Surface preparation and coupling in plastic scintillator dosimetry
Bibliographic record
Abstract
One way to improve the performance of scintillation dosimeters is to increase the light-collection efficiency at the coupling interfaces of the detector system. We performed a detailed study of surface preparation of scintillating fibers and their coupling with clear optical fibers to minimize light loss and increase the amount of light collected. We analyzed fiber-surface polishing with aluminum oxide sheets, coating fibers with magnesium oxide, and the use of eight different coupling agents (air, three optical gels, an optical curing agent, ultraviolet light, cyanoacrylate glue, and acetone). We prepared 10 scintillating fiber and clear optical fiber light guide samples to test different coupling methods. To test the coupling, we first cut both the scintillating fiber and the clear optical fiber. Then, we cleaned and polished both ends of both fibers. Finally, we coupled the scintillating fiber with the clear optical fiber in either a polyethylene jacket or a V-grooved support depending on the coupling agent used. To produce more light, we used an ultraviolet lamp to stimulate scintillation. A typical series of similar couplings showed a standard deviation in light-collection efficiency of 10%. This can be explained by differences in the surface preparation quality and alignment of the scintillating fiber with the clear optical fiber. Absence of surface polishing reduced the light collection by approximately 40%, and application of magnesium oxide on the proximal end of the scintillating fiber increased the amount of light collected from the optical fiber by approximately 39%. Of the coupling agents, we obtained the best results using one of the optical gels. Because a large amount of the light produced inside a scintillator is usually lost, better light-collection efficiency will result in improved sensitivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".