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Covalency competition dominates the water oxidation structure–activity relationship on spinel oxides

An Author Correction to this article was published on 04 November 2020

This article has been updated


Spinel oxides have attracted growing interest over the years for catalysing the oxygen evolution reaction (OER) due to their efficiency and cost-effectiveness, but fundamental understanding of their structure–property relationships remains elusive. Here we demonstrate that the OER activity on spinel oxides is intrinsically dominated by the covalency competition between tetrahedral and octahedral sites. The competition fabricates an asymmetric MT−O−MO backbone where the bond with weaker metal–oxygen covalency determines the exposure of cation sites and therefore the activity. Driven by this finding, a dataset with more than 300 spinel oxides is computed and used to train a machine-learning model for screening the covalency competition in spinel oxides, with a mean absolute error of 0.05 eV. [Mn]T[Al0.5Mn1.5]OO4 is predicted to be a highly active OER catalyst and subsequent experimental results confirm its superior activity. This work sets mechanistic principles of spinel oxides for water oxidation, which may be extendable to other applications.

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Fig. 1: Patterning OER mechanisms on spinel oxides based on the DOS.
Fig. 2: Relationship between OER activity and the covalency competition in spinel oxides.
Fig. 3: Machine-learning approach for fast screening the covalency competition in spinel oxides.
Fig. 4: Experimental analysis of the synthesized spinel Al0.5Mn2.5O4.

Data availability

The data supporting the findings of this study are available within the article and its Supplementary Information. Additional data are available from the corresponding authors on reasonable request.

Code availability

The machine-learning codes for making the covalency competition prediction are available at

Change history

  • 04 November 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.


  1. 1.

    Armstrong, R. C. et al. The frontiers of energy. Nat. Energy 1, 15020 (2016).

    Google Scholar 

  2. 2.

    Lewis, N. S. & Nocera, D. G. Powering the planet: chemical challenges in solar energy utilization. Proc. Natl Acad. Sci. USA 103, 15729–15735 (2006).

    CAS  Google Scholar 

  3. 3.

    Staffell, I. et al. The role of hydrogen and fuel cells in the global energy system. Energy Environ. Sci. 12, 463–491 (2019).

    CAS  Google Scholar 

  4. 4.

    Dau, H. et al. The mechanism of water oxidation: from electrolysis via homogeneous to biological catalysis. ChemCatChem 2, 724–761 (2010).

    CAS  Google Scholar 

  5. 5.

    Lee, Y., Suntivich, J., May, K. J., Perry, E. E. & Shao-Horn, Y. Synthesis and activities of rutile IrO2 and RuO2 nanoparticles for oxygen evolution in acid and alkaline solutions. J. Phys. Chem. Lett. 3, 399–404 (2012).

    CAS  Google Scholar 

  6. 6.

    Seitz, L. C. et al. A highly active and stable IrOx/SrIrO3 catalyst for the oxygen evolution reaction. Science 353, 1011–1014 (2016).

    CAS  Google Scholar 

  7. 7.

    Reier, T., Oezaslan, M. & Strasser, P. Electrocatalytic oxygen evolution reaction (OER) on Ru, Ir, and Pt catalysts: a comparative study of nanoparticles and bulk materials. ACS Catal. 2, 1765–1772 (2012).

    CAS  Google Scholar 

  8. 8.

    Yang, L. et al. Efficient oxygen evolution electrocatalysis in acid by a perovskite with face-sharing IrO6 octahedral dimers. Nat. Commun. 9, 5236 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Li, H. et al. Metal–oxygen hybridization determined activity in spinel-based oxygen evolution catalysts: a case study of ZnFe2–xCrxO4. Chem. Mater. 30, 6839–6848 (2018).

    CAS  Google Scholar 

  10. 10.

    Zhao, Q., Yan, Z., Chen, C. & Chen, J. Spinels: controlled preparation, oxygen reduction/evolution reaction application, and beyond. Chem. Rev. 117, 10121–10211 (2017).

    CAS  Google Scholar 

  11. 11.

    Chen, J. Y., Miller, J. T., Gerken, J. B. & Stahl, S. S. Inverse spinel NiFeAlO4 as a highly active oxygen evolution electrocatalyst: promotion of activity by a redox-inert metal ion. Energy Environ. Sci. 7, 1382–1386 (2014).

    CAS  Google Scholar 

  12. 12.

