Different photolysis kinetics at the surface of frozen freshwater vs. frozen salt solutions
Bibliographic record
Abstract
Abstract. Reactions at air-ice interfaces can proceed at very different rates than those in aqueous solution, due to the unique disordered region at the ice surface known as the quasi-liquid layer (QLL) . The physical and chemical nature of the surfacial region of ice is greatly affected by solutes such as sodium halide salts. In this work, we studied the effects of sodium chloride and sodium bromide on the photolysis kinetics of harmine, an aromatic organic compound, in aqueous solution and at the surface of frozen salt solutions above the eutectic temperature. In common with other aromatic organic compounds we have studied, harmine photolysis is much faster on ice surfaces than in aqueous solution, but the presence of NaCl or NaBr – which does not affect photolysis kinetics in solution – reduces the photolysis rate on ice. The rate decreases monotonically with increasing salt concentration; at the concentrations found in seawater, harmine photolysis at the surface of frozen salt solutions proceeds at the same rate as in aqueous solution. These results suggest that the brine excluded to the surfaces of frozen salt solutions is a true aqueous solution, and so it may be possible to use aqueous-phase kinetics to predict photolysis rates at sea ice surfaces. This is in marked contrast to the result at the surface of frozen freshwater samples, where reaction kinetics are often not well-described by aqueous-phase processes.
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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 | 0.000 |
| 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".