Accelerated Formation of Oxide Layers on Zircaloy-4 Utilizing Air Oxidation and Comparison with Water-Corroded Oxide Layers
Bibliographic record
Abstract
For the dry storage of Canada Deuterium Uranium (CANDU) spent nuclear fuels, the integrity of Zircaloy-4 fuel cladding has to be verified. However, the formation of ~10 µm-thick oxide layers in typical CANDU reactor operating conditions takes several years, which makes sample preparation a slow process. To overcome such limitations, in this study, an accelerated formation of an oxide layer on Zircaloy-4 cladding tube was developed with a combination of high-temperature water corrosion (HT-WC) and air oxidation (AO). First, Zircaloy-4 tubes were corroded in oxygenated (2 ppm dissolved oxygen) high-temperature water (360 °C/19.5 MPa) for 500 h. Then, the tubes were air-oxidized at 500 °C for 30 h. Finally, the tubes were corroded again in HT-WC for 500 h to produce ~10 µm-thick oxide layers. The morphology and characteristics of the oxide layer in each step were analyzed using X-ray diffraction, scanning and transmission electron microscopy. The results showed that the oxide layer formed in the accelerated method was comparable to that formed in HT-WC in terms of morphology and oxide phases. Thus, the accelerated oxide formation method can be used to prepare an oxidized Zircaloy-4 cladding tube for CANDU fuel integrity analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".