Evaluation of the potential for diacetylenes as reporter molecules in 3D micelle gel dosimetry
Bibliographic record
Abstract
Radiochromic micelle gel dosimeters are promising for three-dimensional (3D) radiation dosimetry because they can be read out by optical CT techniques and they have superior spatial stability compared to polymer and Fricke gel dosimeters. This study evaluates the use of diacetylenes as reporter molecules in micelle gel dosimeters. Several gels containing pentacosa-10,12-diynoic acid (PCDA) emulsified using sodium dodecyl sulfate (SDS) changed from colourless to blue upon irradiation. Unfortunately, all phantoms that experienced a colour change were turbid and would be unsuitable for 3D dosimetry. Two techniques (use of organic solvent and aqueous-phase additives) were successful in increasing colloidal stability to prevent the turbidity problem, but none of the resulting transparent gels changed colour in response to radiation. Transparent PCDA emulsions were prepared using NaOH solutions with no SDS or other emulsifier, but these transparent emulsions also did not change colour. Only turbid gels and emulsions with precipitated particles responded to radiation. These results indicate that the colour change was due to the oligomerization within precipitated PCDA crystals, and that liquid-phase emulsified PCDA did not undergo oligomerization. As a result, PCDA is not suitable for use in micelle gel dosimeters, and other radiochromic reporter molecules will need to be identified.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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 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".