Passivation of Ni-Cr and Ni-Cr-Mo Alloys in Low and High pH Sulfate Solutions
Bibliographic record
Abstract
The kinetics of passive oxide film formation, its thickening, and composition on Ni-22Cr and Ni-22Cr-6Mo wt% alloys were investigated at selected anodic potentials. Experiments were performed in acidic and alkaline sulfate environments using a number of characterization techniques including a combination of potentiodynamic polarization, on-line atomic emission spectro-electrochemistry (AESEC), in situ potentiostatic passive film growth, along with in situ neutron reflectometry (NR) and ex situ X-ray photoelectron spectroscopy (XPS). The roles of solution pH and Mo on the passivation behavior were discussed in terms of thermodynamic and kinetic factors governing passivation. The pH was found to have an impact on the relative chemical compositions of passive film of the Ni-22Cr alloy but not noticeably for the Ni-22Cr-6Mo alloy. Ni-rich films formed early during the passivation process while Cr(III) enrichment was observed at longer times, albeit less extensively than observed previously in Cl− solutions. The fraction of Cr(III) cations also increased with alloying of Mo at low and high pH demonstrating a strong effect of Mo on Cr(III) content during aqueous passivation in Ni based superalloys, however, a larger effect is seen in Cl−. This is a noteworthy finding as Mo is usually assumed to mainly influence pit and crevice stabilization.
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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 | 0.000 | 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".