Periodically loaded transmission line with effective negative refractive index and negative group velocity
Bibliographic record
Abstract
Media with negative refractive index (NRI) are expected to exhibit properties that are unusual compared to materials with a positive index of refraction. However, until recently, these properties were not experimentally observed, since no NRI material occurs naturally. Periodic structures with NRI have been constructed. Our group created artificial NRI materials by loading a cellular network of transmission lines with series capacitors and shunt inductors (Eleftheriades, G.V. et al., IEEE Trans. Microwave Theory and Techniques, vol.50, no.12, p.2702-12, 2002; Microwave and Wireless Component Lett., 2003). We have extended that work to design a medium that exhibits negative group velocity (NGV) in addition to NRI. To achieve the NGV, a resonant circuit is embedded within each loaded transmission line (LTL) unit cell. The resonance produces a region of anomalous dispersion in which the group delay, and thus the group velocity, is negative. The NGV means that the peak of the output pulse emerges from the LTL prior to the peak of the input pulse, though much reduced in magnitude. Note that the front of the output pulse does not precede the front of the input pulse; that is, the output pulse front suffers the usual positive delay. The proposed transmission line is fabricated using coplanar waveguide technology. Scattering matrix measurements verify the theoretical predictions.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".