ArticleACS omega2018
Reinforcement Learning for Dynamic Microfluidic Control.
Article in ACS omega, 2018. 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.
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Who cites it
24 citing papers in PubMed.
- Computational methods for inertial microfluidics: recent advances and future perspectives.Microsystems & nanoengineering · 2025Review
- Enhancing Microparticle Separation Efficiency in Acoustofluidic Chips via Machine Learning and Numerical Modeling.Sensors (Basel, Switzerland) · 2025Article
- Article
- Ophthalmic drug discovery and development using artificial intelligence and digital health technologies.NPJ digital medicine · 2025Review
- AI-Enabled Microfluidics for Respiratory Pathogen Detection.Sensors (Basel, Switzerland) · 2025Review
- Artificial intelligence-assisted organoid construction in congenital heart disease: current applications and future prospects.Frontiers in bioengineering and biotechnology · 2025Review
- Organ-on-a-Chip Models-New Possibilities in Experimental Science and Disease Modeling.Biomolecules · 2024Review
- Democratizing Microreactor Technology for Accelerated Discoveries in Chemistry and Materials Research.Micromachines · 2024Review
- Design automation of microfluidic single and double emulsion droplets with machine learning.Nature communications · 2024Article
- Review
- Advances in Integration, Wearable Applications, and Artificial Intelligence of Biomedical Microfluidics Systems.Micromachines · 2023Review
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- Deep learning with microfluidics for on-chip droplet generation, control, and analysis.Frontiers in bioengineering and biotechnology · 2023Review
- Computer vision meets microfluidics: a label-free method for high-throughput cell analysis.Microsystems & nanoengineering · 2023Review
- Organ-on-a-chip meets artificial intelligence in drug evaluation.Theranostics · 2023Review
- Advancing microfluidic diagnostic chips into clinical use: a review of current challenges and opportunities.Lab on a chip · 2022Review
- Machine learning for microfluidic design and control.Lab on a chip · 2022Review
- Integration of reinforcement learning to realize functional variability of microfluidic systems.Biomicrofluidics · 2022Article
- Manipulation of free-floating objects using Faraday flows and deep reinforcement learning.Scientific reports · 2022Article
- Investigation of Different Free Image Analysis Software for High-Throughput Droplet Detection.ACS omega · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent years have witnessed an explosion in the application of microfluidic techniques to a wide variety of problems in the chemical and biological sciences. Despite the many considerable advantages that microfluidic systems bring to experimental science, microfluidic platforms often exhibit inconsistent system performance when operated over extended timescales. Such variations in performance are because of a multiplicity of factors, including microchannel fouling, substrate deformation, temperature and pressure fluctuations, and inherent manufacturing irregularities. The introduction and integration of advanced control algorithms in microfluidic platforms can help mitigate such inconsistencies, paving the way for robust and repeatable long-term experiments. Herein, two state-of-the-art reinforcement learning algorithms, based on Deep Q-Networks and model-free episodic controllers, are applied to two experimental "challenges," involving both continuous-flow and segmented-flow microfluidic systems. The algorithms are able to attain superhuman performance in controlling and processing each experiment, highlighting the utility of novel control algorithms for automated high-throughput microfluidic experimentation.
Identifiers
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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.