Department of Biomedical Engineering, Boston University, Boston, MA, USA.
Biological Design Center, 610 Commonwealth Avenue, Boston, MA, USA.
Nat Commun. 2021 Jan 4;12(1):25. doi: 10.1038/s41467-020-20284-z.
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.
基于液滴的微流控设备在成为生命科学应用中现有筛选平台的廉价替代品方面具有巨大潜力，例如酶发现和早期癌症检测。然而，缺乏对液滴生成的可预测性理解使得基于液滴的平台的工程成为一个迭代和资源密集型的过程。我们提出了一个基于网络的工具 DAFD，它可以预测性能并实现流聚焦液滴发生器的设计自动化。我们利用机器学习算法来预测液滴的直径和速率，平均绝对误差小于 10 μm 和 20 Hz。该工具可以在指定的性能范围内，将所需的直径和速率控制在 4.2%和 11.5%以内。我们证明，DAFD 可以通过社区扩展以支持其他流体组合，而无需广泛的机器学习知识或大规模数据集。该工具将减少对微流控专业知识和设计迭代的需求，并促进微流控在生命科学中的应用。
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