Suppression of OH Generation from the Photo-Fenton Reaction in the Presence of α-Pinene Secondary Organic Aerosol Material
Bibliographic record
Abstract
Although Fenton and Photo-Fenton chemistry is thought to be an important source of OH in cloud and fog water, a high dissolved organic content, especially of secondary organic aerosol (SOA) material, may affect the production of OH via this mechanism. The relative production of OH was measured for Fenton and Photo-Fenton reactions with H 2 O 2 and Fenton-like and Photo-Fenton-like reactions with α-pinene ozonolysis SOA material, under cloud water relevant conditions (5 μM iron, 45 μM H 2 O 2, and 1500 μM SOA). It is demonstrated that the generation of OH radicals from Photo-Fenton chemistry can be significantly suppressed by addition of α-pinene SOA material, where the OH yield for solutions containing H 2 O 2 and SOA material together was decreased by a factor of 6 compared to that when only H 2 O 2 was present, likely because of complexation by carboxylic acids (such as pinonic acid). When SOA is examined without additional H 2 O 2 present, OH is generated by Photo-Fenton chemistry but at a rate lower than that for Photo-Fenton chemistry with H 2 O 2 alone. Without taking into account the suppression by SOA material, one may overestimate the generation of OH by Photo-Fenton chemistry. Furthermore, the suppression of Photo-Fenton chemistry in aqueous organic aerosol may be enhanced by a higher SOA material concentration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".