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Record W2789387756 · doi:10.1038/s41529-018-0026-5

The role of titanium in the initiation of localized corrosion of stainless steel 444

2018· article· en· W2789387756 on OpenAlexafffund
Samantha Michelle Gateman, Lisa I. Stephens, Samuel C. Perry, Robert Lacasse, Robert Schulz, Janine Mauzeroll

Bibliographic record

Venuenpj Materials Degradation · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsHydro-QuébecMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsCorrosionIntergranular corrosionMaterials scienceMetallurgyTitaniumScanning electron microscopeElectrochemistryMetalPitting corrosionComposite materialElectrodeChemistry

Abstract

fetched live from OpenAlex

Abstract Titanium has been added to ferritic stainless steels to combat the detrimental effects of intergranular corrosion. While this has proven to be a successful strategy, we have found that the resulting Ti-rich inclusions present on the surface play a significant role in the initiation of other forms of localized corrosion. Herein, we report the effect of these inclusions on the localized corrosion of a stainless steel using macro and micro electrochemical techniques. Through the use of scanning electrochemical microscopy, we observe the microgalvanic couple formed between the conductive inclusions and passivated metal matrix. The difference in local reactivity across the material’s surface was quantified using a 3D finite element model specifically built to respect the geometry of the corrosion-initiating features. Combined with electron microscopy and micro elemental analysis, localization of other alloying elements has been reported to provide new insight on their significance in localized corrosion resistance.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.264
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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

Citations44
Published2018
Admission routes2
Has abstractyes

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