Ethylene oxide (EO) is a major commodity chemical used in the production of materials, industrial solutions, surfactants and consumer goods. EO is manufactured via a catalytic reaction between ethylene and oxygen gas utilizing highly complex catalysts that have evolved over the years primarily through industrial research involving experimental screening of a large array of catalytic, promoter, and stabilizing materials. While those efforts have led to high-performing catalysts containing five or more promoting materials, there remains significant opportunity to improve the catalyst technology. State-of-the art theory, machine learning, spectroscopic and reaction analysis methods will be combined to better understand the role of chlorine, one of the key promoting elements, and identify opportunities for increasing its effectiveness. Higher-performing EO catalysts would improve process energy efficiency, reduce emissions, and promote U.S. competitiveness in a chemical market sector that accounted for $45B in 2016.
Among the many different combinations of promoters reported in commercial EO catalysts, chlorine (Cl) is the most ubiquitous promoter, and its addition to an otherwise unmodified silver (Ag) catalyst leads to the greatest increase in selectivity to EO. Reactant and promoter-induced surface dynamics and reconstruction have long been known to play a critical role in many catalytic reactions. The study combines catalyst synthesis, spectroscopic characterization, reactivity testing, and machine-learning enhanced molecular simulations, to explore the dynamic nature of the Cl-promoted Ag surface under reaction conditions. The bottom-up approach to synthesis and characterization yields itself to several opportunities for reconciling conflicting spectroscopic assignments in the literature, mechanistic proposals for this system, and hypotheses for the mode of action of Cl through the combination of cutting-edge computational and experimental methods. Specifically, the study will utilize efficient computational approaches for modeling dynamic evolution of systems with high configurational complexity. Time-averaged simulated Raman spectra derived from molecular dynamics simulations will be used in conjunction with multi-variate curve resolution of experimental Raman spectra to make molecularly precise assignments for different surface oxygen species and the influence of Cl on their structure. The transient behavior of these systems will also be tied to observations at steady-state operation at conditions relevant for industrial systems. The collaborative nature of the project will provide opportunities for cross-exposure of graduate students from the two research groups to theoretical and experimental methods, thereby teaching skills for effective research collaboration. The methodologies developed in the research and their application will be integrated into graduate courses taught by the co-investigators to demonstrate the importance of embracing complexity of catalytic systems, even within fundamental studies.
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.