Structural and magnetic properties of a chemically ordered face-centered-cubic (111) Mn alloy film
Bibliographic record
Abstract
A 4 ML Ni∕W(110) substrate is used to establish a (111) face-centered-cubic (fcc) template upon which 3 ML of Fe is deposited and annealed to 580K to form a substrate with very good short and long range fcc (111) order, that is Fe rich at the surface. Mn alloy films are formed by annealing a subsequent Mn deposit of 0.3–1.6 ML. Low-energy electron diffraction (LEED), Auger electron spectroscopy (AES), and directional AES show that an ordered alloy is not formed until an annealing temperature of 580K, upon which a multilayer alloy with a P(23×23)R30° LEED pattern is created. The alloy films formed from 0.3 to 0.5 ML of Mn have magnetic properties similar to the FeNi substrate. Hysteresis loops and ac-susceptibility curves measured using the Kerr effect give square loops with a ferromagnetic moment along the in-plane fcc [-211] direction and a Curie temperature TC of about 460K. There is an increase in coercive field likely due to the inhomogeneities introduced by the Mn. Alloy films formed from 0.8 to 1.6 ML of Mn show a marked increase in the width of the susceptibility peak, and a decrease in the peak temperature. The hysteresis loop becomes slanted with a reduced coercive field. The measurements are consistent with a paramagnetic or antiferromagnetic Mn alloy forming an uneven interface within the FeNi film, so that the remaining FeNi film has a wide distribution in TC.
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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".