Influence of metal-mediated aerosol-phase oxidation on secondary organic aerosol formation from the ozonolysis and OH-oxidation of alpha-pinene
Bibliographic record
Abstract
The organic component is the most abundant fraction of atmospheric submicron particles, while the formation mechanisms of secondary organic aerosol (SOA) are not fully understood. The effects of sulfate seed aerosols on SOA formation were investigated with a series of experiments carried out using a 9m(3) smog chamber. The presence of FeSO4 or Fe-2(SO4)(3) seed aerosols decreased SOA yields and increased oxidation levels in both ozonolysis and OH-oxidation of a-pinene compared to that in the presence of ZnSO4 or (NH4)(2)SO4. These findings were explained by metal-mediated aerosol-phase oxidation of organics: reactive radicals were generated on FeSO4 or Fe-2(SO4)(3) seed aerosols and reacted further with the organic mass. This effect would help to explain the high O/C ratios of organics in ambient particles that thus far cannot be reproduced in laboratory and model studies. In addition, the gap in the SOA yields between experiments with different seed aerosols was more significant in OH-oxidation experiments compared to ozonolysis experiments, while the gap in estimated O/C ratios was less obvious. This may have resulted from the different chemical compositions and oxidation levels of the SOA generated in the two systems, which affect the branching ratio of functionalization and fragmentation during aerosol oxidation.
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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".