Magnetoelastic interactions and magnetic damping in Co2Fe0.4Mn0.6Si and Co2FeGa0.5Ge0.5 Heusler alloys thin films for spintronic applications
Bibliographic record
Abstract
Abstract Co2Fe0.4Mn0.6Si (CFMS) and Co2FeGa0.5Ge0.5 (CFGG) Heusler alloys are among the most promising thin film materials for spintronic devices due to a high spin polarization, low magnetic damping and giant/tunneling magnetoresistance ratios. Despite numerous investigations of Heusler alloys magnetic properties performed up to now, magnetoelastic effects in these materials remain not fully understood; due to quite rare studies of correlations between magnetoelastic and other magnetic properties, such as magnetic dissipation or magnetic anisotropy. In this research we have investigated epitaxial CFMS and CFGG Heusler alloys thin films of thickness in the range of 15–50 nm. We have determined the magnetoelastic tensor components and magnetic damping parameters as a function of the magnetic layer thickness. Magnetic damping measurements revealed the existence of non-Gilbert dissipation related contributions, including two-magnon scattering and spin pumping phenomena. Magnetoelastic constant B11 values and the effective magnetic damping parameter αeff values were found to be in the range of − 6 to 30 × 106 erg/cm3 and between 1 and 12 × 10–3, respectively. The values of saturation magnetostriction λS for CFMS Heusler alloy thin films were also obtained using the strain modulated ferromagnetic resonance technique. The correlation between αeff and B11, depending on magnetic layer thickness was determined based on the performed investigations of the above mentioned magnetic properties.
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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".