Enhanced Reactivity toward Oxidation by Water Vapor: Interactions of Toluene and NO<sub>2</sub> on Hydrated Magnetite Nanoparticles
Bibliographic record
Abstract
In the atmosphere, water vapor affects the interaction of trace gases and particles, influencing key processes including cloud nucleation, radiation, and heterogeneous chemistry. In this study, the effect of water vapor on the reactions of toluene and NO2 on magnetite, a component of atmospheric dust particles, is investigated using a suite of analytical techniques, namely, X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (TOF SIMS). Adsorption isotherms show that water vapor reduces the adsorption of toluene on magnetite. XPS spectra reveal that exposure to water vapor results in limited dissociation and molecular adsorption of water, and partial oxidation of magnetite. When toluene is added, enhanced dissociation of water and oxidation of the magnetite surface are observed, strongly suggesting the importance of intermolecular interactions between water molecules and the interaction of toluene with the H-bonded network of adsorbed water. Upon addition of NO2, enhanced oxidation and NO3 are observed in XPS and TOF SIMS spectra, respectively. In contrast, on oxidized magnetite, less dissociation and sorption of water is observed, and no enhanced oxidation is observed. Our results show that hydrated magnetite surfaces inactive toward further water dissociation can be reactivated depending on the surface chemistry, due to Fe2+. We show that the effect of water vapor on the interaction of toluene and NO2 on magnetite depends on the Fe2+/Fe3+ ratio, which can vary under environmental conditions. Different reactivity of the Fe3O4 in dust can thus be expected, with implications on the fate of pollutants in the atmosphere.
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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".