Coherent Raman scattering imaging with a near-infrared achromatic metalens
Bibliographic record
Abstract
optical diagnosis. Toward the development of these small-scale systems, a challenge arises from the design and fabrication of achromatic and high-end miniature optical components for both pump and Stokes laser wavelengths. Here, we report a metasurface converting a low-cost plano-convex lens into a water-immersion, nearly diffraction-limited and achromatic lens. The metasurface comprising amorphous silicon nanopillars is designed in a way that all incident rays arrive at the focus with the same phase and group delay, leading to corrections of monochromatic and chromatic aberrations of the refractive lens, respectively. Compared to the case without the metasurface, the hybrid metasurface-refractive lens has higher Strehl ratios than the plano-convex lens and a tighter depth of focus. The hybrid metasurface-refractive lens is utilized in spectroscopic stimulated Raman scattering and coherent anti-Stokes Raman scattering imaging for the differentiation of two different polymer microbeads. Subsequently, the hybrid metalens is harnessed for volumetric coherent Raman scattering imaging of bead and tissue samples. Finally, we discuss possible approaches to integrate such hybrid metalens in a miniature scanning system for label-free coherent Raman scattering endoscopes.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".