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Record W2725759259 · doi:10.1149/2.0511709jes

Inverse Crevice Corrosion of Carbon Steel: Effect of Solution Volume to Surface Area

2017· article· en· W2725759259 on OpenAlexaff
Linda Wu, Dan Guo, Li Mi, Jiju M. Joseph, James J. Noël, Peter Keech, J.C. Wren

Bibliographic record

VenueJournal of The Electrochemical Society · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsNuclear Waste Management OrganizationWestern University
Fundersnot available
KeywordsCrevice corrosionCorrosionMetallurgyMaterials scienceCarbon steelDissolutionVolume (thermodynamics)ChemistryThermodynamics

Abstract

fetched live from OpenAlex

Crevice corrosion of carbon steel was investigated in different exposure environments by performing coupon exposure and electrochemical tests. The extent of corrosion on the bold surface of a carbon steel crevice coupon was more severe at 80°C than at 21°C, in aerated rather than dearated solutions, and with γ-radiation present. In contrast to normal crevice corrosion, we observed 'inverse crevice corrosion' behavior, the phenomenon where it is the corrosion on the bold surface that is accelerated when coupled, rather than that on the crevice surface. The coupling current measured between a crevice and a bold electrode in an electrochemical cell was also negative. This inverse crevice corrosion behavior is attributed to a significantly lower metal cation dissolution capacity of the small occluded water volume in the crevice, compared to that of the bulk water volume over the bold surface. The reduction in dissolution capacity results in faster and earlier formation of a protective oxide layer. Corrosion of the bold and crevice surfaces evolves at different rates, leading to galvanically accelerated corrosion of the bold surface. The effect of γ-radiation on corrosion evolution in different solution environments leading to inverse crevice corrosion is discussed.

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 score0.004
Threshold uncertainty score0.304

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 science0.0010.000
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 head0.013
GPT teacher head0.259
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 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

Citations7
Published2017
Admission routes1
Has abstractyes

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