Effect of Thermal Pretreatment on the Corrosion of Stainless Steel in Flowing Supercritical Water
Bibliographic record
Abstract
The effect of high-temperature microstructure degradation (thermal ageing) on the corrosion resistance of austenitic stainless steels in supercritical water (SCW) was evaluated in this study. Mill-annealed (MA) and thermally treated (TT) samples of Type 316L and Type 310S stainless steel were exposed in 25 MPa SCW at 550°C with 8 ppm dissolved oxygen in a flowing autoclave testing loop. The thermal treatments applied to Type 316L (815°C for 1000 hr + water quench) and Type 310S (800°C for 1000 hr + air cool) were successful in precipitating the expected intermetallic phases in each alloy, both within the grains and on the grain boundaries. It was found that a prolonged time at relatively high temperature was sufficient to suppress significant compositional variation across the various intermetallic phase boundaries. This paper presents the results of the gravimetric analysis and oxide scale characterization using scanning electron microscopy (SEM) coupled with X-ray energy-dispersive spectroscopy (EDS). The role played by the fine precipitate structure on formation of the oxide scale, and thus corrosion resistance, is discussed. The combined role of dissolved oxygen and flow (revealed by examining the differences between Type 316L samples exposed in a static autoclave and in the flowing autoclave loop) is also addressed. It was concluded that formation of intermetallic phase precipitates during high-temperature exposure is not likely to have a major effect on the apparent corrosion resistance because of the discontinuous nature of the precipitation.
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 | 0.000 |
| 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".