IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-56136-x.html
   My bibliography  Save this article

Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning

Author

Listed:
  • Hao Xu

    (Peking University Shenzhen Graduate School
    Peking University
    Eastern Institute of Technology)

  • Wenchao Wu

    (Peking University Shenzhen Graduate School
    Peking University)

  • Yuntian Chen

    (Eastern Institute of Technology
    Eastern Institute of Technology)

  • Dongxiao Zhang

    (Eastern Institute of Technology)

  • Fanyang Mo

    (Peking University Shenzhen Graduate School
    Peking University
    Peking University Shenzhen Graduate School
    Peking University Shenzhen Graduate School)

Abstract

In chemistry, empirical paradigms prevail, especially within the realm of chromatography, where the selection of separation conditions frequently relies on the chemist’s experience. However, the underlying rationale for such experiential knowledge has not been established or analysed. This study explicitly elucidates how chemists use thin-layer chromatography (TLC) to determine column chromatography (CC) conditions, employing statistical analysis and machine learning techniques. An experimental dataset of the CC is generated from the automatic platform developed in this study. On this basis, an “artificial intelligence (AI) experience” is generated through a knowledge discovery framework, where the relationship between the retardation factor (RF) value from TLC and retention volume from CC is unveiled in the form of explicit equations. These equations demonstrate satisfactory accuracy and generalizability, providing a scientific basis for the selection of the experimental conditions, and contributing to a better understanding of chromatography.

Suggested Citation

  • Hao Xu & Wenchao Wu & Yuntian Chen & Dongxiao Zhang & Fanyang Mo, 2025. "Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56136-x
    DOI: 10.1038/s41467-025-56136-x
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-56136-x
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-025-56136-x?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
    ---><---

    References listed on IDEAS

    as
    1. Xavier Domingo-Almenara & Carlos Guijas & Elizabeth Billings & J. Rafael Montenegro-Burke & Winnie Uritboonthai & Aries E. Aisporna & Emily Chen & H. Paul Benton & Gary Siuzdak, 2019. "The METLIN small molecule dataset for machine learning-based retention time prediction," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hao Xu & Jinglong Lin & Dongxiao Zhang & Fanyang Mo, 2023. "Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    2. Fleming Kretschmer & Jan Seipp & Marcus Ludwig & Gunnar W. Klau & Sebastian Böcker, 2025. "Coverage bias in small molecule machine learning," Nature Communications, Nature, vol. 16(1), pages 1-19, December.
    3. Mingdu Luo & Yandong Yin & Zhiwei Zhou & Haosong Zhang & Xi Chen & Hongmiao Wang & Zheng-Jiang Zhu, 2023. "A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics," Nature Communications, Nature, vol. 14(1), pages 1-15, 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:16:y:2025:i:1:d:10.1038_s41467-025-56136-x. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.