Quasiperiodic Nanohole Arrays on Optical Fibers as Plasmonic Sensors: Fabrication and Sensitivity Determination
Bibliographic record
Abstract
Surface plasmon resonance enhanced optical transmission has been observed in periodic nanohole arrays and plenty of plasmonic applications from label-free biosensing to surface-enhanced spectroscopies on various platforms have been found. Recently this effect has also been demonstrated for nanohole arrays with quasiperiodic patterns such as the Penrose tiling. Here we pattern and transfer quasiperiodic nanohole arrays onto optical fibers and investigate their optical performance in refractive index sensing. These quasiperiodic arrays show multiple resonances closely related to their geometric features. The resonances are narrow and sensitive to the dielectric changes on the probe surface due to our high quality fabrication. We find the measured sensitivity of our quasiperiodic nanohole arrays is as high as that of periodic nanohole arrays and reaches the theoretical sensitivity limit as predicted by our universal sensitivity analysis. This result in turn verifies our sensitivity theory on propagating surface plasmon resonance in a wider range beyond periodic nanostructure arrays. Our study demonstrates the quasiperiodic nanohole array based optical fiber is a high-performance plasmonic sensor.
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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 | 0.001 |
| 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) | 0.000 | 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".