Electronic structures of amorphous M<sub>100-<i>x</i></sub>Zr<sub><i>x</i></sub>alloys (M = Fe, Co, Ni, Cu) studied using core-level x-ray photoemission spectroscopy
Bibliographic record
Abstract
The electronic structures of amorphous a-Fe 100- x Zr x and a-(Fe 1- y M y ) 33 Zr 67 alloys (M = Co, Ni, Cu) have been investigated by using x-ray photoemission spectroscopy. For a-Fe 100- x Zr x , the linewidths of both the Fe 2p and Zr 3d core-level spectra become narrower with increasing x . Systematic line-shape analysis reveals that such a trend could be either due to the decrease in the Auger recombination process or due to the decrease in the exchange splitting of core-hole electrons. The broad Zr 3d spectrum for x = 10 is decomposed into two peaks, suggesting a localized nature of the Zr 4d electrons in a-Fe 90 Zr 10 . For a-(Fe 1- y M y ) 33 Zr 67 (M = Co, Ni), the Fe 2p spectrum becomes narrower and the shoulder-like satellite structure becomes more pronounced with increasing y , reflecting the increasingly localized nature of the Fe 3d electrons. In contrast, the M 2p spectra of a-(Fe 1- y M y ) 33 Zr 67 remain essentially unchanged with varying y , implying that the M 2p spectra of a-M 100- x Zr x are much less sensitive to the M ion species than to the fractional concentrations of M and Zr ions. As M varies from Fe to Co and Ni in a-M 33 Zr 67 , the Zr 3d spectrum exhibits a small shift toward higher binding energy and a slight increase in the linewidth. Band-structure calculations for ordered MZr 2 (M = Fe, Co, Ni, Cu) predict that both the density of states at the Fermi level N ( E F ) and the total number of valence electrons increase as M varies from Fe to Co and Ni, which is consistent with the measured Zr 3d spectra of a-M 33 Zr 67 .
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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".