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Record W2332692219 · doi:10.1149/1.2215515

Effects of Manganese on the Passivity of Fe-18Cr-<em>x</em>Mn (<em>x</em> = 0, 6, 12)

2006· article· en· W2332692219 on OpenAlexaff
HyukSang Kwon, Kyungjin Park

Bibliographic record

VenueECS Transactions · 2006
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsPassivityAlloyManganeseCorrosionMetallurgyOxidePitting corrosionMaterials scienceElectrochemistryChlorideChemistryNuclear chemistryAnalytical Chemistry (journal)ElectrodeEnvironmental chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.013
GPT teacher head0.240
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueECS TransactionsSame topicHydrogen embrittlement and corrosion behaviors in metalsFrench-language works237,207