ArticleNature communications2024
Design automation of microfluidic single and double emulsion droplets with machine learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Luminescent Nanocomposites of Liquid Crystals and Carbon Dots in Microfluidic Channels: Effects of Walls, Dynamics, and Bioactive Additive.Nanomaterials (Basel, Switzerland) · 2026Article
- Review
- Peroxidase-Mimicking Nanozymes for Rapid Detection of Infectious Diseases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Food-Grade Emulsion Gels as Nutrient Delivery Systems-Standardized Workflow for Fabrication, Characterization, and Application.Gels (Basel, Switzerland) · 2026Review
- ML-automated microfluidic circuit design.Science advances · 2026Article
- Autonomous Bayesian Optimization-Based Control System for Droplet Generation.Small methods · 2025Article
- Recent Advances in the Preparation of Block Copolymer Colloids and Porous Hydrogels Mediated by Emulsion Droplets.Gels (Basel, Switzerland) · 2025Review
- Intelligence prediction of microfluidically prepared nanoparticles.Scientific reports · 2025Article
- Article
- A data driven framework for optimizing droplet microfluidics with residual block and Fourier enhanced networks.Scientific reports · 2025Article
- Continuous FACS Sorting of Double Emulsion Picoreactors with a 3D-Printed Vertical Mixer.Analytical chemistry · 2025Article
- New 3D Spiral Microfluidic Platform Tested for FeMolecules (Basel, Switzerland) · 2025Article
- Artificial Intelligence-Empowered Automated Double Emulsion Droplet Library Generation.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Data-Driven Theoretical Modeling of Centrifugal Step Emulsification and Its Application in Comprehensive Multiscale Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Review
- Continuous FACS sorting of double emulsion picoreactors with a 3D printed vertical mixer.bioRxiv : the preprint server for biology · 2025Article
- A Machine Vision Perspective on Droplet-Based Microfluidics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Component library creation and pixel array generation with micromilled droplet microfluidics.Microsystems & nanoengineering · 2025Article
- Machine Learning-Driven Innovations in Microfluidics.Biosensors · 2024Review
- Microfluidics for foodborne bacteria analysis: Moving toward multiple technologies integration.Biomicrofluidics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.