Suppression in droplet growth kinetics by the addition of organics to sulfate particles
Bibliographic record
Abstract
Abstract Aerosol‐cloud interactions are affected by the rate at which water vapor condenses onto particles during cloud droplet growth. Changes in droplet growth rates can impact cloud droplet number and size distribution. The current study investigated droplet growth kinetics of acidic and neutral sulfate particles which contained various amounts and types of organic compounds, from model compounds (carbonyls) to complex mixtures (α‐pinene secondary organic aerosol and diesel engine exhaust). In most cases, the formed droplet size distributions were shifted to smaller sizes relative to control experiments (pure sulfate particles), due to suppression in droplet growth rates in the cloud condensation nuclei counter. The shift to smaller droplets correlated with increasing amounts of organic material, with the largest effect observed for acidic seed particles at low relative humidity. For all organics incorporated onto acidic particles, formation of high molecular weight compounds was observed, probably by acid‐catalyzed Aldol condensation reactions in the case of carbonyls. To test the reversibility of this process, carbonyl experiments were conducted with acidic particles exposed to higher relative humidity. High molecular weight compounds were not measured in this case and no shift in droplet sizes was observed, suggesting that high molecular weight compounds are the species affecting the rate of water uptake. While these results provide laboratory evidence that organic compounds can slow droplet growth rates, the modeled mass accommodation coefficient of water on these particles (α > 0.1) indicates that this effect is unlikely to significantly affect cloud properties, consistent with infrequent field observations of slower droplet growth rates.
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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) | 0.000 | 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".