Mechanistic Studies of the Crevice Corrosion of Alloy 22 in Chloride-Nitrate Solutions
Bibliographic record
Abstract
Abstract Alloy 22 is susceptibility to crevice corrosion in concentrated chloride solutions at elevated temperature. A number of oxyanions, most notably nitrate, sulfate, and carbonate, inhibit the aggressiveness of the chloride ion. The ratio of aggressive to inhibitive anions is a key parameter in predicting the possibility of the crevice corrosion of waste packages in the Yucca Mountain repository. The results of a preliminary experimental program to study the effects of chloride and nitrate ions on the propagation and stifling of the crevice corrosion of Alloy 22 are described. A coupled-electrode technique was used to study the stifling of crevice corrosion in various solutions following artificial initiation achieved through galvanostatic polarization. The environments studied included CaCl2-NaNO3 mixtures with varying [NO3−]:[Cl−] ratio and total salinity and a solution of 5 mol·dm-3 NaCl with and without added NO3−. The test temperature in all cases was 120° C. Even with electrochemical polarization, it was found difficult to initiate crevice corrosion in CaCl2 solutions containing nitrate. Some superficial damage was observed in concentrated solutions. In contrast, initiation occurred readily in NaCl solutions, although the coupled current decreased quickly in nitrate-containing solutions, indicating stifling of the crevice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".