Free-Space Imaging Beyond the Diffraction Limit Using a Veselago-Pendry Transmission-Line Metamaterial Superlens
Bibliographic record
Abstract
Focusing using conventional lenses relies on the collection and interference of propagating waves, but discounts the evanescent waves that decay rapidly from the source. Since these evanescent waves contain the finest spatial details of the source, the image suffers a loss of resolution and is referred to as ldquodiffraction-limited.rdquo Superlensing is the ability to create an image with fine features beyond the diffraction limit, and can be achieved with a ldquoVeselago-Pendryrdquo lens made from a metamaterial. Such a Veselago-Pendry superlens for imaging in free space must be stringently designed to restore both propagating and evanescent waves, but meeting these design conditions (isotropicn= epsivr= mur= -1) has proven difficult and has made its realization elusive. We demonstrate free-space imaging with a resolution over three times better than the diffraction limit at microwave frequencies using a Veselago-Pendry metamaterial superlens based on the negative-refractive-index transmission-line (NRI-TL) approach, which affords precise control over its properties and is also less susceptible to losses than other approaches. A microwave superlens can be particularly useful for illumination and discrimination of closely spaced buried objects over practical distances by way of back-scattering, e.g., in tumour or landmine detection, or for targeted irradiation over electrically small regions in tomography/hyperthermia 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".