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Record W4407694419 · doi:10.1016/j.corsci.2025.112801

Topographical and statistical studies of the corrosion damage underneath a sulfide film formed on a Cu surface

2025· article· en· W4407694419 on OpenAlexafffund
Jian Chen, Xiaoqing Pan, Heng‐Yong Nie, Brad Kobe, Erik Bergendal, Christina Lilja, Mehran Behazin, David W. Shoesmith, James J. Noël

Bibliographic record

VenueCorrosion Science · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsNuclear Waste Management OrganizationWestern University
FundersNatural Sciences and Engineering Research Council of CanadaSvensk KärnbränslehanteringLuonnontieteiden ja Tekniikan Tutkimuksen ToimikuntaNuclear Waste Management Organization
KeywordsCorrosionSulfideMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Damage to Cu corroded in sulfide-containing solutions was investigated using scanning electron microscopy and surface profilometry. For dilute sulfide solutions (≤ 5 × 10 −5 M), a thin, porous film was formed, and general corrosion to a depth of < 1 μm observed. At a higher concentration (5 × 10 −4 M), a compact film was formed and micro-galvanic corrosion observed. With increasing exposure time at higher concentration, the extent of micro-galvanic corrosion decreased in favor of general corrosion, as the aspect ratio of the locally corroded sites increased. This indicates that micro-galvanic corrosion will eventually be stifled and only rough general corrosion observed. • A film pickling method to remove the sulfide formed on Cu without causing additional corrosion was developed. • Corrosion damage underneath the sulfide film formed on Cu corroded in sulfide solutions was investigated. • At low SH - concentrations, general surface roughening was observed, with most Cu surfaces corroding to a depth of ≤ 0.5 μm. • At high SH - concentration, film thickening increased Cu(I) transport resistance, reducing micro-galvanic corrosion rate. • The initiation, growth, and stifling mechanism of micro-galvanic corrosion were proposed.

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)0.001
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 score0.043
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.037
GPT teacher head0.340
Teacher spread0.303 · 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

Citations5
Published2025
Admission routes2
Has abstractyes

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