Mitigating the Stress Corrosion Cracking of Zircaloy-4 Fuel Sheathing: Siloxane Coatings Revisited
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For more than 50 years, a thin (3–20 μm) graphite coating has played an important role in limiting the stress corrosion cracking (SCC) of Zircaloy-4 fuel sheathing in CANDU® nuclear reactors. Siloxane coatings, which were examined alongside graphite coatings in the early 1970s, demonstrated even better tolerance against power-ramp-induced SCC and exhibited better wear resistance than graphite coatings. Although siloxane technology developed significantly in the 1980s/1990s, siloxane coatings remain unused in CANDU reactors, because graphite is relatively inexpensive and performs well in-service. However, advanced CANDU designs will accommodate average burnups, exceeding the threshold tolerable by the graphite coating (450 MWh/kgHE). In addition, siloxane coatings may find applicability in pressurized and boiling water reactors, wherein the burnups are inherently larger than those in CANDU reactors. Consequently, a commercially available siloxane coating is evaluated by its present-day chemistry, wear resistance, and performance in hot, stressful, and corrosive environments. After subjecting slotted Zircaloy-4 rings to iodine concentrations exceeding the estimated in-reactor concentration (1 mg/cm3), mechanical deflection tests and scanning electron microscopy (SEM) show that the siloxane coating outperforms the graphite coating in preserving the mechanical integrity of the rings. Furthermore, the baked siloxane coating survived a 50-day exposure to thermal neutron flux ((2.5±0.1)×1011 n/cm2 s) in the SLOWPOKE-2 nuclear reactor at the Royal Military College of Canada.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it