Machine learning enables design automation of microfluidic flow-focusing droplet generation
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 04, 2021
- Source ID
- 10.1038/s41467-020-20284-z
Entities
People
- Ali Lashkaripour
- Christopher Rodriguez
- David McIntyre
- Douglas Densmore
- Joshua D Campbell
- Lizmarie Comenencia Ortiz
- Noushin Mehdipour
- Rizki Mardian
Organizations
- National Science Foundation
- United States Department of Defense
- United States National Library of Medicine