Radiation damage evaluation of materials for radiotherapy quality assurance devices under high dose and ultra-high dose rate electron and proton beams
Bibliographic record
Abstract
Abstract FLASH radiotherapy is a promising technique based on the delivery of ultra-high dose rates (UHDR) to spare healthy tissue. Quality Assurance (QA) devices such as phantoms and ionization chambers are typically made out of polymeric materials. This study aims to characterize radiation damage in common materials used for QA devices and identify which of them can withstand UHDR conditions. A variety of materials commonly used in phantoms and ionization chambers, with diverse characteristics (transparent, opaque, conductive and non-conductive) were irradiated using 68 MeV proton and 20 MeV electron beams, reaching doses up to 1 MGy under UHDR conditions (average dose rates of 100 – 500 Gy/s). Material damage was evaluated through optical, chemical, mechanical and electrical tests. External appearance of some materials significantly changed after irradiation. All transparent materials exhibited change in color post-irradiation. Chemical analyses conducted after irradiation and repeated two years later indicated partial recovery in some materials. No significant difference in damage was observed between proton and electron irradiation, suggesting comparable radiation damage mechanisms. Dose rate alone did not exacerbate damage beyond total dose effects; however, extended irradiation at high dose rates resulted in thermal damage under some conditions. Mechanical testing revealed increased fragility, changes in hardness and dimensions, while some materials experienced significant changes in dielectric constant and conductivity. Materials such as PMMA, PC, POM, and its conductive variant POM ELS, are unsuitable for prolonged irradiations at UHDR due to significant structural degradation observed. For see-through phantom construction, CPS offers a more durable alternative to PMMA. For detector construction, PEEK (for non-conductive parts)and graphite (for conductive parts) demonstrated promising durability, making them preferable choices under high dose and UHDR conditions. The higher stability of these materials can be attributed to the presence of aromatic rings in their chemical structure, which enhances radiation resistance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".