective in promoting undergraduate success, especially when the students in the team have diverse cultural and socio-economic backgrounds. The project will combine the investigators’ experimental and computational expertise to develop new heterolytic C-H activation catalysts based on the unique properties of Au/oxide interfaces, which will be tuned to maximize activity by varying the composition of the oxide. A combination of density functional theory and new in-situ IR techniques will be employed to quantify H2 activation parameters over a carefully chosen series of Au/MOx catalysts. H-H and C-H bond activation are closely related processes, so the broad approach is to first study how oxide composition tunes the thermodynamics and kinetics of H2 activation, and then extend this knowledge to design effective interfaces for C-H activation. Machine learning techniques will use these results to survey H2 activation chemistry for thousands of oxide compositions, which will provide direct insight into underlying physio-chemical processes that govern complex interactions at the metal-oxide interface. Coupled with experiments intentionally designed to inform and refine the computational models, the research will go beyond identification of factors that impact C-H activation (such as support reducibility, support basicity, M-OH bond flexibility) to assess the relative impact of each system variable. The resulting C-H bond activation chemistry will be tested with a suite of hydrocarbons chosen for their adsorption properties and with catalytic benzyl alcohol oxidation. Ideally, the project will generate a research protocol that will combine efficient computational screening and experimental validation of metal-support catalyst combinations tailored for efficient C-H bond activation across a broad range of hydrocarbon and organic molecules.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.