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Record W4407597724 · doi:10.1038/s41529-025-00562-1

Particle-particle interface corrosion of cold sprayed copper in dilute nitric acid solutions: geometry-controlled corrosion mechanism

2025· article· en· W4407597724 on OpenAlexafffund
Xuejie Li, Fraser P. Filice, Jeffrey D. Henderson, Mehran Behazin, S. Ramamurthy, I. Barker, Reza Moshrefi, Sebastian Amland Skaanvik, Samantha Michelle Gateman, David W. Shoesmith, James J. Noël

Bibliographic record

Venuenpj Materials Degradation · 2025
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNuclear Waste Management OrganizationWestern University
FundersOntario Advanced Manufacturing ConsortiumNuclear Waste Management Organization
KeywordsCorrosionCopperParticle (ecology)Nitric acidMaterials scienceMechanism (biology)MetallurgyPhysicsGeology

Abstract

fetched live from OpenAlex

We report here the mechanism of accelerated corrosion observed at the particle-particle interfaces (PPIs) of cold sprayed (CS) Cu exposed to dilute nitric acid. PPI corrosion is triggered by the oxide inclusions present along the PPIs. The accelerated corrosion at PPIs results from the combined effects of confined geometry and catalytic reactions, which involve the electrochemical dissolution of Cu and reduction of NO 3 − . Gas bubbles composed of N 2 O and NO form continually during PPI corrosion, indicating the ongoing reduction of NO 3 − . Annealing the CS Cu at 600 °C causes the oxide inclusions to coalesce, thereby breaking the interconnected oxide inclusion network. Consequently, the propagation of PPI corrosion is impeded. This work demonstrates how geometrical factors can determine the corrosion process and emphasizes the role of microstructural defects in the corrosion properties of additively manufactured materials.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.242
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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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