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Record W4285013374 · doi:10.5006/4083

Passivity Breakdown of Copper in Borate Buffer Solutions Containing Cl−, SO42−, and NO3−

2022· article· en· W4285013374 on OpenAlexaff
Yuting Zhou, Danbin Jia, Feixiong Mao, Jingkun Yu, Edouard Asselin

Bibliographic record

VenueCORROSION · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCopperPassivityCorrosionDielectric spectroscopyMaterials scienceScanning electron microscopeErosion corrosion of copper water tubesElectrochemistryMetallurgyCopper oxideAdsorptionOxidePolarization (electrochemistry)Raman spectroscopyInorganic chemistryChemistryElectrodeComposite material

Abstract

fetched live from OpenAlex

The effects of Cl−, , and on the corrosion of copper in slightly alkaline, deaerated borate buffer solutions (BBS) were analyzed by potentiodynamic polarization, in situ surface-enhanced Raman spectroscopy, electrochemical impedance spectroscopy, scanning electron microscopy, and confocal microscope. Results showed that all three ions significantly affected the corrosion of copper in BBS, leading to a decrease in the breakdown potential for copper, thereby promoting passivity breakdown. The adsorption of Cl−, , and on the copper oxide film surface was detected, forming corrosion products, atacamite, brochantite, and gerhardtite, respectively. The passivity breakdown occurred at a lower potential for -containing solutions than for those with the other ions. The most severe corrosion morphology was obtained in -containing solutions, and large-scale pits with deep depths were distributed on the copper surface after passivity breakdown. In comparison, small pits and laterally growing pits and/or local rupture of the passive film occurred on the copper surface in the solution containing Cl− or after passivity breakdown.

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 score0.046
Threshold uncertainty score0.896

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 science0.0000.000
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 head0.026
GPT teacher head0.253
Teacher spread0.227 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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