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Record W2994652276 · doi:10.1002/maco.201911335

Investigation of micro‐electrochemical activities of oxide inclusions and microphases in duplex stainless steel and the implication on pitting corrosion

2019· article· en· W2994652276 on OpenAlexaff
Yaohua Zhang, Qian Hu, Mingjie Dai, Feng Huang, Jing Liu

Bibliographic record

VenueMaterials and Corrosion · 2019
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsMaterials sciencePitting corrosionCorrosionMetallurgyOxideElectrochemistryInclusion (mineral)Crevice corrosionDual-phase steelPhase (matter)Duplex (building)MicrostructureElectrodeChemistryMineralogyMartensite

Abstract

fetched live from OpenAlex

Abstract In this study, the oxide inclusions contained in the 2205 duplex stainless steel were characterized, and the pitting corrosion initiated at the inclusions was statistically analyzed. The micro‐electrochemical behavior was measured by a home‐designed capillary microelectrode technique. Results show that three types of oxide inclusions are contained in the steel, namely, Al–Mg–O (inclusion A), Al–Si–Ca–O (inclusion B), and Al–Mg–Si–Ca–O (inclusion C) inclusions. Inclusion A possesses higher electrochemical stability than the steel substrate, and there is no corrosion pit initiated on the inclusion. Inclusions B and C are more electrochemically active than the steel, and pitting corrosion occurs on both inclusions. The α phase has higher electrochemical activity and lower corrosion resistance than the γ phase. Pitting corrosion is more likely to occur at the inclusion/α phase interface, rather than the inclusion/γ phase interface. When the inclusion is located at the α/γ interface, the pit initiation is dominated by the α phase on the coexisting dual phases.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.240
Teacher spread0.229 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations31
Published2019
Admission routes1
Has abstractyes

Explore more

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