Quantifying the impact of lead doping on plastic scintillator response to radiation
Bibliographic record
Abstract
Purpose Through the addition of high‐Z dopants, the sensitivity of plastic scintillators to low‐energy radiation can be increased. This study quantifies this change in sensitivity as a function of dopant concentration. Methods Measurements were conducted using four different lead‐doped scintillators (0%, 1%, 1.5%, and 5% Pb) in high‐energy electrons (6 to 15 MeV) and low‐energy photon (100 to 300 kVp) radiation fields. High‐energy and low‐energy irradiations were done using a clinical linear accelerator and an orthovoltage unit, respectively. Light emitted by the scintillator was quantified using a photosensor module. The experimental setup was replicated in Geant4.10.3 Monte Carlo and scintillator parameters (Quenching parameter: kB and the light yield: L 0 ) were varied until agreement between measured and simulated results was reached. Monoenergetic electrons were used to simulate the high‐energy electron beam while a spectrum generated using SpekCalc ® software was used in the low‐energy simulations. Light produced by the scintillator was quantified using a flux scorer sensitive only to photons in the visible wavelength range. In order to compare measured and simulated results, the light produced by the scintillator was normalized to the absorbed dose‐to‐water at the point of measurement. Results At high lead dopant concentrations, the scintillator's sensitivity to the 100 kVp beam increased by 474% relative to the 15 MeV electron beam; the scintillator's kB parameter increased from 0.126 to 0.27 mm/MeV. A model quantifying the change in kB and L 0 as a function of Z eff was derived; presenting a modified Birks' Law for metal‐doped plastic scintillators. Conclusion The impact of high‐ Z doping on plastic scintillator response was quantified; this can allow for the controlled induction of energy dependence in plastic scintillator detectors.
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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".