Effect of Approach Velocity on Thin Liquid Film Drainage between an Air Bubble and a Flat Solid Surface
Bibliographic record
Abstract
The dynamic drainage process of the liquid film trapped between an air bubble and a flat silica surface over a wide range of hydrodynamic conditions is studied by a newly developed instrument called integrated thin liquid film force apparatus (ITLFFA) under different salt concentrations. The ITLFFA allows the simultaneous measurement of interaction forces and spatiotemporal film thickness with accurate control of bubble approach velocity in a large range of Reynolds number from 0.005 to 135. Our study demonstrates that increasing the bubble approach velocity plays a significant role in the hydrodynamic pressure and fluid flow within the draining film promoting dimple formation and longer drainage time. The drainage time also depends on the competition between the electrical double-layer and van der Waals interactions, which are repulsive in our system, resulting in a flat equilibrium film at the end. The evolution of the draining film is analyzed using the Stokes–Reynolds–Young–Laplace (SRYL) model. Comparisons between theory and experiments indicate that the model captures the essential physical properties of the drainage system. Moreover, the thickness of the first occurrence of the dimple can also be precisely predicted from the bubble approach velocity with a simple analytical expression.
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 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 | 0.000 |
| 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 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".