Polarization‐Tuned Fano Resonances in All‐Dielectric Short‐Wave Infrared Metasurface
Bibliographic record
Abstract
Abstract The short‐wave infrared (SWIR) is an underexploited portion of the electromagnetic spectrum in metasurface‐based nanophotonics despite its strategic importance in sensing and imaging applications. This is mainly attributed to the lack of material systems to tailor light–matter interactions in this range. Herein, this limitation is addressed and an all‐dielectric silicon‐integrated metasurface enabling polarization‐induced Fano resonance control at SWIR frequencies is demonstrated. The platform consists of a 2D Si/Ge0.9Sn0.1 core/shell nanowire array on a silicon wafer. By tuning the light polarization, it is shown that the metasurface reflectance can be efficiently engineered due to Fano resonances emerging from the electric and magnetic dipoles competition. The interference of optically induced dipoles in high‐index nanowire arrays offers additional degrees of freedom to tailor the directional scattering and the flow of light while enabling sharp polarization‐modulated resonances. This tunablity is harnessed in nanosensors yielding an efficient detection of 10−2 changes in the refractive index of the surrounding medium.
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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".