Bibliographic record
Abstract
Metamaterials, which are materials engineered to possess novel optical properties, have been increasingly studied. The ability to fabricate metamaterials has sparked an interest in determining possible applications. We investigate using a metamaterial for boundary engineering in waveguides. A metamaterial-clad cylindrical waveguide is used to provide confinement for an optical signal, thereby increasing the local electromagnetic energy density. We show that metamaterial-clad dielectric waveguides have unique optical properties, including new modes, which we call hybrid modes. These modes have properties of both ordinary guided modes and surface plasmon-polariton modes. We show that for certain metamaterial parameters, the surface plasmon-polariton modes of a metamaterial-clad guide have less propagation loss than those of a metal-clad waveguide with the same permittivity. This low-loss mode is exploited for all-optical control of weak fields. Embedding three-level Λ atoms in the dielectric core of a metamaterial-clad waveguide allows the use of electromagnetically induced transparency to control an optical signal propagating through the guide. Adjusting the pump field alters the group velocity of the signal, thereby controllably delaying pulses. The signal can even be stopped with applications to optical memory. Using the low-loss surface mode of a metamaterial-clad guide reduces losses by 20% over a metal cladding without sacrificing the group velocity reduction or confinement. In addition, we show that losses can be reduced by as much as 40% with sufficient reduction of the magnetic damping constant of the metamaterial. As this work aims for applications, practical considerations for fabricating and testing metamaterial-clad waveguides are discussed. An overview of the benefits and drawbacks for two different dielectric core materials is given. Also, a short discussion of other modes that could be used, along with some issues that may arise with their use, is given.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".