自动化
微流控
工作流程
快速成型
公制(单位)
理论(学习稳定性)
计算机科学
质量(理念)
接口(物质)
工程类
工艺工程
系统工程
控制工程
纳米技术
机械工程
材料科学
并行计算
认识论
数据库
机器学习
气泡
最大气泡压力法
哲学
运营管理
作者
David McIntyre,Ali Lashkaripour,Diana Arguijo,Polly M. Fordyce,Douglas Densmore
出处
期刊:Lab on a Chip
[The Royal Society of Chemistry]
日期:2023-01-01
卷期号:23 (23): 4997-5008
被引量:1
摘要
Droplet generation is a fundamental component of droplet microfluidics, compartmentalizing biological or chemical systems within a water-in-oil emulsion. As adoption of droplet microfluidics expands beyond expert labs or integrated devices, quality metrics are needed to contextualize the performance capabilities, improving the reproducibility and efficiency of operation. Here, we present two quality metrics for droplet generation: performance versatility, the operating range of a single device, and stability, the distance of a single operating point from a regime change. Both metrics were characterized in silico and validated experimentally using machine learning and rapid prototyping. These metrics were integrated into a design automation workflow, DAFD 2.0, which provides users with droplet generators of a desired performance that are versatile or flow stable. Versatile droplet generators with stable operating points accelerate the development of sophisticated devices by facilitating integration of other microfluidic components and improving the accuracy of design automation tools.
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