MétaCan
Menu
Back to cohort
Record W4413167743 · doi:10.1002/admi.202500383

Reduced Passivity and Enhanced Pitting Around Crack Tip Measured Using Scanning Electrochemical Cell Microscopy

2025· article· en· W4413167743 on OpenAlexafffund
Sarah R. Yassine, Egor Katkov, Pierre‐Antony Deschênes, Robert Lacasse, Janine Mauzeroll

Bibliographic record

VenueAdvanced Materials Interfaces · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsHydro-QuébecMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaHydro-Québec
KeywordsPassivityMaterials scienceScanning electrochemical microscopyPitting corrosionElectrochemistryMicroscopyScanning ion-conductance microscopyScanning electron microscopeComposite materialMetallurgyCorrosionScanning confocal electron microscopyElectrodeOpticsElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Stainless steels, widely used in industrial applications, are often subjected to combined corrosive and mechanical stress conditions, leading to corrosion fatigue. Herein, it is investigated how stress‐induced deformation impacts the localized corrosion behavior of the CA6NM martensitic stainless steel using oil‐immersed scanning electrochemical cell microscopy (SECCM). Compact tension specimens are cyclically loaded to induce plasticity around a growing crack, and electrochemical properties are mapped with high spatial resolution. The electrochemical activity is progressively changed near the crack, with open circuit potential and corrosion potential shifting toward more active values as the distance to the crack decreases. Pitting is also more frequent closer to the crack, gradually declining further away, indicating a spatial dependence in localized corrosion behavior. These findings help understanding how mechanical stress modifies passivity and pitting susceptibility, contributing to a better understanding of corrosion‐fatigue mechanisms and materials design.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.299
Teacher spread0.285 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueAdvanced Materials InterfacesSame topicCorrosion Behavior and InhibitionFrench-language works237,207