Stability of Chromia (Cr2O3)-Based Scales Formed During Corrosion of Austenitic Fe-Cr-Ni Alloys in Flowing Oxygenated Supercritical Water
Bibliographic record
Abstract
The comparative corrosion resistance of two high-chromium austenitic Fe-Cr-Ni alloys, namely Type 310S stainless steel (UNS S31008) and Alloy 33 (UNS R20033), was examined after exposure in supercritical water (25 MPa and 550°C), using a flow-loop autoclave testing facility operated at a flow rate of 200 mL/min. Electron microscopy techniques were used to determine links between the composition and structure of the Cr2O3-based oxide scale formed on both alloys and the difference in corrosion resistance observed. The weight change kinetics was distinctly different: progressively positive (weight gain) for Type 310S stainless steel and progressively negative (weight loss) for Alloy 33. The descaled weight loss was lower for Alloy 33, indicating improved corrosion resistance. This improved corrosion resistance was attributed to the improved stability of the Cr2O3-based scale that formed on Alloy 33 despite the negative weight change kinetics. The suitability of these alloys as candidate fuel cladding for the Generation IV supercritical water-cooled reactor concept is discussed in light of the findings presented.
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".