Sensitivity analysis of 1D and 2D photonic crystals sensors based on change of thickness and refractive index in material
Bibliographic record
Abstract
A detail study was done on the sensitivities of 1-D photonic crystal (PC) and 2-D PC coupled cavity sensors with changing sensing layer parameters of thickness and refractive index (RI). Though both refractive index and thickness are interrelated they have significant individual affects on device response. In 1-D PC shifts in normal transmission peak due to surface change in thickness and RI and in 2-D PC coupled cavity shifts in transmission dip due to surface changes are observed. Here sensitivity analysis in change in thickness and RI on these devices was done for four cases; case 1: change in thickness from 2nm-10nm on PC sensors, case 2: change in thickness from 75nm-175nm on PC sensors, case 3: change in RI in thin film (6nm) on surface and case 4: change in RI in thick film (100nm) on sensors surface. Sensitivities due to change in thickness (St) of 1-D PC and 2-D PC coupled cavity were calculated from the slope of the sensitivity curves and found to be (for RI of 1.4) 1.423nm/nm and 2.285nm/nm for case 1 and 0.455nm/nm and 0.801nm/nm for case 2. Sensitivities due to change in RI (Sr) of 1-D PC and 2-D PC coupled cavity were obtained from the transmission peak and dip shifts due to change in RI from 1(air) to 2. Sr for 1-D PC and 2-D PC coupled cavity were found to be 70nm/RIU and 103nm/RIU for case 3 and 143nm/RIU and 213nm/RIU for case 4. The results are based on FDTD simulations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".