Highly pressurized partially miscible liquid-liquid flow in a micro-T-junction. I. Experimental observations
Bibliographic record
Abstract
This is the first part of a two-part study on a partially miscible liquid-liquid flow (liquid carbon dioxide and deionized water) which is highly pressurized and confined in a microfluidic T-junction. Our main focuses are to understand the flow regimes as a result of varying flow conditions and investigate the characteristics of drop flow distinct from coflow, with a capillary number, Ca_{c}, that is calculated based on the continuous liquid, ranging from 10^{-3} to 10^{-2} (10^{-4} for coflow). Here in part I, we present our experimental observation of drop formation cycle by tracking drop length, spacing, frequency, and after-generation speed using high-speed video and image analysis. The drop flow is chronologically composed of a stagnating and filling stage, an elongating and squeezing stage, and a truncating stage. The common "necking" time during the elongating and squeezing stage (with Ca_{c}∼10^{-3}) for the truncation of the dispersed liquid stream is extended, and the truncation point is subsequently shifted downstream from the T-junction corner. This temporal postponement effect modifies the scaling function reported in the literature for droplet formation with two immiscible fluids. Our experimental measurements also demonstrate the drop speed immediately following their generations can be approximated by the mean velocity from averaging the total flow rate over the channel cross section. Further justifications of the quantitative analysis by considering the mass transfer at the interface of the two partially miscible fluids are provided in part II.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".