Binary nucleation rates for ethanol/water mixtures in supersonic Laval nozzles: Analyses by the first and second nucleation theorems
Bibliographic record
Abstract
We performed pressure trace measurements and small angle x-ray scattering measurements to determine the vapor-liquid nucleation rates of EtOH/H2O mixtures including pure EtOH and pure H2O in two supersonic Laval nozzles with different expansion rates. The nucleation rates varied from 0.9 × 10(17) to 16 × 10(17) cm(-3) s(-1) over the temperature range of 210 K to 230 K, EtOH activity range of 0 to 11.6, and H2O activity range of 0 to 124. The first and second nucleation theorems were applied to the nucleation rates to estimate the sizes, compositions, and excess energies of the critical clusters. The critical clusters contained from 4 to 15 molecules for pure H2O and EtOH/H2O clusters, and from 16 to 23 molecules for pure EtOH clusters. Comparing the excess energies of the pure H2O critical clusters with the results of a quantum-chemistry calculation suggested that the pre-factor of the theoretical nucleation rate is almost constant regardless of the monomer concentration. One possible explanation for this result is that cooling of the critical clusters limits the nucleation rate under the highly supersaturated conditions. The results of the analyses also yielded the relation between the surface energy and the composition of the critical clusters, where the latter are predicted to consist only of surface molecules. Applying this relationship to the EtOH/H2O bulk liquid mixtures, we estimated the EtOH mole fraction in the surface layer and found it is higher than that derived from the surface tension based on the Gibbs adsorption equation when the EtOH mole fraction in the liquid is higher than about 0.2 mol/mol. This discrepancy was attributed to the existence of the EtOH depletion layer just below the surface layer of the liquid.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".