Effects of gel composition on the radiation induced density change in PAG polymer gel dosimeters: a model and experimental investigations
Bibliographic record
Abstract
Due to a density change that occurs in irradiated polyacrylamide gel (PAG), x-ray computed tomography (CT) has emerged as a feasible method of performing polymer gel dosimetry. However, applicability of the technique is currently limited by low sensitivity of the density change to dose. This work investigates the effect of PAG composition on the radiation induced density change and provides direction for future work in improving the sensitivity of CT polymer gel dosimetry. A model is developed that describes the PAG density change (delta(rho)gel) as a function of both polymer yield (%P) and an intrinsic density change, per unit polymer yield, that occurs on conversion of monomer to polymer (delta(rho)polymer). %P is a function of the fraction of monomer consumed and the weight fraction of monomer in the unirradiated gel (%T). Applying the model to experimental CT and Raman spectroscopic data, two important fundamental properties of the response of PAG density to dose (delta(rho)gel dose response) are discovered. The first property is that delta(rho)polymer)depends on PAG %C (cross-linking fraction of total monomer) such that low and high %C PAGs exhibit a higher deltarho(polymer)than do more intermediate %C PAGs. This relationship is opposite to the relationship of polymer yield to %C and is explained by the effect of %C on the type of polymer formed. The second property is that the delta(rho)gel dose response is linearly dependent on %T. From the model, the inference is that, at least for %T < or = 2%, monomer consumption and delta(rho)polymer depend solely on %C. In terms of optimizing CT polymer gel dosimetry for high sensitivity, these results indicate that delta(rho)polymer can be expected to vary with each polymer gel system and thus should be considered when choosing a polymer gel for CT gel dosimetry. However, delta(rho)polymerand %P cannot be maximized simultaneously and maximizing %P, by choosing gels with intermediate %C and high %T, is found to have the greatest impact on increasing the sensitivity of PAG density to dose. As such, future research into new gel formulations for high sensitivity CT polymer gel dosimetry should focus on gels that exhibit an intrinsic density change and maximizing polymer yield in these systems.
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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".