Reaction Mechanism of Rapid CO Electroreduction to Propylene and Cyclopropane (C₃₊) over Triple Atom Catalysts
Abstract
The carbon monoxide reduction reaction (CORR) toward C2+ and C3+ products such as propylene and cyclopropane can not only reduce anthropogenic emissions of CO and CO2 but also produce value-added organic chemicals for polymer and pharmaceutical industries. Here, we introduce the concept of triple atom catalysts (TACs) that have three intrinsically strained and active metal centers for reducing CO to C3+ products. We applied grand canonical potential kinetics (GCP-K) to screen 12 transition metals (M) supported by nitrogen-doped graphene denoted as M3N7, where M stands for Mn, Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag, Ir, Pt, and Au. We sought catalysts with favorable CO binding, hydrogen binding, and C–C dimerization energetics, identifying Fe3N7 and Ir3N7 as the best candidates. We then studied the entire reaction mechanism from CO to C3H6 and C2H4 as a function of applied potential via, respectively, 12-electron and 8-electron transfer pathways on Fe3N7 and Ir3N7. Density functional theory (DFT) predicts an overpotential of 0.17 VRHE for Fe3N7 toward propylene and an overpotential of 0.42 VRHE toward cyclopropane at 298.15 K and pH = 7. Also, DFT predicts an overpotential of 0.15 VRHE for Ir3N7 toward ethylene. This work provides fundamental insights into the design of advanced catalysts for C2+ and C3+ synthesis at room temperature.
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Acknowledgement
W.A.G. thanks the US National Science Foundation (CBET-2311117) for support. G.H.C. acknowledges financial supports by the General Research Fund (Grant No. 17309620) and Research Grants Council (RGC: T23-713/22-R). W.A.G. and G.H.C. acknowledge support from the Hong Kong Quantum AI Lab, AIR@InnoHK of the Hong Kong Government.
Data Availability
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Grand canonical potential kinetics, density of states, materials and methods, machine learning, and additional figures (PDF)
Conflict of Interest
The authors declare no competing financial interest.
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Additional details
- ISSN
- 1944-8252
- National Science Foundation
- CBET-2311117
- General Research Fund
- 17309620
- University Grants Committee
- T23-713/22-R
- Hong Kong Quantum AI Lab
- AIR@InnoHK