Photonic crystal fiber-based plasmonic sensors for the detection of biolayer thickness
Bibliographic record
Abstract
The application of metallized photonic crystal fibers in surface plasmon resonance sensors of biolayer thickness is demonstrated. By the judicious design of photonic crystal fibers, the effective refractive index of the fundamental core mode can be tuned to enable efficient phase matching with a plasmon anywhere from the visible to near IR. Among other advantages of the presented sensors we find high sensitivity in the visible and near-IR spectral regions, as well as high coupling efficiency from an external Gaussian beam. Based on the numerical simulations, we present designs using various types of photonic crystal fibers, including holey fibers with and without defect, as well as honeycomb photonic crystal fibers. We find that in addition to the fundamental plasmonic excitation, higher order plasmonic modes can also be excited. In principle, using several plasmonic excitations at the same time can enhance sensor detection limit. Both amplitude and spectral-based methodologies for the detection of changes in the biolayer thickness are discussed. Sensor resolutions of the biolayer thickness as high as 0.039-0.044 nm are demonstrated in the whole 600-920 nm region. Finally, we perform analysis of the effect of imperfections in the metal layer geometry on the sensor sensitivity.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".