Experimental and theoretical verification of focusing in a large, periodically loaded transmission line negative refractive index metamaterial
Bibliographic record
Abstract
We have previously shown that a new class of Negative Refractive Index (NRI) metamaterials can be constructed by periodically loading a host transmission line medium with inductors and capacitors in a dual (high-pass) configuration. A small planar NRI lens interfaced with a Positive Refractive Index (PRI) parallel-plate waveguide recently succeeded in demonstrating focusing of cylindrical waves. In this paper, we present theoretical and experimental data describing the focusing and dispersion characteristics of a significantly improved device that exhibits minimal edge effects, a larger NRI region permitting precise extraction of dispersion data, and a PRI region consisting of a microstrip grid, over which the fields may be observed. The experimentally obtained dispersion data exhibits excellent agreement with the theory predicted by periodic analysis, and depicts an extremely broadband region from 960MHz to 2.5GHz over which the refractive index remains negative. At the frequency at which the theory predicts a relative refractive index of -1, the measured field distribution shows a focal spot with a maximum beam width under one-half of a guide wavelength. These results are compared with field distributions obtained through mathematical simulations based on the plane-wave expansion technique, and exhibit a qualitative correspondence. The success of this experiment attests to the repeatability of the original experiment and affirms the viability of the transmission line approach to the design of NRI metamaterials.
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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 | 0.001 |
| 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) | 0.001 | 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 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".