Tunable magnetization and damping of sputter-deposited, exchange coupled Py|Fe bilayers
Bibliographic record
Abstract
)|Fe and Fe|Py bilayers, prepared by sputter-deposition on an amorphous 3 nm Ta seed layer. FMR measurements are performed on varying thicknesses of the individual Py and Fe layers while keeping the total bilayer structure thickness fixed. When Fe is grown directly on Ta, there is large magnetic inhomogeneity and damping. However, when a Py layer is deposited between Fe and Ta, both the magnetic inhomogeneity and damping significantly decrease even if Fe is covered by Ta. The intrinsic damping of the Ta|Py|Fe film can be further lowered by increasing the Fe to Py ratio. SQUID measurements show a linear increase in saturation magnetization with increasing ratio of Fe to Py. A combination of in-plane and out-of-plane X-ray diffraction measurements show that Py is textured along the 〈111〉 directions and Fe is textured along the 〈110〉, with Fe texture significantly improving if it is deposited on Ta|Py instead of Ta. By improving the texture of Fe by introducing a thin Py layer between Fe and Ta, one can grow Fe thin films with zero in-plane anisotropy, tunable magnetic moment, and low magnetic damping, approaching that of the best single crystal Fe.
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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".