Characterization and Oxidation of Fe Nanoparticles Deposited onto Highly Oriented Pyrolytic Graphite, Using X-ray Photoelectron Spectroscopy
Bibliographic record
Abstract
The characterization of Fe 0 nanoparticles (NPs), both before and during oxidation, has been of concern for the last two decades. We have studied the 2p and 3p XPS core levels of Fe NPs evaporated onto highly oriented pyrolytic graphite (HOPG) under ultrahigh vacuum. Both components of the 2p spectrum of Fe 0 are found to be highly asymmetric to higher binding energy; each is composed of a major photoemission peak, plus several smaller peaks attributable to a vacancy cascade, a process known to occur in Fe. In contrast, the two Fe 3p spectral components are too close to be separated with precision, and were treated as one single component; as with the 2p components, it, too, is asymmetric, due to the vacancy cascade. The onset of oxidation affects both spectra somewhat differently, causing the introduction, and subsequent increase, of components on the high binding energy side of the 2p 3/2 spectrum, superimposed on the vacancy cascade; this is not as obvious for the 3p spectrum because of a larger probe depth, to which the surface contributes less. These new components represent the FeO, γ-Fe 2 O 3, and Fe 3 O 4 formed on oxidation; their oxidation kinetics indicate that the initially formed FeO is rate controlling.
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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) | 0.000 | 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 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".