Investigation of Metal–H2O Systems at Elevated Temperatures: Part III: Solubility Data and New Zr Pourbaix Diagrams at 298.15 K and 373.15 K
Bibliographic record
Abstract
Abstract The fuel bundles in coolant systems of CANDU™ reactors operate between 250 and 310 °C (523 and 583 K). Given these extreme conditions and because of its neutronic properties, zirconium and Zircaloy-4 are used in these applications, which require corrosion resistance at elevated temperatures. However, thermodynamic and hydrolysis properties of aqueous zirconium species have not been measured above standard conditions, making prediction using standard thermodynamic tools, such as the Pourbaix (E-pH) diagram difficult. This lack of information is addressed through solubility measurements and the development of elevated temperature Pourbaix diagrams for zirconium and Zircaloy-4. These Pourbaix diagrams of zirconium and a multi-element diagram (Sn, Zr, Cr) of Zircaloy-4 were developed at 100 °C (373.15 K) and are presented in this work. Solubility measurements were made using a batch-style pressure vessel and concentration measurements were made using inductively coupled plasma optical emission spectroscopy (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS), for zirconium and Zircaloy-4, respectively. For Zr(OH)62−; Zr(OH)4 (aq); Zr(OH)3+; and Zr(OH)22+, the Gibbs energy of formation (ΔGf, 100 °C°) was found to be −2177.4±8.5 kJ/mol, −1704.9±1.5 kJ/mol, −1808.8±8.9 kJ/mol, and −1095.1±2.7 kJ/mol, respectively.
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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 | 0.001 | 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".