Mechanistic insights into nitric acid-enhanced iodic acid particle nucleation in the upper troposphere and lower stratosphere
Bibliographic record
Abstract
Abstract. In the upper troposphere and lower stratosphere (UTLS), new particles frequently form to seed cloud condensation nuclei (CCN), thereby affecting radiative forcing and global climate. Iodic acid (IA) particles have been widely detected in the UTLS; however, how they form is still largely unknown. Given the abundance of nitric acid (NA) and ammonia (NH3) in the UTLS and their nucleation potential, we explore the influence of NA and NH3 on IA nucleation by quantum chemical calculations and cluster dynamics simulations. The structural analysis indicates that NA and NH3 can cluster with IA via hydrogen bonds, halogen bonds, and electrostatic attractions between ions. The small-sized IA–NA–NH3 clusters have lower free energies than typical sulfuric acid (SA)–NA–NH3 clusters in the upper troposphere, exhibiting greater stability and higher nucleation efficiency. Moreover, the NA-enhanced effect on the established efficient IA–NH3 nucleation is more evident at lower temperatures, especially with richer NA and NH3. In the extremely low-temperature UTLS, the proposed IA–NA–NH3 ternary pathway dominates nucleation, while in the mid troposphere with higher temperatures, the role of NA is minor due to its rapid evaporation. These findings underscore the important role of NA in iodine particle formation in the UTLS, offering mechanistic insights into the missing secondary particle sources.
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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 | 0.001 |
| 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".