Competing reactions of selected atmospheric gases on Fe <sub>3</sub> O <sub>4</sub> nanoparticles surfaces
Bibliographic record
Abstract
Heterogeneous reactions on atmospheric aerosol surfaces are increasingly considered important in understanding aerosol-cloud nucleation and climate change. To understand potential reactions in polluted atmospheres, the co-adsorption of NO2 and toluene to magnetite (Fe3O4i.e. FeO·Fe2O3) nanoparticles at ambient conditions was investigated for the first time. The surface area, size distribution, and morphology of Fe3O4 nanoparticles were characterized by BET method and high-resolution transmission electron microscopy. Adsorption isotherms, collected by gas chromatography with flame ionization detection, showed that the presence of NO2 decreased the adsorption of toluene. The analyses of the surface chemical composition of Fe3O4 by X-ray photoelectron spectroscopy (XPS) reveal that, upon the addition of NO2, the surface is oxidized and a contribution at 532.5 ± 0.4 eV in the O1s spectrum appears, showing that NO2 likely competes with toluene by dissociating on Fe(2+) sites and forming NO3(-). Different competing effects were observed for oxidized Fe3O4; oxidation occurred when exposed solely to NO2, whereas, the mixture of toluene and NO2 resulted in a reduction of the surface i.e. increased Fe(2+)/Fe(3+). Analyses by time of flight secondary ion mass spectrometry further suggest toluene reacts with Fe(3+) sites forming oxygenated organics. Our results indicate that on reduced magnetite, NO2 is more reactive and competes with toluene; in contrast, on oxidized Fe3O4, toluene is more reactive. Because magnetite can assume a range of oxidation ratios in the environment, different competing interactions between pollutants like NO2 and toluene could influence atmospheric processes, namely, the formation of Fe(2+) and the formation of atmospheric oxidants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".