Trapping of trace gases by growing ice surfaces including surface‐saturated adsorption
Bibliographic record
Abstract
Key steps leading to the uptake of trace gases into atmospheric cloud ice particles include gas phase diffusion of trace gas molecules toward growing ice crystals and the kinetics of molecular interactions at the ice surfaces. In the case of nitric acid, laboratory studies and airborne field observations indicate uptake in growing ice films and cirrus ice particles above the thermodynamic solubility limit. This implies that uptake of nitric acid molecules on growing ice surfaces (trapping) controls the nitric acid content in ice particles residing in supersaturated air. A previous trapping model is updated to include effects of surface‐saturated adsorption. Exact analytical solutions to the problem are derived to enable the calculation of the amount of vapor trapped for a given ice growth rate, assuming Langmuir‐type adsorption isotherms. Those solutions are provided in terms of trapping efficiencies and equivalent uptake coefficients, net vapor fluxes toward ice crystals or ice films, steady state molar ratios of trapped molecules in the ice phase, and gas‐ice partitioning factors. The trapping model includes the underlying adsorption equilibrium model asymptotically for nongrowing ice particles. Comparisons to laboratory and field measurements of nitric acid uptake are carried out. Observed dependences of uptake as a function of nitric acid partial pressure, ice growth rate, and temperature are reproduced fairly well. Nitric acid molar ratios in cirrus ice are neither controlled purely by adsorption nor purely by gas phase diffusion, underscoring the importance of using the trapping concept to interpret these observations. These results also suggest further mechanisms that enhance the nitric acid content in cirrus ice at the tropical tropopause at very low temperatures. A discussion of potential model improvements outlines the physical conditions in which the updated model describes trapping well and leads to further insight into the physical nature of the trapping process.
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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 | 0.001 |
| 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".