Is secondary organic aerosol yield governed by kinetic factors rather than equilibrium partitioning?
Bibliographic record
Abstract
The numerical description of the formation of secondary organic aerosol (SOA) in the atmosphere relies on the use of particle yields, which are often determined in chamber experiments. What is sometimes not appreciated is that such yields (i) can be defined in different ways and (ii) depend on atmospheric conditions. Here we show with the help of hypothetical scenario simulations that the differential SOA yield upon addition of oxidation products to an atmosphere already containing such products and SOA is more relevant in the ambient atmosphere than the commonly used integrative yield from chamber studies. Furthermore, we suggest that the SOA formation scenarios that have been studied so far comprise merely a subset of possible atmospheric situations. In particular, while in the standard scenarios factors such as volatility and aerosol loading are important, scenarios can be envisaged where these factors become less important while the differential yield approaches unity for all oxidation products. Finally, we suggest aerosol growth in the atmosphere should be seen as being determined by a dynamic situation arising from many simultaneously occurring kinetic processes rather than a thermodynamic equilibrium 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".