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Record W4411450656 · doi:10.1016/j.corsci.2025.113121

Inhibitive effect of chloride on copper corrosion in dilute nitric acid

2025· article· en· W4411450656 on OpenAlexafffund
Sebastian Amland Skaanvik, Xuejie Li, Jessica E.M. Winslade, Heng‐Yong Nie, Jonas Hedberg, Jeffrey D. Henderson, S. Ramamurthy, Peter Keech, Mehran Behazin, Mark C. Biesinger, David W. Shoesmith, James J. Noël

Bibliographic record

VenueCorrosion Science · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsNuclear Waste Management OrganizationWestern University
FundersNatural Sciences and Engineering Research Council of CanadaNuclear Waste Management Organization
KeywordsNitric acidCorrosionCopperChlorideErosion corrosion of copper water tubesChemistryInorganic chemistryMetallurgyMaterials science

Abstract

fetched live from OpenAlex

The effect of chloride on the corrosion mechansim of copper in deaerated, 0.1 M HNO 3 was studied by electrochemistry, atomic force microscopy, and scanning electron microscopy. Nitrate reduction, the first cathodic step in the corrosion pathway, was completely inhibited in the presence of ppm levels of chloride, thereby shutting down the corrosion sequence of the system involving solution reactions by copper ions and the subsequent reduction of NO 2 – . Nitrite reduction was minimally affected, suggesting that the two reduction steps differ substantially in their sensitivity to the surface state during corrosion. Our results support that the key step for the inhibitive effect of chloride is the adsorption of chloride to form an adlayer on the surface. Nitrate reduction on copper appears to be inhibited by chloride in the presence of corrosion accelerators NO 2 – and Cu + /Cu 2+ , which suggests that the chloride adlayer remains intact under more aggressive conditions and lowers the rate of corrosion from nitrate reduction in the presence of other oxidants.

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 score0.006
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.263
Teacher spread0.255 · 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
Published2025
Admission routes2
Has abstractyes

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