Characterization of novel 3D printed plastic scintillation dosimeters
Bibliographic record
Abstract
Abstract We propose a new methodology for the fabrication and evaluation of scintillating detector elements using a consumer grade fusion deposition modeling (FDM) 3D printer. In this study we performed a comprehensive investigation into both the effects of the 3D printing process on the scintillation light output of 3D printed plastic scintillation dosimeters (PSDs) and their associated dosimetric properties. Fabrication properties including print variability, layer thickness, anisotropy and extrusion temperature were assessed for 1 cm 3 printed samples. We then examined the stability, dose linearity, dose rate proportionality, energy dependence and reproducibility of the 3D printed PSDs compared to benchmarks set by commercially available products. Experimental results indicate that the shape of the emission spectrum of the 3D printed PSDs do not show significant spectral differences when compared to the emission spectrum of the commercial sample. However, the magnitude of scintillation light output was found to be strongly dependent on the parameters of the fabrication process. Dosimetric testing indicates that the 3D printed PSDs share many desirable properties with current commercially available PSDs such as dose linearity, dose rate independence, energy independence in the MV range, repeatability, and stability. These results demonstrate that not only does 3D printing offer a new avenue for the production and manufacturing of PSDs but also allows for further investigation into the application of 3D printing in dosimetry. Such investigations could include options for 3D printed, patient-specific scintillating dosimeters that may be used as standalone dosimeters or incorporated into existing 3D printed patient devices (e.g. bolus or immobilization) used during the delivery of radiation therapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".