Tuning the response of long-period fiber gratings for chemical sensing applications
Bibliographic record
Abstract
In recent years, the use of long-period gratings (LPGs) as fiber optic chemical sensors has been proposed by several authors. Such implementations take advantage of the changes in the LPG transmittance characteristics with ambient refractive index and may make use of a polymer coating to enhance chemical selectivity and sensitivity. While technically feasible, these designs are subject to fairly rigid constraints related to the optical characteristics of the fiber and grating, as well as the thickness and refractive index of the chemically selective polymer. Compromises in design may lead to sub-optimal sensor performance in terms dynamic range, sensitivity, linearity, stability and response time. In this work, LPG sensor designs based on one-, two- and three-layer geometries are explored, where the outer layer is the chemically selective polymer and the properties of the other layers (thickness, refractive index) can be adjusted. It is demonstrated through calculations based on a hybrid mode model that the use of more than one layer greatly enhances the flexibility of sensor design and allows the response characteristics to be tuned for optimal performance. A case study is used to illustrate how the same sensor can be optimized for several factors, including linearity, range, sensitivity, and stability.
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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 | 0.000 | 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".