Effects of Manganese on the Passivity of Fe-18Cr-<em>x</em>Mn (<em>x</em> = 0, 6, 12)
Bibliographic record
Abstract
Effects of Mn on the passivity of Fe-18Cr-xMn(x=0, 6, 12) were examined by various electrochemical tests including potentiodynamic test, micro-droplet cell test and photoelectrochemical test. With an increase in Mn content of Fe-18Cr-xMn(x=0, 6, 12) alloys, passivity of the alloys was significantly degraded in an acidified chloride solution. It was demonstrated through the micro-droplet cell tests conducted in 0.1 M NaCl solution that Mn decreased considerably the resistance to pitting corrosion of Fe-18Cr alloys even if any nonmetallic inclusions (NMI) was not included in the observed region. Passive film formed on Fe-18Cr-6Mn alloy in pH 8. 5 buffer solution is found to be composed of Cr-substituted gamma-Fe2O3 containing nano-sized Mn-oxide particles. The significant degradation in the resistance to localized corrosion of Fe-18Cr alloy, even if any NMI is absent in the alloy, appears to be associated with the nano-sized Mn-oxide particles present in the passive film.
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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) | 0.000 | 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".