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Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning

Nature Catalysisvolume 1pages531539 (2018) | Download Citation

Abstract

Single-atom catalysts offer high reactivity and selectivity while maximizing utilization of the expensive active metal component. However, they are susceptible to sintering, where single metal atoms agglomerate into thermodynamically stable clusters. Tuning the binding strength between single metal atoms and oxide supports is essential to prevent sintering. We apply density functional theory, together with a statistical learning approach based on least absolute shrinkage and selection operator regression, to identify property descriptors that predict interaction strengths between single metal atoms and oxide supports. Here, we show that interfacial binding is correlated with readily available physical properties of both the supported metal, such as oxophilicity measured by oxide formation energy, and the support, such as reducibility measured by oxygen vacancy formation energy. These properties can be used to empirically screen interaction strengths between metal–support pairs, thus aiding the design of single-atom catalysts that are robust against sintering.

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Acknowledgements

This work was supported by National Science Foundation grant CHE-1505607.

Author information

Author notes

  1. These authors contributed equally: Nolan J. O'Connor, A. S. M. Jonayat.

Affiliations

  1. Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA

    • Nolan J. O’Connor
    • , Michael J. Janik
    •  & Thomas P. Senftle
  2. Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, University Park, PA, USA

    • A. S. M. Jonayat
  3. Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, USA

    • Thomas P. Senftle

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Contributions

N.J.O. completed the DFT calculations and associated data analysis. A.S.M.J. completed the statistical learning analysis. The project idea was conceived by M.J.J. and T.P.S. All authors contributed to writing the manuscript and approved the final version.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Michael J. Janik or Thomas P. Senftle.

Supplementary information

  1. Supporting Information

    Supplementary Figures 1–13; Supplementary Tables 1–11; Supplementary Methods; Supplementary References

  2. Supplementary Data

    The values of all primary descriptors

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DOI

https://doi.org/10.1038/s41929-018-0094-5