Surface magnon-polaritons at a dielectric/graphene/gyromagnetic interface in a perpendicular applied magnetic field
Bibliographic record
Abstract
We present a theoretical study of the surface magnon-polaritons at an interface formed by vacuum and a gyromagnetic medium (that can be either ferromagnetic or antiferromagnetic), when there is a graphene layer deposited between the media at the interface and a magnetic field is applied perpendicular to the interface. The retarded-mode dispersion relations are calculated by considering a superposition of transverse magnetic and transversal electric electromagnetic waves in both media. Our results reveal the appearance of the surface magnon-polariton modes (with frequencies typically of a few GHz) that do not exist in the absence of graphene at the interface. Also, a typical magnon-polariton dispersion relation with damping is revealed, including a resonant frequency that depends on the applied magnetic field. The effects of varying the doping levels, which modify the Fermi energies in the graphene, and varying the perpendicular applied magnetic field are presented, revealing a strong influence exerted by the presence of graphene on the surface magnon-polariton modes. Other effects include the control of the slope of the dispersion curves (with respect to the in-plane wave vector) for the modes as the Fermi energies of the graphene sheet are changed and the distinctive localization properties for the emerging surface modes.
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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 | 0.001 |
| 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".