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Record W4224303948 · doi:10.1080/1478422x.2022.2065419

Electrochemical thermodynamics of stress corrosion of pipeline steel in active and passive environments studied by scanning Kelvin probe

2022· article· en· W4224303948 on OpenAlexafffund
Yicheng Wang, Y. Frank Cheng

Bibliographic record

VenueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCorrosionVolta potentialKelvin probe force microscopeDissolutionMaterials scienceMetallurgyStress (linguistics)ElectrochemistryElectrochemical potentialPassivityChemistryElectrodePhysical chemistryNanotechnology

Abstract

fetched live from OpenAlex

Electrochemical corrosion thermodynamics of an X52 pipeline steel under plastic stresses was studied by measurements of corrosion potential and Volta potential with a scanning Kelvin probe in an anaerobic near-neutral pH and an aerobic high pH corrosive environments, where the steel was under active dissolution and passivity, respectively. The stress-corrosion thermodynamics of the stressed steel in both environments was studied by Volta potential measurements. While the corrosion potential shifts negatively due to a stress-enhanced corrosion activity in both solutions, the shift of Volta potential is more indicative of the corrosion thermodynamics under identical stresses. Volta potential measurements provide a more promising method than corrosion potential to indicate the stress corrosion thermodynamics of the steel. The relationships between Volta potential and corrosion potential in both active and passive solutions deviate from linearity in the presence of stress. However, the difference between the two potentials increases linearly with stress .

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 score0.049
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.004
GPT teacher head0.216
Teacher spread0.212 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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Same venueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion ControlSame topicCorrosion Behavior and InhibitionFrench-language works237,207