Volumetric negative-refractive-index metamaterials based upon the shunt-node transmission-line configuration
Bibliographic record
Abstract
A volumetric negative-refractive-index transmission-line (NRI-TL) metamaterial is presented. This structure constitutes a natural extension of the planar NRI-TL metamaterials1 and maintains the desired features of broad bandwidth and low transmission loss. Unlike their planar counterparts, the proposed volumetric NRI-TL metamaterials can effectively couple incident plane waves from free space. The proposed topology can be readily made by stacking layers that are individually fabricated using standard multilayer printed-circuit board techniques at microwave frequencies. However, the creation of the volumetric structure results in the presence of a parasitic parallel-plate mode. This mode can interfere with the desired backward wave mode of the metamaterial, causing a stop band to appear. To facilitate the rapid analysis of this new design, a multiconductor transmission line model was developed. Through the use of this model and full-wave simulations, it will be demonstrated that this unwanted parallel-plate mode can be eliminated by properly arranging the vertical inductive loading wires. Using this process, it will be shown that a properly designed inductive load can result in a practical NRI metamaterial slab which is matched to free space over a large bandwidth (22%) and with low insertion loss (<−1 dB). This approach can also be used to design NRI-TL metamaterials with backward wave dispersion bandwidths of over 140%.
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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 | 0.001 | 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".