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Microstructural characterization of interfaces between cold sprayed copper and electrodeposited copper corrosion barrier coatings for used nuclear fuel containers

2024· article· en· W4402738763 on OpenAlexafffundabout
Liyang Zheng, Dominique Poirier, Jean-Gabriel Legoux, Jason D. Giallonardo, Jane Y. Howe, U. Erb

Bibliographic record

VenueSurface and Coatings Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNuclear Waste Management OrganizationNational Research Council CanadaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaOntario Research Foundation
KeywordsCopperCorrosionMetallurgyMaterials scienceCharacterization (materials science)Gas dynamic cold sprayComposite materialNanotechnologyCoating

Abstract

fetched live from OpenAlex

Copper coatings on steel, manufactured by a novel combination of two different manufacturing technologies , electrodeposition and cold spraying, are employed as a corrosion barrier in the current design of used fuel containers (UFCs) for the long-term storage of Canada's used nuclear fuel. This study examines such copper coatings on a prototype container section, with a particular focus on the interface region where copper materials fabricated by the two processes converge. Distinct microstructures for the electrodeposited and cold sprayed copper layers were observed. An unexpected recrystallized layer on top of electrodeposited copper was identified for the first time. Local annealing at 350 °C and 600 °C for one hour resulted in more homogeneous microstructures and Vickers hardness profiles among the copper layers, which is favorable for this application by reducing processing related heterogeneity in the interface region. More homogenized structures with high fractions of special grain boundaries in this region are expected to provide enhanced mechanical and corrosion properties .

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 head0.006
GPT teacher head0.221
Teacher spread0.216 · 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.

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

Citations9
Published2024
Admission routes3
Has abstractyes

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