Surface Chemistry and Thermal Stability of Fe Nanoparticles Annealed under Ultrahigh-Vacuum Conditions
Bibliographic record
Abstract
In situ X-ray photoelectron spectroscopy (XPS) and ex situ atomic force (AFM) and high-resolution transmission electron (TEM) microscopies were used to study the surface structure and thermal stability of Fe nanoparticles (NPs) deposited onto highly oriented pyrolytic graphite (HOPG) surfaces. The evolutions of both core level and valence band spectra were followed as a function of annealing temperature, from room temperature to 560 °C, under ultrahigh-vacuum conditions. These results reveal NP surface diffusion and agglomeration, without coalescence, on HOPG surfaces as the annealing temperature is increased to 400 °C. Simultaneously, a surface chemical reaction occurs, involving the loss of both metal oxide and oxidized carbon on the NP surface, leading to the disappearance of the O 1s spectrum and the formation of a Fe@C m H n core–shell structure. On annealing at temperatures above 400 °C, the NP agglomerates are found to be stabilized on the surface of HOPG. Both XPS and TEM results suggest that the individual NP size is independent of annealing temperature, attributed to the presence of the stabilizing hydrocarbon shell during the entire annealing process. Its presence above 400 °C, at a constant thickness, indicates a continual process of hydrocarbon loss and replacement from the vacuum background, reminiscent of the Fischer–Tropsch (F–T) process for hydrocarbon production.
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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".