Data-Driven Theoretical Modeling of Centrifugal Step Emulsification and Its Application in Comprehensive Multiscale...

Area of Science:

• Microfluidics
• Biochemical Engineering
• Fluid Dynamics

Background:

• Precise droplet generation is essential in microfluidics for handling diverse sample sizes.
• Current droplet microfluidic platforms often rely on empirical methods due to a lack of predictive models.
• This necessitates the development of advanced modeling and experimental techniques.

Purpose of the Study:

• To develop a novel customized assembled centrifugal step emulsifier (CASE).
• To establish a comprehensive theoretical model for predicting droplet size and generation frequency.
• To enable tailored droplet generation for various microfluidic applications.

Main Methods:

• A customized assembled centrifugal step emulsifier (CASE) with a "jigsaw puzzles" design was developed.
• Numerical simulations were employed to analyze fluid dynamics during step emulsification.
• Experimental data and simulation results were used to train and validate a theoretical model.

Main Results:

• A key connection tube was identified as the determinant of droplet size in the CASE.
• A comprehensive theoretical model was established with an average error rate of 4.8% for predicting droplet size and generation frequency.
• The CASE demonstrated all-in-one functionality, including droplet pre-design, generation, manipulation, and on-site detection.

Conclusions:

• The developed predictive model fills a critical gap in droplet microfluidics, enabling precise control over droplet generation.
• The CASE platform facilitates multiscale sample analysis, from nanoscale nucleic acids to microscale bacteria and 3D cell spheroids.
• This work provides valuable guidance for customized droplet generation using centrifugal step emulsifiers, promoting their use in biochemical assays.