Band diagrams of layered plasmonic metamaterials
Bibliographic record
Abstract
We introduce a method to map the band diagrams, or equipotential contours (EPCs), of any layered plasmonic metamaterial using a general expression for the Poynting vector in a lossy layered medium of finite extent under plane-wave illumination. Unlike conventional methods to get band diagrams by solving the Helmholtz equation using the Floquet-Bloch theorem (an approach restricted to infinite, periodic, lossless media), our method adopts a bottom-up philosophy based on spatial-frequency decomposition of the electric and magnetic fields (an approach applicable to finite, lossy media). Equipotential contours are used to visualize phase and group velocities in a wide range of layered plasmonic systems, including the basic building block of a thin metallic layer and more complex multi-layers with unique optical properties such as negative phase velocity, super-resolution imaging, canalization, and far-field imaging. We show that a thin metallic layer can mimic a left-handed electromagnetic response at the surface plasmon resonance and that stacks of metal and dielectric layers can do the same provided that the dielectric layer is sufficiently thin. We also use EPCs to estimate resolution limits of both Pendry's silver slab lens and the Veselago lens and show that the image location and lateral image resolution of metal-dielectric layered flat lenses can be described (and tailored) by the concavity and spectral reach of the dominant band in their EPCs. Homogenization methods for describing the effective optical properties of various layered systems are validated by the extent to which they accurately mimic features in their EPCs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".