Droplet formation in microfluidic T-junction generators operating in the transitional regime. I. Experimental observations
Bibliographic record
Abstract
This is the first part of a two-part study on the generation of droplets at a microfluidic T-junction operating in the transition regime where confinement of the droplet creates a large squeezing pressure that influences droplet formation. In this regime, the operation of the T-junction depends on the geometry of the intersection (height-to-width ratio, inlet width ratio), capillary number, flow ratio, and viscosity ratio of the two phases. Here in paper I we presented our experimental observations through the analysis of high-speed videos of the droplet formation process. Various parameters are tracked during the formation cycle such as the shape of the droplet (penetration depth and neck), interdroplet spacing, production rate, and flow of both phases across several T-junction designs and flow conditions. Generally, the formation process is defined by a two-stage model consisting of an initial filling stage followed by a necking stage. However, video evidence suggests the inclusion of a third stage, which we term the lag stage, at the beginning of the formation process that accounts for the retraction of the interface back into the injection channel after detachment. Based on the observations made in this paper, a model is developed to describe the formation process in paper II, which can be used to understand the design and operation of T-junction generators in the transition regime.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".