Advances in transition metals and rare earth elements doped ZnO as thermoluminescence dosimetry material
Bibliographic record
Abstract
The numerous benefits like smaller size, easy handling, quicker readouts and cost effectiveness render TLDs as prominent radiation dose measuring instruments. Zinc Oxide (ZnO) is well recognized among these TLD materials due to its remarkable post exposure glow properties. Moreover, along with reasonable TL properties it inherently shows good mechanical and chemical stability, non-toxic and non-hygroscopic nature. However, in comparison to its contemporaries it lacks in sensitivity when exposed to low radiation doses and exhibits moderate fading effects. These draw backs stem from ZnO electronic nature i.e. availability of low number of radiation responding electrons along with trapping centers which can retain these electrons and are responsible for thermal assisted glow curve. One of the solutions is doping it with high atomic number metallic ions. Transition metals (TM) and rare earth (RE) elements doped ZnO materials show promising results i.e. doped ZnO shows improved TL response i.e. more linearity, better stability and less fading. As TM and RE doped ZnO appears to be promising TLD materials, this review provides an overview of research on TL characteristics of TM and RE doped ZnO materials.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".