A two-dimensional uniplanar transmission-line metamaterial with a negative index of refraction
Bibliographic record
Abstract
A uniplanar transmission-line (TL) network has been loaded with lumped elements (chip or printed), enabling one to achieve a two-dimensional (2D) uniplanar negative-refractive-index (NRI) metamaterial. The metamaterial consists of a 2D array of unit cells, composed of TL sections connected in series and loaded in a specified manner. The unit cell dimensions can be designed to be much smaller than the operating wavelength, enabling one to identify the structure as an effective medium, with a negative index of refraction. This NRI metamaterial supports transverse electric (TE) waves, as opposed to related previous work on NRI-TL media that supported transverse magnetic (TM) waves. The dispersion characteristics are calculated using a simple, fast 2D loaded TL model with periodic (Bloch) boundary conditions. Subsequently the dispersion relation is simplified in the homogeneous limit, thus allowing one to identify effective permittivities and permeabilities, which are shown to be simultaneously negative. Simulations demonstrating the negative refraction of a plane wave on an interface between such a NRI uniplanar metamaterial and a commensurate positive-refractive-index (PRI) metamaterial verify the validity of the proposed concept and theory. A fully printed unit cell is presented at microwave frequencies (∼10 GHz) along with a prescription for synthesizing an isotropic 3D transmission line NRI metamaterial based on this unit cell.
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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".