IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v11y2020i1d10.1038_s41467-020-14969-8.html
   My bibliography  Save this article

Determining the adsorption energies of small molecules with the intrinsic properties of adsorbates and substrates

Author

Listed:
  • Wang Gao

    (Jilin University)

  • Yun Chen

    (Jilin University)

  • Bo Li

    (Jilin University)

  • Shan-Ping Liu

    (Jilin University)

  • Xin Liu

    (Jilin University)

  • Qing Jiang

    (Jilin University)

Abstract

Adsorption is essential for many processes on surfaces; therefore, an accurate prediction of adsorption properties is demanded from both fundamental and technological points of view. Particularly, identifying the intrinsic determinants of adsorption energy has been a long-term goal in surface science. Herein, we propose a predictive model for quantitative determination of the adsorption energies of small molecules on metallic materials and oxides, by using a linear combination of the valence and electronegativity of surface atoms and the coordination of active sites, with the corresponding prefactors determined by the valence of adsorbates. This model quantifies the effect of the intrinsic properties of adsorbates and substrates on adsorbate–substrate bonding, derives naturally the well-known adsorption-energy scaling relations, and accounts for the efficiency and limitation of engineering the adsorption energy and reaction energy. All involved parameters are predictable and thus allow the rapid rational design of materials with optimal adsorption properties.

Suggested Citation

  • Wang Gao & Yun Chen & Bo Li & Shan-Ping Liu & Xin Liu & Qing Jiang, 2020. "Determining the adsorption energies of small molecules with the intrinsic properties of adsorbates and substrates," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14969-8
    DOI: 10.1038/s41467-020-14969-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-020-14969-8
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-020-14969-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiaoyun Lin & Xiaowei Du & Shican Wu & Shiyu Zhen & Wei Liu & Chunlei Pei & Peng Zhang & Zhi-Jian Zhao & Jinlong Gong, 2024. "Machine learning-assisted dual-atom sites design with interpretable descriptors unifying electrocatalytic reactions," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14969-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.