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Abstract We develop original flow-based methods to interrogate and manipulate out-of-equilibrium behaviour of ternary fluids systems at the small scale. In particular, we examine droplet and jet formation of ternary fluid systems in coaxial microchannels when an aqueous phase is injected into a solvent-rich oil phase using common fluids, such as ethanol for the aqueous phase, silicone oil for the oil phase and isopropanol for the solvent. Alcohols are often employed to impart oil and water properties with a myriad of practical uses as extractants, antiseptics, wetting agents, emulsifiers or biofuels. Here, we systematically examine the role of alcohol solvents on the hydrodynamic stability of aqueous–oil multiphase flows in square microchannels. Broad variations of flow rates and solvent concentration reveal a variety of intriguing droplet and jet flow regimes in the presence of spontaneous emulsification phenomena and significant mass transfer across the fluid interface. Typical flow patterns include dripping and jetting droplets, phase inversion and dynamic wetting and conjugate jets. Functional relationships are developed to model the evolution of multiphase flow characteristics with solvent concentration. This work provides insights into complex natural phenomena relevant to the application of microfluidic droplet systems to chemical assays as well as fluid measurement and characterisation technologies.
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This content will become publicly available on December 1, 2025
Design automation of microfluidic single and double emulsion droplets with machine learning
Abstract Droplet microfluidics enables kHz screening of picoliter samples at a fraction of the cost of other high-throughput approaches. However, generating stable droplets with desired characteristics typically requires labor-intensive empirical optimization of device designs and flow conditions that limit adoption to specialist labs. Here, we compile a comprehensive droplet dataset and use it to train machine learning models capable of accurately predicting device geometries and flow conditions required to generate stable aqueous-in-oil and oil-in-aqueous single and double emulsions from 15 to 250 μm at rates up to 12000 Hz for different fluids commonly used in life sciences. Blind predictions by our models for as-yet-unseen fluids, geometries, and device materials yield accurate results, establishing their generalizability. Finally, we generate an easy-to-use design automation tool that yield droplets within 3 μm (<8%) of the desired diameter, facilitating tailored droplet-based platforms and accelerating their utility in life sciences.
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- Award ID(s):
- 2027045
- PAR ID:
- 10566582
- Publisher / Repository:
- Nature
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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