Aqueous-phase Direct Photolysis of Phenolic Compounds - the Formation of Dimers and Their Contributions to Atmospheric Brown Carbon
Bibliographic record
Abstract
Brown carbon (BrC) aerosols, primarily emitted from biomass burning events such as wildfires, significantly impact the climate by absorbing sunlight and contributing to atmospheric warming. However, the details of BrC photochemical aging are not fully understood. When exposed to UV radiation, the chemical composition and optical properties of BrC can change, influencing atmospheric radiative forcing. Previous studies suggested that water-soluble BrC in clouds and fog initially experiences enhanced light absorption due to phenolic monomers forming dimers. However, the identities of dimers responsible for the enhancement have neither been identified nor quantified. This study investigates the direct photolysis of a few key phenolic compounds present in BrC under UVA and UVB radiation. In particular, vanillin was used as a model compound with an aim to identify and quantify divanillin, the dimer of vanillin, and evaluate its role in photo-enhancement. Using liquid chromatography-mass spectrometry and UV-visible spectroscopy, we confirmed the formation of phenolic dimers during photolysis. Quantitative analysis of vanillin revealed that while phenolic dimers formed under both UVA and UVB conditions, with yields peaking at approximately 10% after 5 minutes, their contribution to enhanced visible-range absorption was minor (about 10%). The lack of direct correlation between dimer formation and photo-enhancement suggests other photo-products are more influential. Indeed, we found that demethylated dimers (dimer-CH2) and other photoproducts may better explain the observed photo-enhancement. These insights can enhance air quality predictions by clarifying BrC transformation mechanisms and highlighting the importance of incorporating a broader range of photo-products into considerations.
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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".