Improvement of valley splitting and valley injection efficiency for graphene/ferromagnet heterostructure*
Bibliographic record
Abstract
The valley splitting has been realized in the graphene/Ni heterostructure with the splitting value of 14 meV, and the obtained valley injecting efficiency from the heterostructure into graphene was 6.18% [Phys. Rev. B 92 115404 (2015)]. In this paper, we report a way to improve the valley splitting and the valley injecting efficiency of the graphene/Ni heterostructure. By intercalating an Au monolayer between the graphene and the Ni, the split can be increased up to 50 meV. However, the valley injecting efficiency is not improved because the splitted valley area of graphene moves away from the Fermi level. Then, we mend the deviation by covering a monolayer of Cu on the graphene. As a result, the valley injecting efficiency of the Cu/graphene/Au/Ni heterostructure reaches 10%, which is more than 60% improvement compared to the simple graphene/Ni heterostructure. Then we theoretically design a valley-injection device based on the Cu/graphene/Au/Ni heterostructure and demonstrate that the valley injection can be easily switched solely by changing the magnetization direction of Ni, which can be used to generate and control the valley-polarized current.
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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".