IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-023-44068-3.html
   My bibliography  Save this article

Design automation of microfluidic single and double emulsion droplets with machine learning

Author

Listed:
  • Ali Lashkaripour

    (Stanford University
    Stanford University)

  • David P. McIntyre

    (Boston University
    Boston University)

  • Suzanne G. K. Calhoun

    (Stanford University)

  • Karl Krauth

    (Stanford University)

  • Douglas M. Densmore

    (Boston University
    Boston University
    Boston University)

  • Polly M. Fordyce

    (Stanford University
    Stanford University
    Chan-Zuckerberg Biohub
    Stanford University)

Abstract

Droplet microfluidics enables kHz screening of picoliter samples at a fraction of the cost of other high-throughput approaches. However, generating stable droplets with desired characteristics typically requires labor-intensive empirical optimization of device designs and flow conditions that limit adoption to specialist labs. Here, we compile a comprehensive droplet dataset and use it to train machine learning models capable of accurately predicting device geometries and flow conditions required to generate stable aqueous-in-oil and oil-in-aqueous single and double emulsions from 15 to 250 μm at rates up to 12000 Hz for different fluids commonly used in life sciences. Blind predictions by our models for as-yet-unseen fluids, geometries, and device materials yield accurate results, establishing their generalizability. Finally, we generate an easy-to-use design automation tool that yield droplets within 3 μm (

Suggested Citation

  • Ali Lashkaripour & David P. McIntyre & Suzanne G. K. Calhoun & Karl Krauth & Douglas M. Densmore & Polly M. Fordyce, 2024. "Design automation of microfluidic single and double emulsion droplets with machine learning," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44068-3
    DOI: 10.1038/s41467-023-44068-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-44068-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-44068-3?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. Lauren D. Zarzar & Vishnu Sresht & Ellen M. Sletten & Julia A. Kalow & Daniel Blankschtein & Timothy M. Swager, 2015. "Dynamically reconfigurable complex emulsions via tunable interfacial tensions," Nature, Nature, vol. 518(7540), pages 520-524, February.
    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. Gazendra Shakya & Tao Yang & Yu Gao & Apresio K. Fajrial & Baowen Li & Massimo Ruzzene & Mark A. Borden & Xiaoyun Ding, 2022. "Acoustically manipulating internal structure of disk-in-sphere endoskeletal droplets," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    2. Bradley D. Frank & Saveh Djalali & Agata W. Baryzewska & Paolo Giusto & Peter H. Seeberger & Lukas Zeininger, 2022. "Reversible morphology-resolved chemotactic actuation and motion of Janus emulsion droplets," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Grace C. Thaggard & Kyoung Chul Park & Jaewoong Lim & Buddhima K. P. Maldeni Kankanamalage & Johanna Haimerl & Gina R. Wilson & Margaret K. McBride & Kelly L. Forrester & Esther R. Adelson & Virginia , 2023. "Breaking the photoswitch speed limit," Nature Communications, Nature, vol. 14(1), pages 1-12, 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:15:y:2024:i:1:d:10.1038_s41467-023-44068-3. 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.