Enhancement of spin pumping damping in ferromagnet/CoPt structures (Conference Presentation)
Bibliographic record
Abstract
Spin-pumping is the phenomenon by which a pure spin current can be injected into a non-magnetic metal from an adjacent ferromagnet. Spin-pumping into Pt is of great interest since Pt exhibits large inverse spin Hall effect. However, Pt limits the efficiency of spin-pumping due to spin-memory loss at the ferromagnet/Pt interface. By alloying Pt with Co we saw a large enhancement in magnetic damping suggesting a significant increase in spin-pumping efficiency. Analyzing the damping results of ferromagnet/CoPt using spin-pumping model yields to an increase in the spin-mixing parameter by a factor of 4 as compared to ferromagnet/Pt. The enhancement in damping cannot be explained by an increase in the interface spin-memory effect since the spin-diffusion length in CoPt is increased by a factor of 2 as compared to pure Pt. An additional potential source of spin-pumping-like interface damping could be originating form the proximity effect. It is found that proximity effect at the ferromagnet/CoPt interface induces a long-range magnetic moment in CoPt, up to 6nm. Proximity effect can also lead to an increase in fluctuating moment in CoPt, which can then enhance the transfer of magnetic momentum from the ferromagnet to CoPt. In this case, effective spin-pumping parameter will depend on the interaction of spin-correlated clusters in CoPt with the adjacent ferromagnet. This is different from the typically used spin-pumping model where the loss of magnetic moment is mediated by the independent normal-metal electrons reflected at the spin-dependent electron potential at the ferromagnet/normal metal interface.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.017 | 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 teacher head, 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".