Multivariate Bayesian Optimization of CoO Nanoparticles for CO2 Hydrogenation Catalysis

Karadaghi, Lanja R.; Williamson, Emily M.; To, Anh T.; Forsberg, Allison P.; Crans, Kyle D.; Perkins, Craig L.; Hayden, Steven C.; LiBretto, Nicole J.; Baddour, Frederick G.; Ruddy, Daniel A.; Malmstadt, Noah; Habas, Susan E.; Brutchey, Richard L.

doi:10.1021/jacs.4c03789

Multivariate Bayesian Optimization of CoO Nanoparticles for CO₂ Hydrogenation Catalysis

Journal Article · Fri May 10 00:00:00 EDT 2024 · Journal of the American Chemical Society

DOI:https://doi.org/10.1021/jacs.4c03789· OSTI ID:2349137

^[1]; ^[1]; ^[2]; ^[1]; Crans, Kyle D. ^[1]; ^[2]; Hayden, Steven C. ^[2]; LiBretto, Nicole J. ^[2]; ^[2]; ^[2]; ^[1]; ^[2]; ^[1]

University of Southern California, Los Angeles, CA (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)

The hydrogenation of CO₂ holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO₂ hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO₂ and assessed for thermocatalytic CO₂ hydrogenation against larger, polydisperse CoO nanoparticles on SiO₂ and a conventionally prepared catalyst. The optimized CoO/SiO₂ catalyst consistently exhibited higher activity and CH₄ selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States); University of Southern California, Los Angeles, CA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Bioenergy Technologies Office (BETO); USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC36-08GO28308; SC0006812

OSTI ID:: 2349137

Alternate ID(s):: OSTI ID: 2370392
OSTI ID: 3013918
OSTI ID: 2356756

Report Number(s):: NREL/JA--5100-89490

Journal Information:: Journal of the American Chemical Society, Journal Name: Journal of the American Chemical Society Journal Issue: 20 Vol. 146; ISSN 0002-7863

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
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Multivariate Bayesian Optimization of CoO Nanoparticles for CO2 Hydrogenation Catalysis

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Multivariate Bayesian Optimization of CoO Nanoparticles for CO₂ Hydrogenation Catalysis