Immersion Meta-Lenses at Visible Wavelengths for Nanoscale Imaging
Bibliographic record
Abstract
Immersion objectives can focus light into a spot smaller than what is achievable in free space, thereby enhancing the spatial resolution for various applications such as microscopy, spectroscopy, and lithography. Despite the availability of advanced lens polishing techniques, hand-polishing is still required to manufacture the front lens of a high-end immersion objective, which poses major constraints for lens design. This limits the shape of the front lens to spherical. Therefore, several other lenses need to be cascaded to correct for spherical aberration, resulting in significant challenges for miniaturization and adding design complexity for different immersion liquids. Here, by using metasurfaces, we demonstrate liquid immersion meta-lenses free of spherical aberration at various design wavelengths in the visible spectrum. We report water and oil immersion meta-lenses of various numerical apertures (NA) up to 1.1 and show that their measured focal spot sizes are diffraction-limited with Strehl ratios of approximately 0.9 at 532 nm. By integrating the oil immersion meta-lens (NA = 1.1) into a commercial scanning confocal microscope, we achieve an imaging spatial resolution of approximately 200 nm. These meta-lenses can be easily adapted to focus light through multilayers of different refractive indices and mass-produced using modern industrial manufacturing or nanoimprint techniques, leading to cost-effective high-end optics.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".