Structural and electronic properties of Fe dopants in cobalt oxide nanoislands on Au(111)
Bibliographic record
Abstract
Mixed metal oxides of earth-abundant 3d transition metals are an interesting class of materials that show interesting magnetic properties and a significant synergistic effect as catalysts for electrochemical oxygen evolution compared to simple unary oxides. However, the exact atomic-scale nature of such mixed oxide phases and the link to their interesting physico-chemical properties are poorly understood. Here, a combination of scanning tunneling microscopy and x-ray photoemission spectroscopy reveals that Fe species embed in a facile way into CoO bilayers on Au(111) resulting in an Fe doped oxide. Density functional theory and the spectroscopic fingerprint from x-ray photoemission spectroscopy reveal that the Fe dopants in the cobalt oxide matrix assume a higher oxidation state than in the structurally corresponding unary bilayer oxide. Furthermore, the substituted Fe is structurally displaced further away from the Au than the metal in either of the corresponding pure unary oxides. Both O and to a smaller extent Co in the nearest coordination shell are also structurally and electronically perturbed. The interesting effects observed in the bilayer binary oxides may enable a better fundamental understanding of the nature of doping of metal oxides, in general, and promotion effects in catalytic applications.
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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".