X-Ray, Proton, and Electron Radiation Effects on Type I Fiber Bragg Gratings
Bibliographic record
Abstract
Fiber Bragg gratings (FBGs) are one of the most used optical fiber sensors and they have recently drawn the attention of several research groups for their potential applications in harsh radiation environments. Up to now, their performances have been mainly evaluated under ionizing radiations, such as X-rays or γ-rays. We compare here the effects of different irradiation types, including X-rays, protons, and electrons, on type I FBGs written by UV laser exposure in Ge-, P/Ce-, and B/Ge-doped single-mode optical fibers. Different irradiation conditions were used according to the sources; 6-MeV electron irradiations were performed at a dose rate of 120 Gy(SiO2)/s up to an accumulated dose of 500 kGy; whereas for the 63-MeV protons, the estimated equivalent dose rate was of 0.75 Gy(SiO2)/s up to a total dose of 7 kGy. In order to compare their effects with those induced by X-rays, two irradiations with 45-keV photons were performed with different dose rates [0.75 and 60 Gy/s (SiO2)] up to a total dose of 10 and 500 kGy, respectively. We demonstrated that X-rays and protons induce comparable effects at doses of about 10 kGy, whereas the behavior under electron beam appears to be strongly dependent on the fiber composition. For example, the grating in the B/Ge co-doped fiber is the most sensitive to electrons and the most resistant to X-rays; whereas the FBG inscribed in the H2-loaded P/Ce co-doped fiber has exactly the opposite behavior.
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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) | 0.001 | 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".