The Nanoscale Microstructure and Mechanics of the Oxide Formed on Zr-2.5Nb Alloy Exposed to High-Temperature Water
Bibliographic record
Abstract
Zr-2.5Nb alloy is used in Canada Deuterium Uranium (CANDU®) reactors as a pressure boundary material for pressure tubes (PT). Understanding the degradation of PT during service is crucial for ensuring the safe and long-term operation of reactors. In this research, a comprehensive characterization of the oxide formed on Zr-2.5Nb alloy in simulated primary water conditions was conducted. This thesis aims to provide insights into the underlying degradation mechanisms in both irradiated and non-irradiated materials. The difference in corrosion resistance between the irradiated and non-irradiated samples is strongly linked to Nb-rich precipitates in α-Zr. Irradiation altered the oxide microstructure, resulting in sporadically distributed equiaxed-columnar grains with isolated nanopores. Micropillar compression data of the oxide layer revealed that the elastic modulus of the oxide layer is dependent on the nature and type of defects present in the oxide layer. The failure mode in the oxide layer is brittle. Furthermore, the observed failure mode is mixed - intergranular and transgranular - in the oxide. As the exposure time increases, the nanopore density within the oxide layer increases and the defect density at the water-oxide interface is higher than at the metal-oxide interface. These defects are micro- and nanoporosity and microcracks that effectively control the oxidation tendency of Nb in the oxide layer with +5, +4, +2 and partially oxidized states present in the oxide layer. The deuterium transport through the oxide layer directly correlates with the defect density. The concentration of deuterium is highest at the water-oxide interface and lowest at the metal-oxide interface. This correlates with a change in defect density and Nb oxidation state, which also decreases between these interfaces.
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".