ArticleScientific reports2019
Learning from droplet flows in microfluidic channels using deep neural networks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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Who cites it
15 citing papers in PubMed.
- Autonomous Bayesian Optimization-Based Control System for Droplet Generation.Small methods · 2025Article
- AI-Enabled Microfluidics for Respiratory Pathogen Detection.Sensors (Basel, Switzerland) · 2025Review
- AI-CMCA: a deep learning-based segmentation framework for capillary microfluidic chip analysis.Scientific reports · 2025Article
- Machine Learning-Driven Innovations in Microfluidics.Biosensors · 2024Review
- Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.ACS sensors · 2024Review
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- Microneedle arrays integrated with microfluidic systems: Emerging applications and fluid flow modeling.Biomicrofluidics · 2023Review
- Deep learning with microfluidics for on-chip droplet generation, control, and analysis.Frontiers in bioengineering and biotechnology · 2023Review
- Machine learning for microfluidic design and control.Lab on a chip · 2022Review
- A perspective on magnetic microfluidics: Towards an intelligent future.Biomicrofluidics · 2022Article
- Intelligent acoustofluidics enabled mini-bioreactors for human brain organoids.Lab on a chip · 2021Article
- Adoption of reinforcement learning for the intelligent control of a microfluidic peristaltic pump.Biomicrofluidics · 2021Article
- Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics.Scientific reports · 2020Article
- Machine learning-enabled multiplexed microfluidic sensors.Biomicrofluidics · 2020Review
- Mixing characterization of binary-coalesced droplets in microchannels using deep neural network.Biomicrofluidics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A non-intrusive method is presented for measuring different fluidic properties in a microfluidic chip by optically monitoring the flow of droplets. A neural network is used to extract the desired information from the images of the droplets. We demonstrate the method in two applications: measurement of the concentration of each component of a water/alcohol mixture, and measurement of the flow rate of the same mixture. A large number of droplet images are recorded and used to train deep neural networks (DNN) to predict the flow rate or the concentration. It is shown that this method can be used to quantify the concentrations of each component with a 0.5% accuracy and the flow rate with a resolution of 0.05 ml/h. The proposed method can in principle be used to measure other properties of the fluid such as surface tension and viscosity.
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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.