Stable Light Focusing by Meta-Axicons Applicable in Biosensors, Particle Trapping, Astronomical, and Imaging Devices
Bibliographic record
Abstract
Axicons with the ability to convert Gaussian beams into non-diffracting Bessel beams are well-known in the field of light focusing for their large depth-of-focus. In this paper, we investigate light focusing quality of the meta-axicons in the presence of a turbulent medium in the focusing space like water and opaque obstacles in front of the axicon. We apply the required phase and amplitude profile of the axicon to a planar arrangement of meta-atoms. Dielectric c-shaped meta-atoms are designed to cover all transmitted phase and amplitude values at a wavelength of λ = 700 nm. We show that meta-axicon's focusing parameters like its depth of focus (DOF) and its subwavelength lateral full width at half maximum (FWHM) are not affected by the presence of the turbulence in the focusing area. This effect makes meta-axicons as a promising focusing device in biosensors, telescopes, particle trapping, and imaging 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 | 0.000 | 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".