Transverse magnetic supermodes in plasmonic optical fibers excited by radially polarized light
Bibliographic record
Abstract
The overlap integrals method, with a fully vectorial formulation, is used to model the selective excitation of the TM 01 mode in a few-mode optical fiber with a radially polarized donut beam, and its coupling to guided modes having a plasmonic character (supermodes). The analyses were performed on a waveguide formed as a step-index few-mode optical fiber coated with a thin gold film, at an operating wavelength of 1310 nm. The waveguide was found to support modes having optical fiber, circular metallic waveguide, and surface plasmon characteristics, depending on geometrical and material parameters. Three purely bound transverse magnetic (radially polarized) supermodes were identified: Two symmetric, labeled sTM 01 and sTM 02 modes, and one asymmetric, labeled a b mode, where symmetry pertains to the transverse electric field distribution over the gold film. The effective mode indices of the supermodes were studied as a function of the thickness of the gold film and its proximity to the fiber core. Considerations for the selective excitation of the sTM 01 mode are discussed along with its possible applications. The transmittance of the supermodes is found to be robust even at sharp waveguide transitions. The results predict that effective excitation of TM supermodes with strong plasmonic character, without significant coupling losses, can be achieved by exciting the fiber with a radially polarized donut beam.
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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) | 0.000 | 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".