    Zhou, Y. et al. Enlarged Co–O covalency in octahedral sites leading to highly efficient spinel oxides for oxygen evolution reaction. Adv. Mater. 30, 1802912 (2018).

    Google Scholar 

  13. 13.

    Duan, Y. et al. Mastering surface reconstruction of metastable spinel oxides for better water oxidation. Adv. Mater. 31, 1807898 (2019).

    Google Scholar 

  14. 14.

    Grimaud, A. et al. Double perovskites as a family of highly active catalysts for oxygen evolution in alkaline solution. Nat. Commun. 4, 2439 (2013).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Grimaud, A. et al. Activating lattice oxygen redox reactions in metal oxides to catalyse oxygen evolution. Nat. Chem. 9, 457–465 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Yang, C. & Grimaud, A. Factors controlling the redox activity of oxygen in perovskites: from theory to application for catalytic reactions. Catalysts 7, 149 (2017).

    Google Scholar 

  17. 17.

    Goodenough, J. B. & Loeb, A. L. Theory of ionic ordering, crystal distortion, and magnetic exchange due to covalent forces in spinels. Phys. Rev. 98, 391–408 (1955).

    CAS  Google Scholar 

  18. 18.

    Rong, X., Parolin, J. & Kolpak, A. M. A fundamental relationship between reaction mechanism and stability in metal oxide catalysts for oxygen evolution. ACS Catal. 6, 1153–1158 (2016).

    CAS  Google Scholar 

  19. 19.

    Zhou, Y. et al. Superexchange effects on oxygen reduction activity of edge‐sharing [CoxMn1−xO6] octahedra in spinel oxide. Adv. Mater. 30, 1705407 (2018).

    Google Scholar 

  20. 20.

    Wei, C. et al. Cations in octahedral sites: a descriptor for oxygen electrocatalysis on transition‐metal spinels. Adv. Mater. 29, 1606800 (2017).

    Google Scholar 

  21. 21.

    Seh, Z. W. et al. Combining theory and experiment in electrocatalysis: insights into materials design. Science 355, eaad4998 (2017).

    PubMed  Google Scholar 

  22. 22.

    Lee, Y.-L., Kleis, J., Rossmeisl, J., Shao-Horn, Y. & Morgan, D. Prediction of solid oxide fuel cell cathode activity with first-principles descriptors. Energy Environ. Sci. 4, 3966–3970 (2011).

    CAS  Google Scholar 

  23. 23.

    Suntivch, J., May, K. J., Gasteiger, H. A., Goodenough, J. B. & Shao-Horn, Y. A perovskite oxide optimized for oxygen evolution catalysis from molecular orbital principles. Science 334, 1383–1385 (2011).

    Google Scholar 

  24. 24.

    Suntivch, J., Perry, E. E., Gasteiger, H. A. & Shao-Horn, Y. The influence of the cation on the oxygen reduction and evolution activities of oxide surfaces in alkaline electrolyte. Electrocatalysis 4, 49–55 (2013).

    Google Scholar 

  25. 25.

    Yang, C., Fontaine, O., Tarascon, J. & Grimaud, A. Chemical recognition of active oxygen species on the surface of oxygen evolution reaction electrocatalysts. Angew. Chem. Int. Ed. 56, 8652–8656 (2017).

    CAS  Google Scholar 

  26. 26.

    Garcia, A. C., Touzalin, T., Nieuwland, C., Perini, N. & Koper, M. Enhancement of oxygen evolution activity of nickel oxyhydroxide by electrolyte alkali cations. Angew. Chem. Int. Ed. 58, 12999–13003 (2019).

    CAS  Google Scholar 

  27. 27.

    Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D. & Doyle, A. G. Predicting reaction performance in C–N cross-coupling using machine learning. Science 360, 186–190 (2018).

    CAS  PubMed  Google Scholar 

  28. 28.

    Gawande, M. B. et al. Cu and Cu-based nanoparticles: synthesis and applications in catalysis. Chem. Rev. 116, 3722–3811 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Sun, S. et al. Shifting oxygen charge towards octahedral metal: a way to promote water oxidation on cobalt spinel oxides. Angew. Chem. 131, 6103–6108 (2019).

    Google Scholar 

  30. 30.

    Dong, R. et al. Enhanced supercapacitor performance of Mn3O4 nanocrystals by doping transition-metal ions. ACS Appl. Mater. Inter. 5, 9508–9516 (2013).

    CAS  Google Scholar 

  31. 31.

