Watching Iron Nanoparticles Rust: An <i>in Situ</i> X-ray Absorption Spectroscopic Study
Bibliographic record
Abstract
Iron nanoparticles and iron oxide nanoparticles are among the most commonly studied nanomaterials because of their applications in fields ranging from catalysis to ferrofluids. However, many synthetic methods give iron nanoparticles with large size distributions, and it is difficult to follow the kinetics of iron nanoparticle oxidation reactions and the relative speciation of iron oxidation states in real time. Herein, we introduce a simple approach of controlling the sizes of Fe@Fe x O y nanoparticles and a novel method for following Fe@Fe x O y nanoparticle oxidation in situ in liquid solutions by Fe K- and L-edge X-ray absorption near-edge structure (XANES) spectroscopy. XANES results show that these Fe@Fe x O y nanoparticles have similar XANES spectra before exposure to air. In situ XANES measurements allow for quantitative oxidation kinetics of different nanoparticle sizes to be followed; results show that the rate of Fe(0) oxidation increases with a decrease in average nanoparticle size. However, the rate of Fe core size depletion was found to be ca. 0.02 nm/min for all the nanoparticle systems studied. This suggests similar oxidation mechanisms are at work for all the particle sizes studied. This work shows that in situ liquid cell XANES can be used to follow oxidation state and coordination environment changes in Fe NP dispersions.
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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 | 0.001 |
| 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".