New Design of 2D Photonic Crystal Hydrostatic Pressure Sensor based on F.E.M Method used for Sensing Applications
Bibliographic record
Abstract
The author of this article illustrates a new design and simulation of a twodimensional photonic crystal (PhC) based hydrostatic pressure sensor with a wide dynamic range that ranges from 0 to 4 Gpa and is conducted at a high resolution using the Finite Elements Method (F.E.M) under COMSOL software.In order to analyze the detection principle and its characteristics, the sensor is based on a triangular array PhC-2D of air-immersed silicon rods with a refractive index of 3.6 is used.The sensor consists of a hexagonal shaped ring resonator, which sits between two obtuse angle shaped waveguides.The two waveguides are configured by removing a row of B rods (line faults) and the ring resonator by removing a few rods.This ring resonator reduces the effect of external parameters such as humidity, temperature, etc.The band diagram is presented as well as analyzed using the plane wave expansion (PWE) under the Mat lab software on the one hand and on the other hand, on the other hand, results are obtained and illustrated such as: the distribution of the refractive index 'n' within the structure with the mesh, the distribution of the electric field (TE) at resonance in 2 and 3D, the total energy density (TED), the power flow norm (PFN) and transmission.The sensor is designed for wavelengths between 1520nm and 1640nm.The simulation results show that due to the applied pressure, the refractive index of a sensor is changed and thus the resonant wavelength is shifted linearly to longer wavelengths.The designed sensor behaves linearly between 0GPa and 4GPa of applied pressure and 4.6 nm/GPa of pressure 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 | 0.000 |
| 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".