Highly sensitive double D-shaped channel photonic crystal fiber based plasmonic refractive index sensor
Bibliographic record
Abstract
In this paper, a highly sensitive miniaturized surface plasmon resonance (SPR) based photonic crystal fiber (PCF) sensor is presented for a wide range of analyte sensing. Gold is selected as the plasmonic metal for its higher chemical stability and titanium oxide works as the adhesive layer for gold attachment on silica. The plasmonic metal and the sensing medium are placed exterior to the surface of the sensor design to make it fitting for practical applications. By a careful arrangement of the periodic arrangement of the refractive index in the design, the generation of the evanescent fields is fine-tuned to obtain the phase matching between the leaky core guided mode and the surface plasmon polariton (SPP) mode. Numerical simulations have been carried out by employing the finite element method (FEM) with the consideration of a perfectly matched layer (PML) to absorb surface radiations. The proposed sensor shows a maximum wavelength sensitivity of 34,000 nm/RIU (refractive index units) and a maximum amplitude sensitivity of 331 RIU−1, investigated by using the wavelength and the amplitude interrogation methods, respectively, for the analyte sensing range of 1.16 to 1.37 RI (refractive index). The sensor also exhibits a wavelength resolution of 2.94×10−6 RIU which indicates a high detection accuracy. On that, the proposed sensor would be an excellent candidate for a wide range of RI detection, applicable for various purposes such as chemical detections, medical diagnostics, bio-sensing, and other low RI analytes.
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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 | 0.001 | 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".