Specific Ion Properties Induce Spontaneous H<sub>2</sub>O<sub>2</sub> Production at the Air–Water Interface
Bibliographic record
Abstract
Recent studies have reported spontaneous production of OH radicals and H 2 O 2 at the air–water interface of water droplets. However, the mechanism(s) behind this chemistry remain elusive, with the presence of a strong electric field at the interface being considered one reason for this spontaneous chemistry. Here, we provide evidence that in salt-containing aqueous droplets, the amount of oxidant formation is strongly related to the identity and concentration of the ions present in the solution. Anions have a significantly stronger effect on H 2 O 2 formation compared to cations. The effect of the anions’ identity on H 2 O 2 formation follows the Hofmeister series, which describes changes in the solvation properties of a solution due to the presence of ions. We present a quantitative relationship between two Hofmeister parameters and peroxide concentration derived from our experimental results. This link between H 2 O 2 formation and the Hofmeister series suggests that anions disrupt the water structure at the interface, reducing the solvation of OH – anions and promoting their dissociation into OH radicals and free electrons, leading to an increase in H 2 O 2 formation. This study shows that spontaneous formation of H 2 O 2 is driven by the solvation properties at the interface and not necessarily (or exclusively) by the presence of a strong electric field.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".