An experimental study of the coalescence between a drop and an interface in Newtonian and polymeric liquids
Bibliographic record
Abstract
When a water drop falls onto an oil-water interface, the drop usually rests for some time before merging with the water underneath the interface. We report experiments on this process using water- and oil-based Newtonian liquids and polymer solutions, with an emphasis on the non-Newtonian effects. We deduce that the drop surface is immobilized by contaminants pre-existing in the fluids, and find that the rest time scales with the matrix viscosity for Newtonian fluids. The results are compared with lubrication models for film drainage. If the surrounding matrix is a dilute polymer solution, the rest time is identical to that for a matrix of the solvent alone. Further investigation indicates that the polymer molecules have been cleared from the film by surface adsorption. Depending on the fluid properties and drop size, the drop-interface merging may be completed in one shot or through a cascade of partial coalescence. Partial coalescence occurs for an intermediate range of drop sizes; it is arrested by viscosity for smaller drops and by gravity for larger ones. When either the drop or the matrix phase is a polymer solution, viscoelasticity is shown to suppress partial coalescence for smaller drops. This is apparently due to the inhibition of capillary pinch-off which would otherwise produce a secondary drop before the merging is complete.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".