Parallel plate waveguide with anisotropic graphene plates: Effect of electric and magnetic biases
Bibliographic record
Abstract
The performances of a parallel plate waveguide (PPWG) supported by perfect electric conductor (PEC)-graphene and graphene-graphene plates are evaluated. The graphene plate behavior is modeled as an anisotropic medium with both diagonal and Hall conductivities derived from Kubo formula. The PPWG modes supported by PEC-graphene and graphene-graphene plates are studied. Maxwell's equations are solved for these two waveguides, while the graphene layers are biased with an electric field only and with both electric and magnetic fields. It is shown that when both electric and magnetic biases are applied to the graphene, a hybrid mode (simultaneous transverse electric (TE) and transverse magnetic (TM) modes) will propagate inside the waveguide. The intensity of each TE and TM modes can be adjusted with the applied external bias fields. Study of different waveguides demonstrates that by decreasing the plate separation (d), the wave confinement improves. However, it increases the waveguide attenuation. A dielectric layer inserted between the plates can also be used to improve the wave confinement. The presented analytical procedure is applicable to other guiding structures having walls with isotropic or anisotropic conductivities.
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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) | 0.001 | 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) | 0.000 | 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".