ArticleBiomicrofluidics2016
An automated system for high-throughput generation and optimization of microdroplets.
Article in Biomicrofluidics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- A Review on Micromixers, Microdroplet Generators and Their Integration.Micromachines · 2026Review
- Microfluidics-based fabrication of cell-laden microgels.Biomicrofluidics · 2020Review
- A Facile Strategy for Visualizing and Modulating Droplet-Based Microfluidics.Micromachines · 2019Article
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
No grant is acknowledged in the PubMed record.
Abstract
Microdroplets have been widely used in various biomedical applications. During droplet generation, parameters are manually adjusted to achieve the desired size of droplets. This process is tedious and time-consuming. In this paper, we present a fully automated system for controlling the size of droplets to optimize droplet generation parameters in a microfluidic flow-focusing device. The developed system employed a novel image processing program to measure the diameter of droplets from recorded video clips and correspondingly adjust the flow rates of syringe pumps to obtain the required diameter of droplets. The system was tested to generate phosphate-buffered saline and 8% polyethylene (glycol) diacrylate prepolymer droplets and regulate its diameters at various flow rates. Experimental results demonstrated that the difference between droplet diameters from the image processing and manual measurement is not statistically significant and the results are consistent over five repetitions. Taking the advantages of the accurate image processing method, the size of the droplets can be optimized in a precise and robust manner via automatically adjusting flow rates by the feedback control. The system was used to acquire quantitative data to examine the effects of viscosity and flow rates. Droplet-based experiments can be greatly facilitated by the automatic droplet generation and optimization system. Moreover, the system is able to provide quantitative data for the modelling and application of droplets with various conditions in a high-throughput way.
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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.