Laboratory Studies of Carbon Kinetic Isotope Effects on the Production Mechanism of Particulate Phenolic Compounds Formed by Toluene Photooxidation: A Tool To Constrain Reaction Pathways
Bibliographic record
Abstract
In this study, we examined compound-specific stable carbon isotope ratios for phenolic compounds in secondary organic aerosol (SOA) formed by photooxidation of isotope-label-free toluene. SOA generated by photooxidation of toluene using a continuous-flow reactor and an 8 m(3) indoor smog chamber was collected on filters, which were extracted with acetonitrile for compound-specific analysis. Eight phenolic compounds were identified in the extracts using a gas chromatograph coupled with a mass spectrometer, and their compound-specific stable carbon isotope ratios were determined using a gas chromatograph coupled with a combustion furnace followed by an isotope ratio mass spectrometer. The majority of products, including methylnitrophenols and methylnitrocatechols, were isotopically depleted by 5-6‰ compared to the initial isotope ratio of toluene, whereas the isotope ratio for 4-nitrophenol remained identical to that of toluene. On the basis of the reaction mechanisms proposed in previous reports, stable carbon isotope ratios of these products were calculated. By comparing the observed isotope ratios with the predicted isotope ratios, we explored possible production pathways for the particulate phenolic compounds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".