Leuco-crystal-violet micelle gel dosimeters: I. Influence of recipe components and potential sensitizers
Bibliographic record
Abstract
Radiochromic leuco crystal violet (LCV) micelle gel dosimeters are promising three-dimensional radiation dosimeters because of their spatial stability and suitability for optical readout. The effects of surfactant type and surfactant concentration on dose sensitivity of LCV micelle gels are tested, demonstrating that dose sensitivity and initial colour of the gel increases with increasing Triton x-100 (Tx100) concentration. Using Cetyl Trimethyl Ammonium Bromide (CTAB) in place of Tx100 produces gels that are nearly colourless prior to irradiation, but reduces the dose sensitivity. The separate effects of Tri-chloro acetic acid concentration and pH are investigated, revealing that controlling the pH near 3.6 is crucial for achieving high dose sensitivity. The sensitizing effect of chlorinated species on dose sensitivity is tested using 2,2,2-trichloroethanol (TCE), chloroform, and 1,1,1-trichloro-2-methyl-2-propanol hemihydrate. TCE gives the largest improvement in dose sensitivity and is recommended for use in micelle gel dosimeters because it is less volatile and safer to use than chloroform. Preliminary experiments on a new gel containing CTAB as the surfactant and TCE show that this new gel gives a dose sensitivity that is 24% higher than that of previous LCV micelle gels and is nearly colourless prior to irradiation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".