Sensitivity enhanced long-period grating refractive index sensor with refractive index modified cladding layer
Bibliographic record
Abstract
A sensitivity enhanced long-period grating (LPG) refractive index sensor is proposed and studied by using the LP model. In the simulation, the cladding layer of the LPG is assumed to be partially removed and then deposit a sensitivity enhancement layer (SEL) with a higher refractive index. The effects of the thickness of the original cladding material, and the thickness and refractive index of the SEL layer on the LPG transmission spectrum notch wavelength shift as a function of the ambient refractive index change are reported. The LPG sensor performance depends on the phase match condition of the core mode and cladding mode coupling in the LPG structure and the dependence of the effective index of the cladding mode on the thickness of the original cladding material, and the thickness and refractive index of the SEL layer. The structure modified cladding layer moves the work point of the long period grating to the cladding mode reorganization zone, where the cladding mode effective refractive index changes rapidly upon the LPG waveguide parameter. Proper selection of parameters of the cladding layer can be applied to enhance the modulation of the effective index of the cladding mode by the ambient refractive index through the evanescent field and thus construct sensitivity enhanced ambient refractive index sensors.
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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 | 0.001 |
| Open science | 0.001 | 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".