Influence of Marangoni stress on the variation in number of coalescence cascade stages
Bibliographic record
Abstract
Abstract The present work is an experimental and theoretical study on surfactant‐laden liquid drop impact on a liquid pool. When the drop breaks into a secondary droplet after impinging, then it is called partial coalescence. If this happens successively in self‐similar manner then it is called coalescence cascade. Three different types of surfactants, cationic, anionic, and non‐ionic, are used as drop fluid and water as the liquid pool. Here we report how the surfactant types and concentrations affect the number of stages in coalescence cascade. The experimental outcome revealed that the number of stages in cascade decreases with increasing surfactant concentration. Also, we determine that drop viscosity, density, and size play a crucial role while comparing the stages of cascade among three types of surfactants. We also perform scaling analysis to determine the contribution of inertial and surface forces in the cascade. A theoretical analysis using lubrication approximation has also been carried out to justify the experimental observations. The coalescence process is actually triggered by the drainage of entrapped air between the drop and pool. The theoretical analysis reveals that the faster air drainage rate and acceleration induces a strong Marangoni stress for necking and quick pinch off. Finally, it is shown that Marangoni flow, originated due to the surface tension difference between the drop and pool, is responsible for partial coalescence and a number of coalescence stages in cascade.
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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 | 0.002 |
| 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 | 0.001 | 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".