The Influence of Dissolved hydrogen on Nickel Alloy SCC: A Window to Fundamental Insight
Bibliographic record
Abstract
Prior stress corrosion crack growth rate (SCCGR) testing of nickel alloys as a function of the aqueous hydrogen concentration (i.e., the concentration of hydrogen dissolved in the water) has identified different functionalities at 338 and 360 C. These SCCGR dependencies have been uniquely explained in terms of the stability of nickel oxide. The present work evaluates whether the influence of aqueous hydrogen concentration on SCCGR is fundamentally due to effects on hydrogen absorption and/or corrosion kinetics. Hydrogen permeation tests were conducted to measure hydrogen pickup in and transport through the metal. Repassivation tests were performed in an attempt to quantify the corrosion kinetics. The aqueous hydrogen concentration dependency of these fundamental parameters (hydrogen permeation, repassivation) has been used to qualitatively evaluate the film-rupture/oxidation (FRO) and hydrogen assisted cracking (HAC) SCC mechanisms. This paper discusses the conditions that must be imposed upon these mechanisms to describe the known nickel alloy SCCGR aqueous hydrogen concentration functionality. Specifically, the buildup of hydrogen within Alloy 600 (measured through permeability) does not exhibit the same functionality as SCC with respect to the aqueous hydrogen concentration. This result implies that if HAC is the dominant SCC mechanism, then corrosion at isolated active path regions (i.e., surface initiation sites or cracks) must be the source of localized elevated detrimental hydrogen. Repassivation tests showed little temperature sensitivity over the range of 204 to 360 C. This result implies that for either the FRO or the HAC mechanism, corrosion processes (e.g., at a crack tip, in the crack wake, or on surfaces external to the crack) cannot by themselves explain the strong temperature dependence of nickel alloy SCC.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".