Selective Dissolution and Oxidation Zones in Ni-Cr-Fe Space and Their Relationship to Stress Corrosion Cracking
Bibliographic record
Abstract
Abstract A framework is presented that may explain a wide range of stress corrosion behaviour in aqueous environments at high temperatures, using only two hypotheses for the applicable crack growth mechanisms. The framework is mainly relevant to reducing (hydrogenated) environments, but may account for some aspects of SCC in oxidizing ones. Binary Fe-Ni alloys have been used to explore some of these issues, as well as engineering alloys. In the Ni-rich region, SCC propagates by intergranular oxidation; high Cr content, as in Alloy 690, provides protection by forming an external Cr-rich oxide. In Fe-rich alloys, classical de-alloying occurs in hot caustic solutions; on austenitic stainless steel or binary Fe-10Ni this generates a nanoporous metallic layer enriched in Ni. In high-temperature near-neutral or mildly-acid solutions, there is also Ni enrichment, most clearly seen in binary Fe-10Ni. This Ni accumulates under a magnetite film. It is not yet known whether this Ni is also nanoporous, but the behaviour of noble-metal alloys in analogous conditions suggests that it may be. Higher Ni contents, as in Alloy 800 or model binary alloys, protect against SCC by forming a very thin protective Ni layer rather than a porous one, in accordance with ordinary de-alloying theory.
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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".