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Record W2941210606 · doi:10.31399/asm.cp.itsc2019p0413

Evaluation of the Ductility of Cold-Sprayed Copper Coatings for the Long-Term Disposal of Nuclear Fuel

2019· article· en· W2941210606 on OpenAlexaff
Bruno Guerreiro, Phyong Vo, Dominique Poirier, Jean-Gabriel Legoux, Xuan Zhang, Jason D. Giallonardo

Bibliographic record

VenueThermal spray · 2019
Typearticle
Languageen
FieldEngineering
TopicPowder Metallurgy Techniques and Materials
Canadian institutionsNuclear Waste Management OrganizationNational Research Council Canada
Fundersnot available
KeywordsCopperMaterials scienceGas dynamic cold sprayMetallurgyDuctility (Earth science)Annealing (glass)Raw materialCoatingComposite materialCreep

Abstract

fetched live from OpenAlex

Abstract An internationally recognized best practice for disposing used nuclear fuels is to store them in specially designed containers in deep geological repositories. One type of spent fuel container is a carbon steel canister with a cold-sprayed copper coating. The aim of this study is to assess the impact of various factors on the ductility of this protective copper layer. The current investigation finds that there can be significant variability in ductility when feedstock powder size and chemical composition are changed while keeping spraying and heat treatment conditions constant. Test results show that the ductility of nitrogen-sprayed copper decreases with increasing hardness, but can be improved by raising annealing temperature from 300 to 600 °C. The effects of substrate geometry and process variations are discussed as well.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.020
GPT teacher head0.257
Teacher spread0.237 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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