Valency conversion of samarium ions under high dose synchrotron generated X‐ray radiation
Bibliographic record
Abstract
Abstract Microbeam Radiation Therapy (MRT) is a synchrotron based technique for cancer treatment. During MRT tumours are irradiated with micro‐planar arrays of synchrotron‐generated X‐rays. The quality assurance of such a technique depends on the accurate measurement of very high dose levels (>1000 Gy) together with a precise measurement of the dose distribution over a scale of a few microns. For such applications we need to have a high dynamic range, high‐dose detector with very high spatial resolution. As part of the work towards the development of such a detector we have examined the X‐ray radiation induced conversion of rare‐earth ions, especially Sm 3+ to Sm 2+ , embedded in suitable media. We have exposed a variety of Sm 3+ doped media, from single crystals to glass‐ceramics, to a high dose radiation at the Canadian Light Source to examine whether we could convert the rare‐earth ion valency. The irradiation was carried out at a bending magnet beamline using filtered white beam with a spectrum peaking at 50 keV. We have used the distinctly different signatures in the photoluminescence emission spectra of the two valency states to identify the rare‐earth ions and whether a valency conversion has taken place. We report the results from these experiments, which show that the conversion of Sm 3+ to Sm 2+ is not universal and depends very much on the host material. We discuss possible reasons for the presence and lack of radiation induced valency conversion of Sm‐ions, and identify the challenges that must be overcome to develop a high‐resolution, high‐dose detector. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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".