An automated system for high-throughput generation and optimization of microdroplets
Bibliographic 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".