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Accelerated discovery of CO2 electrocatalysts using active machine learning

Abstract

The rapid increase in global energy demand and the need to replace carbon dioxide (CO2)-emitting fossil fuels with renewable sources have driven interest in chemical storage of intermittent solar and wind energy1,2. Particularly attractive is the electrochemical reduction of CO2 to chemical feedstocks, which uses both CO2 and renewable energy3,4,5,6,7,8. Copper has been the predominant electrocatalyst for this reaction when aiming for more valuable multi-carbon products9,10,11,12,13,14,15,16, and process improvements have been particularly notable when targeting ethylene. However, the energy efficiency and productivity (current density) achieved so far still fall below the values required to produce ethylene at cost-competitive prices. Here we describe Cu-Al electrocatalysts, identified using density functional theory calculations in combination with active machine learning, that efficiently reduce CO2 to ethylene with the highest Faradaic efficiency reported so far. This Faradaic efficiency of over 80 per cent (compared to about 66 per cent for pure Cu) is achieved at a current density of 400 milliamperes per square centimetre (at 1.5 volts versus a reversible hydrogen electrode) and a cathodic-side (half-cell) ethylene power conversion efficiency of 55 ± 2 per cent at 150 milliamperes per square centimetre. We perform computational studies that suggest that the Cu-Al alloys provide multiple sites and surface orientations with near-optimal CO binding for both efficient and selective CO2 reduction17. Furthermore, in situ X-ray absorption measurements reveal that Cu and Al enable a favourable Cu coordination environment that enhances C–C dimerization. These findings illustrate the value of computation and machine learning in guiding the experimental exploration of multi-metallic systems that go beyond the limitations of conventional single-metal electrocatalysts.

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Fig. 1: Screening of Cu and Cu based compounds using computational methods.
Fig. 2: Schematic and characterization of de-alloyed Cu-Al catalyst.
Fig. 3: CO2 electroreduction performance on de-alloyed Cu-Al, porous Cu and deposited Cu catalysts on C-GDL substrates in 1 M KOH electrolytes.
Fig. 4: CO2 electroreduction performance on de-alloyed Cu-Al catalysts on PTFE substrates in alkaline electrolytes at different pH values.

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Source data to generate figures and tables are available from the corresponding authors on reasonable request.

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Code to generate figures and tables is available from the corresponding authors on reasonable request.

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Acknowledgements

This work was supported financially by the Ontario Research Fund Research-Excellence Program, the Natural Sciences and Engineering Research Council (NSERC) of Canada, the Canadian Institute for Advanced Research (CIFAR) Bio-Inspired Solar Energy programme, the University of Toronto Connaught programme, and TOTAL American Services. M.Z. thanks the National Natural Science Foundation of China (grant number 91963121), and S. Tolbert from the University of California, Los Angeles for discussions of de-alloying. S.S. thanks the National Key Research and Development Program of China (grant number 2016YFB0700205) and the National Natural Science Foundation of China (grant number U1632273). We thank R. Morris and D. Sinton from the University of Toronto for discussions. We thank C. McCallum, R. Wolowiec, D. Kopilovic, S. Boccia, A. Ip, M. Liu, Y. Pang, M. Askerka, A. Seifitokaldani, T. T. Zhuang and Z. Liang from the University of Toronto, Canada and C.-W. Huang, L.-J. Chen from National Tsing Hua University, Taiwan, for their help during the course of study. We thank the beamline scientists from the Source optimisée de lumière d’énergie intermédiaire du LURE (SOLEIL) Synchrotron in France for performing in situ X-ray absorption analyses. This research used resources of the National Energy Research Scientific Computing Center, a Department of Energy (DOE) Office of Science User Facility supported by the Office of Science of the US Department of Energy under contract number DE-AC02-05CH11231. Computations were performed on the Southern Ontario Smart Computing Innovation Platform (SOSCIP) Consortium’s Blue Gene/Q computing platform. SOSCIP is funded by the Federal Economic Development Agency of Southern Ontario, the Province of Ontario, IBM Canada Ltd, Ontario Centres of Excellence, MITACS and 15 Ontario academic member institutions.

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Contributions

E.H.S. supervised the project. M.Z. and E.H.S. conceived the idea. M.Z. and C.W. designed and carried out the experiments. K.T., Z.Y. and Z.U. performed the machine learning studies. K.T., Z.Y., Z.U., Y.M., Z.W. O.V., P.D.L., M.A., M.Z. and E.H.S. discussed the machine learning results. Y.M., Z.W. O.V., P.D.L., M.A., A.S., F.C., K.T., Z.Y. and Z.U. carried out the DFT simulations. P.B. carried out the Auger electron spectroscopy analyses. S.S. and P.D.L. performed X-ray absorption spectroscopy measurements. C.-S.T. and S.-C.L. carried out the TEM analyses. C.-T.D., A.S.R., C.-S.T., M.A., M.L., A.S., Y.P. and A.I. contributed to the discussion of the results. All authors discussed the results and assisted during manuscript preparation.

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Correspondence to Zachary Ulissi or Edward H. Sargent.

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This file contains Supplementary Methods, which includes Supplementary Figures 1-68 and Supplementary Tables 1-11.

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Zhong, M., Tran, K., Min, Y. et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature 581, 178–183 (2020). https://doi.org/10.1038/s41586-020-2242-8

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