RoboDrop: A Multi-Input Multi-Output Control System for On-Demand Manipulation of Microfluidic Droplets Based on Computer Vision Feedback
Bibliographic record
Abstract
Active control of individual picoliter- to nanoliter-sized droplets in a network of microchannels is vital to make droplet microfluidic platform an enabling technology for single-cell or single-particle analysis, which has found application in areas such as advanced manufacturing, material synthesis, life science research, and personalized medicine. The challenge manifests from the coupled dynamics between droplet motions and network inlet pressures, which must be overcome in order to control individual droplets successfully. In this article, we proposed a generalized approach for modeling and controlling droplet position. The model is validated experimentally and used in a series of multi-input multi-output linear-quadratic regulation controllers. The controllers obtain feedback from computer vision and actuate electropneumatic transducers to yield desired droplet movements. The ability to dynamically generate, trap, merge, split, and sort droplets according to real-time user demand is demonstrated with successful experimental results.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".