pH Affects the Spontaneous Formation of H<sub>2</sub>O<sub>2</sub> at the Air–Water Interfaces
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Recent studies have shown that the air–water interface of aqueous microdroplets is a source of OH radicals and hydrogen peroxide in the atmosphere. Several parameters such as droplet size, salt, and organic content have been suggested to play key roles in the formation of these oxidants. In this study, we focus on the effect of acidity on the spontaneous interfacial hydrogen peroxide formation of salt-containing droplets. Na 2 SO 4, NaCl, and NaBr bulk solutions, at the range of pH 4 to 9.5, were nebulized, using ultra high-purity N 2 /O 2 (80%/20%), and H 2 O 2 was measured in the collected droplets. All of the experiments were performed in T = 292 ± 1 K and humidity levels of 90 ± 2%. For Na 2 SO 4 and NaCl, the H 2 O 2 concentration was increased by ∼40% under alkaline conditions, suggesting that OH – enriched environments promote its production. When CO 2 was added in the ultrapure air, H 2 O 2 was observed to be lower at higher pH. This suggests that dissolved CO 2 can initiate reactions with OH radicals and electrons, impacting the interfacial H 2 O 2 production. H 2 O 2 formation in NaBr droplets did not display any dependence on the pH or the bath gas, showing that secondary reactions occur at the interface in the presence of Br –, which acts as an efficient interfacial source of electrons.
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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) | 0.000 | 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) | 0.002 | 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".