    Liao, H. et al. A multisite strategy for enhancing the hydrogen evolution reaction on a nano‐Pd surface in alkaline media. Adv. Energy Mater. 7, 1701129 (2017).

    Google Scholar 

  32. 32.

    Laffont, L. & Gibot, P. High resolution electron energy loss spectroscopy of manganese oxides: application to Mn3O4 nanoparticles. Mater. Charact. 61, 1268–1273 (2010).

    CAS  Google Scholar 

  33. 33.

    Wei, C. & Xu, Z. J. The comprehensive understanding of 10 mA cm−2 geo as an evaluation parameter for electrochemical water splitting. Small Methods 2, 1800168 (2018).

    Google Scholar 

  34. 34.

    Sun, S., Li, H. & Xu, Z. J. Impact of surface area in evaluation of catalyst activity. Joule 2, 1024–1027 (2018).

    Google Scholar 

  35. 35.

    Jung, S., McCrory, C. C., Ferrer, I. M., Peters, J. C. & Jaramillo, T. F. Benchmarking nanoparticulate metal oxide electrocatalysts for the alkaline water oxidation reaction. J. Mater. Chem. A 4, 3068–3076 (2016).

    CAS  Google Scholar 

  36. 36.

    Wei, C. et al. Approaches for measuring the surface areas of metal oxide electrocatalysts for determining their intrinsic electrocatalytic activity. Chem. Soc. Rev. 48, 2518–2534 (2019).

    CAS  Google Scholar 

  37. 37.

    Stoerzinger, K. A., Qiao, L., Biegalski, M. D. & Shao-Horn, Y. Orientation-dependent oxygen evolution activities of rutile IrO2 and RuO2. J. Phys. Chem. Lett. 5, 1636–1641 (2014).

    CAS  Google Scholar 

  38. 38.

    Hong, W. T. et al. Toward the rational design of non-precious transition metal oxides for oxygen electrocatalysis. Energy Environ. Sci. 8, 1404–1427 (2015).

    CAS  Google Scholar 

  39. 39.

    Diaz-Morales, O., Ledezma-Yanez, I., Koper, M. & Calle-Vallejo, F. Guidelines for the rational design of Ni-based double hydroxide electrocatalysts for the oxygen evolution reaction. ACS Catal. 5, 5380–5387 (2015).

    CAS  Google Scholar 

  40. 40.

    Görlin, M. et al. Tracking catalyst redox states and reaction dynamics in Ni–Fe oxyhydroxide oxygen evolution reaction electrocatalysts: the role of catalyst support and electrolyte pH. J. Am. Chem. Soc. 139, 2070–2082 (2017).

    Google Scholar 

  41. 41.

    Abild-Pedersen, F. et al. Scaling properties of adsorption energies for hydrogen-contaning molecules on transition-metal surfaces. Phys. Rev. Lett. 99, 016105 (2007).

    CAS  Google Scholar 

  42. 42.

    Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996).

    CAS  Google Scholar 

  43. 43.

    Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–armorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251–14269 (1994).

    CAS  Google Scholar 

  44. 44.

    Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953–17979 (1994).

    Google Scholar 

  45. 45.

    Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996).

    CAS  PubMed  Google Scholar 

  46. 46.

    Dudarev, S., Botton, G., Savrasov, S., Humphreys, C. & Sutton, A. Electron-energy-loss spectra and the structural stability of nickel oxide: an LSDA+U study. Phys. Rev. B 57, 1505–1509 (1998).

    CAS  Google Scholar 

  47. 47.

    Monkhorst, H. J. & Pack, J. D. Special points for Brillouin-zone integrations. Phys. Rev. B 13, 5188–5192 (1976).

    Google Scholar 

  48. 48.

    Blöchl, P. E., Jepsen, O. & Andersen, O. K. Improved tetrahedron method for Brillouin-zone integrations. Phys. Rev. B 49, 16223–16233 (1994).

    Google Scholar 

  49. 49.

    Anaconda Software Distribution. Computer software. v.2-2.4.0. (Anaconda, 2016);

  50. 50.

    Svetnik, V. et al. Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comp. Sci. 43, 1947–1958 (2003).

    CAS  Google Scholar 

  51. 51.

    Jha, D. et al. Elemnet: deep learning the chemistry of materials from only elemental composition. Sci. Rep. 8, 17593 (2018).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Islam, M. et al. Study on the electrochemical reaction mechanism of NiFe2O4 as a high-performance anode for Li-ion batteries. ACS Appl. Mater. Inter. 9, 14833–14843 (2017).

