SU‐C‐201‐01: Core/shell and Multishell Colloidal Quantum Dots Nanodosimeters Behaviour Under Repeated MV and KV Irradiations
Bibliographic record
Abstract
Purpose: This study intends to characterize the energy dependence of the effect of radiation damage on CdSe multi‐shell (MS) (CdS/CdZnS/ZnS) and CdSe core/shell (CS)(ZnS) cQDs. It also aims to investigate irregularities resulting of pauses between subsequent irradiations. Methods: Radioluminescence (RL) measurements were performed with a CCD camera as dose was cumulated by the cQDs (up to 10 kGy), for beam energies 120 kVp, 220 kVp and 6 MV. Repeated expositions of 1999 MU were cumulated. Pauses between subsequent irradiations were varied from 2 to 50 minutes. cQDs photoluminescence (PL) and RL spectral stability was tracked by quantifying the position and FWHM of the luminescence peak. Results: Both types of cQDs showed a clear energy dependence of the RL signal decrease between the kV and the MV beams. For 1.2 kGy of dose cumulated, MS cQDs had 92% of the initial signal left at 6 MV compared to 98% at 120 kVp. The same was observed for CS cQDs: 87% at 6 MV vs 94% at 120 kVp. MS cQDs were found to have a systematic (though small, ∼1%) RL intensity recovery for pauses of 15 minutes or more, while CS cQDs maintain a stable loss regardless of the pause duration. PL and RL spectral measurements revealed a good stability (< 1% variation of the peak position and FWHM) for both types of cQDs. Conclusion: In all, both MS and CS cQDs have a sufficient resistance to large doses of radiation for standard radiation therapy and imaging. Since this resistance is better for lower energy, the utilization of cQDs could be optimized for low energy applications (e.g. theragnostic applications for small animal studies and others). Finally, the ionizing radiation damage mechanisms for this new type of nano‐scintillator still have to be identified properly.
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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 | 0.000 |
| 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 | 0.000 |
| 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".