Corrosion of UNS R30006 in High-Temperature Water Under Intermittent Mechanical Contact
Bibliographic record
Abstract
The wear and corrosion of UNS R30006 in water saturated with hydrogen or nitrogen at various temperatures and pH values (adjusted with boric acid [H3BO3] and lithium hydroxide [LiOH]) were studied using the techniques of linear polarization resistance and potentiodynamic sweep. The immediate change of corrosion rate caused by mechanical wear was similar in various water chemistries; at 150°C, 200°C, and 250°C, corrosion rates increased during wear and then dropped to their original values, while at 65°C and 25°C the rates before and immediately after wear were about the same. During continuous exposure to high-temperature water saturated with hydrogen at various pH or with nitrogen at pH300°C = 6.5, the corrosion rate of UNS R30006 was somewhat variable, but in general, it increased with time during the periods investigated. At all pH, between 6.5 and 7.8 (at 300°C), the corrosion rate of UNS R30006 without wear generally was highest at pH300°C = 7.4, corresponding to the highest concentration of lithium; the corrosion rate of UNS R30006 at 250°C, in fact, increased with an increasing concentration of LiOH regardless of pH. Prolonged exposure of UNS R30006 to high-temperature water saturated with nitrogen at pH300°C = 6.5 reduced corrosion rates.
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 | 0.001 |
| 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 | 0.000 |
| 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".