Investigation of Metal–H2O Systems at Elevated Temperatures: Part I. Development of a Solubility Apparatus Specialized for Super-Ambient Conditions
Bibliographic record
Abstract
Abstract Metals used in aqueous environments where high temperatures and pressures are present are susceptible to corrosion. This is the case for nuclear power plants, especially CANDUTM reactors, where the liquid water systems can reach over 300 °C at pressures well above 101.325 kPa (1 atm). In such situations, failure to control corrosion has economic and safety consequences. To extend corrosion modeling tools, such as Pourbaix diagrams, to harsh aqueous conditions, there is a need for experimental thermochemical data performed at elevated temperatures. This is particularly true for metal and alloy systems where such information is unavailable or unreliable. A relatively simple approach to obtaining temperature dependent thermodynamic properties is the investigation of solid–liquid phase, or solubility, equilibria. However, this requires specially designed instrumentation that can withstand harsh temperatures and extreme pH conditions, while providing accurate data. In this work, an apparatus developed for super-ambient highly acidic and alkaline solubility experiments is presented. Solubility measurements were made in the zinc oxide system, which has been extensively studied. Using these equilibrium data and comparing to the literature, allowed the instrumentation and analysis process to be validated. In situ pH measurements using a constant volume, batch reactor system are described along with a brief presentation of the ZnO dissolution equilibria results at 85 °C (358.15 K).
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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 | 0.001 |
| 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".