Bubble Entrapment in Sprayed Films
Bibliographic record
Abstract
A compressed air sprayer was used to spray model paint onto two glass substrates at the same time. Afterwards, one glass substrate was placed on a LED light source and still photographs were taken from the top using a DSLR camera with a timer system. The other substrate was put on a balance to record weight. Pictures and weight measurements were taken at 5 second intervals for 15 minutes. The sprayed film thickness was varied. The pictures were analyzed using ImageJ software. Bubble Count vs. Time, Sauter Mean Diameter (SMD) of Bubbles vs. Time as well as Weight vs. Time was plotted. It was seen that the pace of weight loss was faster for thinner films. The rate of bubble escape also depended on film thickness. It took a longer time for thicker films to lose the bubbles entrapped in them. In the first 30 seconds, large bubbles escaped due to buoyancy forces and afterwards surface-tension driven flows became dominant. There was also a lot of bubble movement in thicker films. The effect of gravity was studied as well. Gravity did not affect the bubble escape rate since a downward facing film had the same bubble count as an upward facing film confirming that bubble motion was not due to buoyancy forces alone. However, the SMD of bubbles in a downward facing film was larger than an upward facing film. Buoyancy is not a factor in bubble escape from the downward facing film and only surface-tension driven flows play a role.
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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 | 0.001 |
| 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".