Rapid oxidation of phenolic compounds by O <sub>3</sub> and HO <sup>●</sup> : effects of the air–water interface and mineral dust in tropospheric chemical processes
Bibliographic record
Abstract
Environmental media affect the atmospheric oxidation processes of phenolic compounds (PhCs) released from biomass burning in the troposphere. To address the gaps in experimental research, phenol (Ph), 4-hydroxybenzaldehyde (4-HBA), and vanillin (VL) are chosen as model compounds to investigate their reaction mechanism and kinetics at the air–water (A–W) interface, on TiO 2 mineral aerosols, in the gas phase, and in bulk water using a combination of molecular dynamics simulation and quantum chemical calculations. Of the compounds, Ph was the most reactive one. The occurrence percentages of Ph, 4-HBA, and VL staying at the A–W interface are ∼ 72 %, ∼ 68 %, and ∼ 73 %, respectively. As the size of (TiO 2 ) n clusters increases, the adsorption capacity decreases until n > 4, and beyond this, the capacity remains stable. A–W interface and TiO 2 clusters facilitate Ph and VL reactions initiated by the O 3 and HO ⚫ , respectively. However, oxidation reactions of 4-HBA are little affected by environmental media because of its electron-withdrawing group. The O 3 - and HO ⚫ -initiated reaction rate constant ( k ) values follow the order of A–W Ph > TiO 2 VL > A–W VL > A–W 4-HBA > TiO 2 4-HBA > TiO 2 Ph and TiO 2 VL > A–W Ph > A–W VL > TiO 2 4-HBA > TiO 2 Ph > A–W 4-HBA , respectively. Some byproducts are more harmful than their parent compounds, so they should be given special attention. This work provides key evidence for the rapid oxidation observed in the O 3 /HO ⚫ + PhC experiments at the A–W interface. More importantly, differences in the oxidation of PhCs by different environmental media due to the impact of substituent groups were also identified.
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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".