Medical Range Radiation Dosimeter Based on Polymer-Embedded Fiber Bragg Gratings
Bibliographic record
Abstract
Fiber Bragg gratings (FBGs) are valuable dosimeters for doses up to 100 kilograys (kGy), but have hardly been used for the low-dose range of a few grays (Gy) required in medical radiation dosimetry. We report that embedding a doped silica fiber FBG in a polymer material allows a minimum detectable dose of 0.3 Gy for γ-radiation. Comparing the detector response for different doped silica fibers with various core doping, we obtain an independent response, in opposition to what is reported for high-dose range. We hypothesized that the sensor detection is based on the radio-induced thermal expansion of the surrounding polymer. Hence, we used a simple physical model based on the thermal and mechanical properties of the surrounding polymer and obtained good accordance between measured and calculated values for different compositions and thicknesses. We report that over the 4 embedding polymers tested, polyether ether ketone and polypropylene have respectively the lowest (0.056 pm/Gy) and largest sensitivity (0.087 pm/Gy). Such FBG-based dosimeters have the potential to be distributed along the fiber to allow multipoint detection while having a sub-millimeter size that could prove very useful for low-dose applications, in particular for radiotherapy dosimetry.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 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".