S. G. K. Calhoun

This paper trains machine learning models to predict microfluidic device geometries and flow conditions for generating stable emulsions, achieving high accuracy and creating a design automation tool.

This paper systematically characterizes double emulsion droplets to predict their size and stability based on flow rates and buffer properties, enabling better design of bioassays.

This paper introduces sdDE-FACS, a method for high-throughput phenotyping and sorting of double emulsion droplets using flow cytometry, achieving >70% single droplet recovery and efficient nucleic acid extraction.