Designing of a Novel Nanophotonic Structure Based on 2D Photonic Crystals for the Detection of Different Materials
Bibliographic record
Abstract
This article focuses on the study of a sensor for the detection of different materials, for which we proposed a novel platform based on 2D photonic crystals.This platform is a nanostructure that carries two parallel waveguides and a resonator in between.For study, this resonator is replaced each time by materials that are: Human Cornea, Teflon (C2F4), Opal (SiO2-nH2O), Aluminum phosphate (Al2PO4) and Topaz (Al2SiO4 (F; OH)2) with their refractive index following, 1.3375, 1.36, 1.45, 1.53 and 1.606 respectively.The proposed design is composed of silicon dielectric rods (Si) with a refractive index of 3.46 submerged in the air where 'n' of air is 1.To examine this structure, a PWE (plane wave expansion approach) and FEM (finite element method) are applied.The (PWE) is used to extract the PBG (photonic band gap) and (FEM) used by COMSOL software in order to extract the desired numerical results such as: the distribution of 'n' and the size of the mesh element all along the structure, followed by the behavior of the electric field (E) along the structure at the resonance before and after injection of the different materials.We also presented the variations of the power flow norm, the total energy density as well as the transmission for the materials used.This study allowed us to observe a significant change in the power flow norm and the total energy density as transmission for each material used when their refractive index changes.This change in refractive index 'n' is among the most important parameters in the detection of different types of materials.
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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 | 0.001 | 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".