Nickel‐based alloy corrosion in CANDU steam generators: <i>E</i>–pH diagrams of the Ni–NH<sub>3</sub>–H<sub>2</sub>O and Ni–CH<sub>3</sub>COO<sup>−</sup>–H<sub>2</sub>O ternary systems
Bibliographic record
Abstract
Abstract Nuclear power plant steam generator (SG) tubes contain nickel as the main alloying element and there is concern about their corrosion. We optimized models for calculating the high‐temperature and high‐pressure thermodynamic properties of common nuclear alloy elements. Subsequently, we calculated the E–pH diagrams for nickel in different concentrations of ammonia or acetate ion at temperatures ranging from 100°C to 260°C and a pressure of 4.7 MPa. This information is used to predict the corrosion behavior of nickel in the secondary circuit conditions of Canadian Deuterium Uranium (CANDU) SGs. Increasing the ammonia or acetate ion concentration resulted in the predominance of Ni(NH3)n2+ or Ni(Ac)n(2−n) at the expense of Ni2+ and NiO, indicating a higher risk of nickel corrosion. Calculations showed that under normal operating conditions with [NH3]tot = 5 × 10−5 and [CH3COO−]tot = 10−8 m at 260°C and 4.7 MPa, nickel will be passivated as NiO, preventing the rapid degradation of nickel‐based alloys. However, in the presence of a crevice that allows the acetate ion to concentrate, nickel would dissolve in the form of the Ni2+ ion, endangering safe operation of CANDU SGs.
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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".