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 c

SUBMITTER: Lashkaripour A 

PROVIDER: S-EPMC7782806 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature