Mechanistic insights into marine boundary layer nucleation: synergistic interactions of typical sulfur, iodine, and nitrogen precursors
Bibliographic record
Abstract
Abstract. Marine new particles have significant impacts on the global atmosphere, yet the key nucleation process underlying their formation remains highly unclear. Given the coexistence of multiple marine nucleating agents, here we explored how typical sulfur-, iodine-, and nitrogen-bearing chemicals, i.e., methanesulfonic acid (MSA), iodic acid (IA), and dimethylamine (DMA), synergistically interact to drive particle nucleation at the molecular level, by high-level quantum chemical calculations and cluster dynamics simulations. The results show that IA, MSA, and DMA can form stable pre-nucleation clusters via intermolecular hydrogen bonding and halogen bonding, with acid-base reactions occurring during their clustering, yielding ion pair formation. The proposed IA–MSA–DMA ternary nucleation is thermodynamically more favorable in regions rich in sulfur and nitrogen but poor in iodine. The cluster formation rate of IA–MSA–DMA ternary system is notably higher than that of any corresponding binary nucleation, showing a synergistic enhancement on rate of 4–8 orders of magnitude. Moreover, this rate even exceeds that of the well-established efficient iodine oxoacids nucleation under sulfur-rich conditions. In polar coastal regions such as Aboa and Marambio, the simulated rates of IA–MSA–DMA nucleation better agree with field measurements compared with the established IA–DMA nucleation. Accordingly, the proposed IA–MSA–DMA nucleation mechanism is expected to be important in the marine boundary layer, helping to explain the missing sources of iodic acid particles, especially in cold polar marine regions. Incorporating this mechanism into the atmospheric modeling can potentially improve aerosol formation simulations and refine climate predictions.
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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".