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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 teacher head, 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".