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Machine learning enables design automation of microfluidic flow-focusing droplet generation

Author

Listed:
  • Ali Lashkaripour

    (Boston University
    Biological Design Center)

  • Christopher Rodriguez

    (Massachusetts Institute of Technology)

  • Noushin Mehdipour

    (Biological Design Center
    Boston University)

  • Rizki Mardian

    (Biological Design Center
    Boston University)

  • David McIntyre

    (Boston University
    Biological Design Center)

  • Luis Ortiz

    (Biological Design Center
    Boston University)

  • Joshua Campbell

    (Boston University)

  • Douglas Densmore

    (Biological Design Center
    Boston University)

Abstract

Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive understanding of droplet generation makes engineering a droplet-based platform an iterative and resource-intensive process. We present a web-based tool, DAFD, that predicts the performance and enables design automation of flow-focusing droplet generators. We capitalize on machine learning algorithms to predict the droplet diameter and rate with a mean absolute error of less than 10 μm and 20 Hz. This tool delivers a user-specified performance within 4.2% and 11.5% of the desired diameter and rate. We demonstrate that DAFD can be extended by the community to support additional fluid combinations, without requiring extensive machine learning knowledge or large-scale data-sets. This tool will reduce the need for microfluidic expertise and design iterations and facilitate adoption of microfluidics in life sciences.

Suggested Citation

  • Ali Lashkaripour & Christopher Rodriguez & Noushin Mehdipour & Rizki Mardian & David McIntyre & Luis Ortiz & Joshua Campbell & Douglas Densmore, 2021. "Machine learning enables design automation of microfluidic flow-focusing droplet generation," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20284-z
    DOI: 10.1038/s41467-020-20284-z
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