Uptake of acetone, ethanol and benzene to snow and ice: effects of surface area and temperature
Bibliographic record
Abstract
The interactions of gas-phase acetone, ethanol and benzene with smooth ice films and artificial snow have been studied. In one technique, the snow is packed into a cylindrical column and inserted into a low-pressure flow reactor coupled to a chemical-ionization mass spectrometer for gas-phase analysis. At 214 and 228 K, it is found for acetone and ethanol that the adsorbed amounts per surface area match those for adsorption to thin films of ice formed by freezing liquid water, when the specific surface area of the snow (as determined from Kr adsorption at 77 K) and the geometric surface area of the ice films are used. This indicates that freezing thin films of water leads to surfaces that are smooth at the molecular level. Experiments performed to test the effect of film growth on ethanol uptake indicate that uptake is independent of ice growth rate, up to 2.4 µm min −1 . In addition, traditional Brunauer–Emmett–Teller (BET) experiments were performed with these gases on artificial snow from 238 to 266.5 K. A transition from a BET type I isotherm indicative of monolayer formation to a BET type II isotherm indicative of multilayer uptake is observed for acetone at T ≥263 K and ethanol at T ≥255 K, arising from solution formation on the ice. When multilayer formation does not occur, as was the case for benzene at T ≤263 K and for acetone at T ≤255 K, the saturated surface coverage increased with increasing temperature, consistent with the quasi-liquid layer affecting adsorption prior to full dissolution/multilayer formation.
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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) | 0.001 | 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) | 0.001 | 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".