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Machine Learning, Density Functional Theory, and Experiments to Understand the Photocatalytic Reduction of CO<sub>2</sub> on CuPt/TiO<sub>2</sub>

Vaidish Sumaria, Takat B. Rawal, Young Feng Li, David E. Sommer, Jake Vikoren, Robert J. Bondi, Matthias Rupp, Amrit Prasad, Deeptanshu Prasad

2024The Journal of Physical Chemistry C20 citationsDOI

Abstract

The photoconversion of CO 2 to hydrocarbons is a sustainable route for its transformation into value-added compounds, which is crucial to mitigating energy and climate crises. CuPt nanoparticles on TiO 2 surfaces have been reported to show promising photoconversion efficiencies. For further progress, a mechanistic understanding of the catalytic properties of these CuPt/TiO 2 systems is vital. Here, we employ ab initio calculations, machine learning, and photocatalysis experiments to understand the photocatalytic reduction of CO 2 on CuPt/TiO 2 . We explore the configurational space of the CO 2 @CuPt/TiO 2 systems and examine their structures and energetics. We find that the CuPt/TiO 2 interface plays a key role in determining CO 2 activation and, thus, the conversion to hydrocarbons. The interface stabilizes *CO and other intermediates containing CH groups, thus facilitating a higher activity and selectivity for methane. A bias-corrected machine-learning interatomic potential trained on density functional theory data enables the efficient exploration of the potential energy surfaces of numerous CO 2 @CuPt/TiO 2 configurations using basin-hopping Monte Carlo simulations, greatly accelerating the study of these photocatalyst systems. Our simulations show that CO 2 preferentially adsorbs at the interface, with a C atom bonded to a Pt site and one O atom occupying an O-vacancy site. The interface also promotes the formation of *CH and *CH 2 intermediates. For confirmation, we synthesize CuPt/TiO 2 samples with various compositions, analyze their morphologies and compositions using scanning electron microscopy and energy-dispersive X-ray spectroscopy, and measure their photocatalytic activity. Our computational and experimental findings qualitatively agree and highlight the importance of the interface design for the selective conversion of CO 2 to hydrocarbons.

Topics & Concepts

Density functional theoryPhotocatalysisVacancy defectChemistryAb initioChemical physicsAtom (system on chip)CatalysisNanotechnologyMaterials scienceComputational chemistryCrystallographyComputer scienceBiochemistryEmbedded systemOrganic chemistryAdvanced Photocatalysis TechniquesCopper-based nanomaterials and applicationsCO2 Reduction Techniques and Catalysts
Machine Learning, Density Functional Theory, and Experiments to Understand the Photocatalytic Reduction of CO<sub>2</sub> on CuPt/TiO<sub>2</sub> | Litcius