Semi-automated on-demand control of individual droplets with a sample application to a drug screening assay
Bibliographic record
Abstract
Automated control of individual droplets in microfluidic channels offers tremendous potential for applications requiring high accuracy and minimal user involvement. The feasibility of active droplet control has been previously demonstrated with pressure-driven flow control and visual feedback, but the manual operation required to perform droplet manipulations limited the accuracy, repeatability, and throughput. The present study improves upon the aforementioned challenges with a higher-level algorithm capturing the dynamics of droplet motion for a semi-automated control system. With a simple T junction geometry, droplets can now be automatically and precisely controlled on-demand. Specifically, there is ±10% accuracy for droplet generation, ±1.3% monodispersity for 500 μm long droplets and ±4% accuracy for splitting ratios. On-demand merging, mixing, and sorting are also demonstrated as well as the application of a drug screening assay related to neurodegenerative disorders. Overall, this system serves as a foundation for a fully automated system that does not require valves, embedded electrodes, or complex multi-layer fabrication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".