Modeling of Free‐radical Crosslinking Copolymerization of Acrylamide and <i>N</i>,<i>N</i>′‐Methylenebis(acrylamide) for Radiation Dosimetry
Bibliographic record
Abstract
Abstract Over the past decade there has been much interest in the development of three‐dimensional gel dosimeters to aid in the determination of the distribution and magnitude of absorbed dose in clinical radiation therapy. A widely used dosimeter for verification of spatial dose distribution is the polyacrylamide system, sometimes termed polyacrylamide gel (PAG). In this paper, we develop a model to describe the kinetic mechanisms in this gel dosimeter, based on the radiation‐induced copolymerization of acrylamide and bisacrylamide monomers in water and gelatin. Using the proposed model, the conversion of both monomers and total vinyl groups, the concentrations of pendant double bonds (PDB), cyclized groups and crosslinks along the polymer chains as well as temperature changes were simulated. The model predictions and experimental findings in PAG dosimeters agree for a variety of recipes and spatially uniform radiation conditions. Further experiments are required to obtain accurate kinetic parameter estimates and to verify model predictions. Comparison between the temperatures changes predicted by the model (dotted lines) and experimental data (solid lines) for PAGs irradiated to different doses. image Comparison between the temperatures changes predicted by the model (dotted lines) and experimental data (solid lines) for PAGs irradiated to different doses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".