ArticleNature communications2021
Machine learning enables design automation of microfluidic flow-focusing droplet generation.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
69 citing papers in PubMed.
- Active Droplet Formation in a Microfluidic Cross-Junction Using Stacked Piezoelectric Actuators.Micromachines · 2026Article
- Biofabrication and artificial intelligence strategies for investigating solid- and fluid-pressure mechanobiology in pancreatic ductal adenocarcinoma.Cancer metastasis reviews · 2026Review
- A Review on Micromixers, Microdroplet Generators and Their Integration.Micromachines · 2026Review
- Article
- Machine Learning-Driven Capillary Microfluidic Design Automation for Programmable Gradient Generation and Antimicrobial Testing.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip.Acta pharmaceutica Sinica. B · 2026Review
- Droplet microfluidics for biomedical applications: emerging trends and future developments.Microsystems & nanoengineering · 2026Review
- ML-automated microfluidic circuit design.Science advances · 2026Article
- CRISPR-on-Chip for Point-of-Care Diagnostics.ACS nano · 2026Review
- Spatiotemporal prediction of sneeze pollutants in enclosed spaces: a CNN-LSTM approach validated by field measurements and CFD-Robotics arm twin system.Frontiers in robotics and AI · 2026Article
- Translational Models for Glioblastoma: Revolutionizing Drug Development and Personalized Medicine through Clinical Insights.Theranostics · 2026Review
- Autonomous Bayesian Optimization-Based Control System for Droplet Generation.Small methods · 2025Article
- Computational methods for inertial microfluidics: recent advances and future perspectives.Microsystems & nanoengineering · 2025Review
- Intelligence prediction of microfluidically prepared nanoparticles.Scientific reports · 2025Article
- HarnessingSensors (Basel, Switzerland) · 2025Review
- Article
- Extending the Range of Sizes of Monodisperse Core-Shell Hydrogel Capsules from Composite Jet Breakup by Combined Electrical and Mechanical Actuation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- AI-Enabled Microfluidics for Respiratory Pathogen Detection.Sensors (Basel, Switzerland) · 2025Review
- Visualizing nexus of porous architecture and reactive transport in heterogeneous catalysis by deep learning computer vision and transfer learning.Nature communications · 2025Article
- A data driven framework for optimizing droplet microfluidics with residual block and Fourier enhanced networks.Scientific reports · 2025Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive understanding of droplet generation makes engineering a droplet-based platform an iterative and resource-intensive process. We present a web-based tool, DAFD, that predicts the performance and enables design automation of flow-focusing droplet generators. We capitalize on machine learning algorithms to predict the droplet diameter and rate with a mean absolute error of less than 10 μm and 20 Hz. This tool delivers a user-specified performance within 4.2% and 11.5% of the desired diameter and rate. We demonstrate that DAFD can be extended by the community to support additional fluid combinations, without requiring extensive machine learning knowledge or large-scale data-sets. This tool will reduce the need for microfluidic expertise and design iterations and facilitate adoption of microfluidics in life sciences.
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Identifiers
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.