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.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Lashkaripour A,Rodriguez C,Mehdipour N,Mardian R,McIntyre D,Ortiz L,Campbell J,Densmore Ddoi
10.1038/s41467-020-20284-zsubject
Has Abstractpub_date
2021-01-04 00:00:00pages
25issue
1issn
2041-1723pii
10.1038/s41467-020-20284-zjournal_volume
12pub_type
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