Irradiation Induced Defect Clustering in Zircaloy-2
Bibliographic record
Abstract
The effect of irradiation temperature and alloying elements on defect clustering behaviour directly from the cascade collapse in Zircaloy-2 is examined. The in-situ ioWn irradiation technique was employed to study the formation of -type dislocation loops by Kr ion irradiation at 573 K and 773 K, while the dependence of dislocation loop formationon the presence of alloying elements was investigated by comparing with the defect microstructures of pure Zr irradiated under similar conditions. The experimentally observed temperature dependence of defect clustering was further investigated using molecular dynamics (MD) simulations near the experimental irradiation temperatures. We particularly concentrate on yield and morphology of small defect clusters formed directly from cascade collapse at very low ion doses. Smaller loop size and higher defect yield (DY) in Zircaloy-2 as compared to pure Zr suggests that the presence of the major alloying element Sn increases the number of nucleation sites for the defect clusters but suppresses the point defect recombination. MD simulations at 600 and 800 K revealed that the production of both vacancy and interstitial clusters drops significantly with an increase of irradiation temperature, which is reflected in experimentally collected DY data.
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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".