Hybrid metal wire–dielectric terahertz waveguides: challenges and opportunities [Invited]
Bibliographic record
Abstract
In this review we evaluate recent experimental and theoretical progress in the development of wire-based waveguides used for practical low-loss and low-dispersion delivery of terahertz radiation. Waveguides considered in this review utilize plasmonic modes guided in the air gap between two parallel wires. The two parallel wires are, in turn, encapsulated inside of a low-loss, low-refractive-index micro- or nano-structured cladding that provides mechanical stability and isolation from the environment. We describe two alternative techniques that may be used to encapsulate the two-wire waveguides while minimizing the negative impact of dielectric cladding on the optical properties of the waveguide. The first technique uses low-density foam as a cladding material, while the other uses air-filled microstructured plastic claddings to support metallic wires. Additionally, we offer a detailed analysis of the modal properties of wire-based waveguides, compare them with the properties of a classic two-wire waveguide, and present several strategies for the improvement of hybrid waveguide performance. Using the resonant dependence of the confinement properties of some hybrid plasmonic modes also allows us to propose their use in terahertz refractometry. Finally, we demonstrate that wire-based porous waveguides can have a very large operational bandwidth while supporting tightly confined, air-bound modes at both high and low frequencies. This is possible as, at higher frequencies, hybrid fibers can support ARROW-like low-loss air-bound modes while changing their guidance mechanism to plasmonic confinement in the inter-wire air gap at lower frequencies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".