Effects of crosslinker fraction in polymer gel dosimeters using FT Raman spectroscopy
Bibliographic record
Abstract
Radiation-induced chemical changes in polyacrylamide gels (PAGs) used in 3D radiation dosimetry have been studied using Fourier transform Raman spectroscopy. Consumption of monomer and crosslinker were characterized for polymer gel dosimeters with initial fraction of crosslinker (%C) varying from to 100%. Results indicate that the rate of monomer/crosslinker consumption is highly nonlinear over %C. This indicates that maximum polymer gel dosimeter sensitivity (i.e. slope of the monomer consumption curves) is dependent not only on initial %C but also on dose range. For low-dose regions (<5 Gy) 30%C PAGs are the most sensitive dosimeters, their maximum sensitivity being a factor of 1.8 times greater than 50%C PAGs. In mid-dose range (S-15 Gy), however, 50%C PAGs are the most sensitive dosimeters, being potentially a factor of 1.3 more sensitive than any other PAG studied. In high-dose regions (> 15 Gy) 70%C PAGs are at least 1.5 times more sensitive than any other PAG studied. A qualitative model is presented which helps to explain the variation in overall monomer consumption rate as a function of initial %C. The variation in the shapes (i.e. mathematical course) of the consumption curves for different %C PAGs is, in part, qualitatively explained by consideration of the average number of monomer to crosslinker molecules available for reaction in each PAG as it is irradiated to higher doses. Finally, we demonstrate the importance of an existing polymer network on the subsequent monomer consumption rate in a 50%C PAG.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".