(677g) Spectroscopic, Theoretical, and Data Science Insights into Selective Oxygen Species for Ethylene Epoxidation over Ag Supported on ?-Al2O3 Catalysts

Ethylene oxide (EO, C₂H₄O) is an intermediate used in synthesis of pharmaceutical ingredients, detergents, and polymers. Industrially, EO is synthesized through the partial oxidation of ethylene over silver (Ag) supported on low surface area α-Al₂O₃. The main figure of merit for this reaction is the selectivity, which is hindered by total oxidation of ethylene into carbon dioxide (CO₂). Despite the industrial maturity of this reaction, there are still many unanswered questions into the form(s) of selective oxygen(s) involved in this chemistry.¹

In this contribution, we utilize a combination of first principles density functional theory (DFT) modelling augmented by machine learning and in operando surface enhanced Raman spectroscopy² coupled with data science techniques to shed light on the mechanism of ethylene epoxidation. Machine learning augmented DFT modelling reveals that multiple oxygen motifs are stable and co-present under ethylene epoxidation conditions and that ethylene oxide selectivity appears to be higher over motifs with high concentrations of oxygen. In operando SERS on commercial-mimic Ag/Al₂O₃ catalysts under relevant operating conditions provide spectroscopic evidence for the presence of multiple atomic and molecular oxygen species. We have utilized the SERS data to construct a structure-selectivity database that allow us to correlate specific structural motifs to the catalysts performance across a wide range of operating conditions.

References:

1. Pu, T. et al. ACS Catal., 9(12): 10727-10750, and references therein
2. Dix, S.T. and Linic, S. J. Catal., 2021. 396: 32-39.