ArticleSmall (Weinheim an der Bergstrasse, Germany)2025
Artificial Intelligence-Empowered Automated Double Emulsion Droplet Library Generation.
Article in Small (Weinheim an der Bergstrasse, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Integration of Artificial Intelligence and Microfluidics for Drug Delivery Applications.Micromachines · 2026Review
- A Review on Micromixers, Microdroplet Generators and Their Integration.Micromachines · 2026Review
- Open-source robotic chip-to-plate interface for high-throughput microfluidic generation of materials libraries.bioRxiv : the preprint server for biology · 2026Article
- Osmotically Tunable Microdroplets Enable Amplification-Free CRISPR Detection of Gene Doping.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Autonomous Bayesian Optimization-Based Control System for Droplet Generation.Small methods · 2025Article
- Artificial Intelligence-Empowered Automated Double Emulsion Droplet Library Generation.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Double emulsions with core-shell structures are versatile materials used in applications such as cell culture, drug delivery, and materials synthesis. A droplet library with precisely controlled dimensions and properties would streamline screening and optimization for specific applications. While microfluidic droplet generation offers high precision, it is typically labor-intensive and sensitive to disturbances, requiring continuous operator intervention. To address these limitations, we present an artificial intelligence (AI)-empowered automated double emulsion droplet library generator. This system integrates a convolutional neural network (CNN)-based object detection model, decision-making, and feedback control algorithms to automate droplet generation and collection. The system monitors droplet generation every 171 ms-faster than a Formula 1 driver's reaction time-ensuring rapid response to disturbances and consistent production of single-core double emulsions. It autonomously generates libraries of 25 distinct monodisperse droplets with user-defined properties. This automation reduces labor and waste, enhances precision, and supports rapid and reliable droplet library generation. We anticipate that this platform will accelerate discovery and optimization in biomedical, biological, and materials research.
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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.