    CAS  Google Scholar 

  53. 53.

    Du, Y. et al. XAFCA: a new XAFS beamline for catalysis research. J. Synchrotron Rad. 22, 839–843 (2015).

    CAS  Google Scholar 

  54. 54.

    Ravel, B. & Newville, M. ATHENA, ARTEMIS, HEPHAESTUS: data analysis for X-ray absorption spectroscopy using IFEFFIT. J. Synchrotron Rad. 12, 537–541 (2005).

    CAS  Google Scholar 

  55. 55.

    Yu, X., Diao, C., Venkatesan, T., Breese, M. & Rusydi, A. A soft X-ray-ultraviolet (SUV) beamline and diffractometer for resonant elastic scattering and ultraviolet-vacuum ultraviolet reflectance at the Singapore synchrotron light source. Rev. Sci. Instrum. 89, 113113 (2018).

    CAS  Google Scholar 

  56. 56.

    Wei, C. et al. Recommended practices and benchmark activity for hydrogen and oxygen electrocatalysis in water splitting and fuel cells. Adv. Mater. 31, 1806296 (2019).

    Google Scholar 

  57. 57.

    Mishra, R. K. & Thomas, G. Surface energy of spinel. J. Appl. Phys. 48, 4576–4580 (1977).

    CAS  Google Scholar 

  58. 58.

    Farragher, A. Surface vacancies in close packed crystal structures. Adv. Colloid Interface Sci. 11, 3–41 (1979).

    CAS  Google Scholar 

  59. 59.

    Roy, C. et al. Impact of nanoparticle size and lattice oxygen on water oxidation on NiFeOxHy. Nat. Catal. 1, 820 (2018).

    CAS  Google Scholar 

  60. 60.

    Friebel, D. et al. Identification of highly active Fe sites in (Ni, Fe)OOH for electrocatalytic water splitting. J. Am. Chem. Soc. 137, 1305–1313 (2015).

    CAS  PubMed  Google Scholar 

  61. 61.

    Nørskov, J. K. et al. Origin of the overpotential for oxygen reduction at a fuel-cell cathode. J. Phys. Chem. B 108, 17886–17892 (2004).

    Google Scholar 

  62. 62.

    Man, I. C. et al. Universality in oxygen evolution electrocatalysis on oxide surfaces. ChemCatChem 3, 1159–1165 (2011).

    CAS  Google Scholar 

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This work was supported by Singapore Ministry of Education Tier 2 Grant (MOE-2018-T2-2-027) and the Singapore National Research Foundation under its Campus for Research Excellence And Technological Enterprise (CREATE) programme. We thank the Facility for Analysis, Characterization, Testing, and Simulation (FACTS) in Nanyang Technological University. This research used resources of the National Synchrotron Light Source II, a US Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under contract no. DE-SC0012704. We also appreciate the XAS measurements from SSLS, soft X-ray and ultraviolet beamline. Y.S. and Z.X. thank A. Lapkin (University of Cambridge) for helpful discussion on machine-learning concepts and thank L. Zeng (Southern University of Science and Technology) for helpful discussion on catalyst performance. H.Z. gives thanks for the support from ITC via the Hong Kong Branch of National Precious Metals Material (NPMM) Engineering Research Center, and the start-up grant (project no. 9380100) and grants (project no. 9610478 and 1886921) in City University of Hong Kong.

Author information




Z.J.X. and Y.S. proposed the research. Y.S., H.L. and Z.J.X. designed the experiments. Y.S. conducted DFT modelling and simulations. H.L. established the mathematical approach. H.L., J.W., S.S., B.C. and S.J.H.O. carried out the experiments. S.X., C.D., Y.D., J.W., J.O.W., Y.S. and H.L. conducted XAS characterizations. Y.S. wrote the manuscript. H.L., S.X., Y.D., M.B.H.B., S.L., H.Z. and Z.J.X. performed the analysis and revised the manuscript.

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Correspondence to Zhichuan J. Xu.

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Supplementary information

Supplementary Information

Supplementary Tables 1–11 and Figs. 1–12.

Supplementary Data 1

Atomic coordinates of the calculated bulk spinels.

Supplementary Data 2

Atomic coordinates of the OER intermediates.

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Sun, Y., Liao, H., Wang, J. et al. Covalency competition dominates the water oxidation structure–activity relationship on spinel oxides. Nat Catal 3, 554–563 (2020).

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