Corrosion Inhibition by Sulfate after Surface Preparation
Bibliographic record
Abstract
Electropolishing as a surface preparation technique is increasing in popularity in industrial applications and for corrosion studies. Electropolished surfaces have shown a better resistance to pitting corrosion over mechanical polishing, however, the fundamental reason governing the change in corrosion behaviour remains unclear. This study examined the corrosion behaviour of 13Cr4Ni stainless steel (UNS S41500) after five surface preparation techniques and shows that sulfate is incorporated in the oxide film when it is present in the electropolishing solution. Even after removal from the sulfate-containing solution, the sulfate incorporation increases the material’s pitting resistance by lowering the number of sites available for chloride to induce pitting. This work also demonstrates that, when used as a counter electrode, Pt can dissolve and reprecipitate on the working electrode surface during electropolishing. The deposits result in a more noble open circuit potential, indicating an artificial increase in passivity. These artificial changes to corrosion behaviour due to surface preparation method may result in erroneous conclusions. To establish fair comparisons between surface preparation methods, the counter electrode and the sulfate effect should be strictly considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".