Efficient phase shift in metamaterial spoof surface plasmon polaritons waveguides
Bibliographic record
Abstract
We propose a novel method for phase shift control in spoof surface plasmon polaritons (SPPs) by systematically loading engineered metallic-dielectric structures. Prior researches examined material effects on spoof SPPs cells dispersion diagrams, But systematic loading different regions of cells remains unaddressed. By tailoring the dielectric loading of bulky U-shaped metallic unit cells using materials with relative permittivities ranging from 3.2 to 9.8 (TMM 3 to TMM 10) in different regions, we precisely investigate the corresponding dispersion's behavior. A measurement setup including a PCB-to-bulky U-shaped spoof SPPs waveguide transition was engineered to feed the U-shaped cells, ensuring they closely match the ideal theoretical performance, defined as a U-shaped cell without any substrate effects. Our theoretical and experimental analyses show that loading the cell's exterior region maximizes the phase shift, with TMM10 achieving up to [Formula: see text] at 3.5 GHz and TMM3 yielding [Formula: see text] at 5 GHz. Near-field measurements using a NEOSCAN optical probe validate these findings across 0.5-6 GHz, demonstrating a clear trade-off between maximum phase shift and bandwidth. This work provides a practical approach for developing compact, high-performance phase shifters for telecommunications and sensing applications.
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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".