SU‐FF‐T‐363: Plastic Scintillator Preparation and Coupling in Scintillation Dosimetry
Bibliographic record
Abstract
Introduction: One way to improve the performance of scintillation dosimeters is to increase the light collection efficiency at the coupling interfaces of the detector. The present work present a detailed study of scintillating fiber preparation and their coupling to clear optical fibers in order to minimize light losses and to increase the light signal collected. Methods and Materials: Surface polishing with aluminum oxide sheets, reflector coating with MgO and use of eight different coupling agents (air, three optical gels, optical curing, ultraviolet curing, cyanoacrylate glue and acetone) were considered. For each coupling technique, ten samples were prepared. The procedure followed was: first, both the scintillating fiber and the optical fiber were cut. Then, each extremity was cleaned and polished. Finally the coupling between the scintillating fiber and the optical fiber was made either in a polyethylene cylinder or in a V‐grooved support depending on the kind of coupling agent used. To produce a large quantity of light, a UV lamp was used to stimulate scintillation. Results: A typical series of similar couplings showed a standard deviation equal to 10 %. This can be explained by the difference in the surface quality and the alignment of the scintillating fiber over the optical fiber. Surface polishing improves the light collection by approximately 65 % and a reflective coating on the distal end of the scintillator by approximately 40 %. For the coupling agents, the best results were obtained using an optical gel. Conclusion: In plastic scintillation dosimetry, there is usually a compromise to be made between the spatial resolution of the probe (i.e. its size) and the total signal collected by the photodetector. Since a large amount of the light produced inside the scintillator is usually lost, a better collection efficiency will result in improved spatial resolution for a given signal intensity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".