The Effect of Dissolving Gases or Solids in Water Droplets Boiling on a Hot Surface
Bibliographic record
Abstract
We conducted experiments on the effect of dissolving either a gas (carbon dioxide) or a solid salt (sodium carbonate or sodium bicarbonate) in water droplets boiling on a hot stainless steel surface. Substrate temperatures were varied from 100°C to 300°C. We recorded the boiling of droplets with a video system, and photographed droplet impact using short-duration flash photography. At surface temperatures that were too low to initiate nucleate boiling, dissolved salts were found to reduce the evaporation rate since they lower the vapor pressure of water. Dissolved gas had the opposite effect: it came out of solution and formed bubbles in the liquid, enhancing evaporation. In the nucleate boiling regime dissolved carbon dioxide enhanced heat transfer by a small amount. However, sodium carbonate prevented coalescence of vapor bubbles and produced foaming in the droplet, greatly enhancing heat transfer and reducing the droplet lifetime to approximately half that of a pure water drop. Sodium bicarbonate, which decomposes to give carbon dioxide and sodium carbonate when heated, produced an even larger enhancement of heat transfer. When the surface temperature was raised above the Leidenfrost temperature of water, droplets went into film boiling and bounced off the surface following impact. Dissolved carbon dioxide was found to suppress heterogeneous bubble formation in the droplet during impact. However, dissolved salts promoted bubble formation and led to droplet break-up during impact.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".