The Effect of Dissolving Salts in Water Sprays Used for Quenching a Hot Surface: Part 1—Boiling of Single Droplets
Bibliographic record
Abstract
We conducted experiments on the effect of dissolving three different salts sodium chloride (NaCl), sodium sulfate Na2SO4 and magnesium sulfate MgSO4 in water droplets boiling on a hot stainless steel surface. Substrate temperatures were varied from 90°C to 220°C. We photographed droplets as they evaporated, and recorded their evaporation time. At surface temperatures that were too low to initiate nucleate boiling all three salts were found to reduce droplet evaporation rates because they lower the vapor pressure of water. In the nucleate boiling regime, low concentrations (<0.1 mol/l) of Na2SO4 and MgSO4 enhanced heat transfer because they prevented coalescence of vapor bubbles and produced foaming in the droplet, significantly reducing droplet lifetimes. Increasing the salt concentration further did not produce a corresponding increase in droplet boiling rate. Dissolved salts prevent bubble coalescence because they increase surface tension and stabilize the liquid film separating bubbles, and because electric charge that accumulates on the surfaces of bubbles produces a repulsive force, preventing them from approaching each other. Na2SO4 and MgSO4, which have high ionic strengths, produced a large amount of foaming in droplets and increased their boiling rate significantly. NaCl, which has low ionic strength, had little effect on droplet boiling.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".