Approach for Temperature-Sensitivity Enhancement in a Tapered Dual-Core As<sub>2</sub>Se<sub>3</sub>-PMMA Fiber With an Antisymmetric Long-Period Grating
Bibliographic record
Abstract
We propose and demonstrate an approach for temperature-sensitivity enhancement based on effective group-velocity matching between the even and odd modes of a dual-core As2Se3-PMMA fiber taper on which an antisymmetric long-period grating is inscribed. The transmission of optical pulses in the dual-core As2Se3-PMMA taper inscribes the antisymmetric long-period grating that causes the electric fields to couple back and forth between the even and odd modes leading to effective group-velocity matching between the two modes. The variation of the difference between phases of the two modes φd(λ) with respect to wavelength tends to 0 (∂φd(λ)/∂λ → 0) near the resonance wavelength of the grating due to effective group-velocity matching between the two modes, and consequently, thermally induced change of the difference between phases of the two modes φd(λ) leads to a large wavelength shift indicating enhancement of the temperature measurement sensitivity. Experimental results show that temperature measurement sensitivity in the wavelength range with effective group velocity matching is enhanced by a factor of 4.0 in comparison with the sensitivity in the wavelength range that does not have effective group velocity matching in the dual-core taper with As2Se3core diameter of 1.5 μm. Exploiting sensitivity enhancement within the group-velocity matching wavelength range opens the path toward the realization of novel high-sensitivity fiber sensors for temperature and strain measurement.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".