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Record W2610165461 · doi:10.1080/1478422x.2017.1306972

Galvanic corrosion of copper-coated carbon steel for used nuclear fuel containers

2017· article· en· W2610165461 on OpenAlexafffundabout
Thalia E. Standish, Dmitrij Zagidulin, S. Ramamurthy, Peter Keech, James J. Noël, David W. Shoesmith

Bibliographic record

VenueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsNuclear Waste Management OrganizationWestern University
FundersArgonne National LaboratoryNuclear Waste Management Organization
KeywordsCorrosionMetallurgyCoatingMaterials scienceGalvanic cellGalvanic corrosionCarbon steelSpent nuclear fuelRadioactive wasteCopperGas dynamic cold sprayComposite materialNuclear engineeringWaste managementEngineering

Abstract

fetched live from OpenAlex

Canada is currently considering Cu-coated carbon steel containers for the long-term storage of used nuclear fuel in a deep geological repository. The Cu coating provides a corrosion-resistant barrier, protecting the underlying steel from coming into contact with groundwater. However, galvanically accelerated corrosion of steel is possible if there is a defect through the Cu coating. To investigate this scenario, the progression of steel corrosion at the base of a simulated though-coating defect was imaged using synchrotron X-ray micro-computed tomography. Results show that coatings produced using different methods (cold spray, annealed cold spray, electrodeposition) lead to different corrosion propagation geometries. These findings can be used for modelling steel corrosion at a though-coating defect under deep geological repository conditions.This paper is part of a supplement on the 6th International Workshop on Long-Term Prediction of Corrosion Damage in Nuclear Waste Systems.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.029
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.010
GPT teacher head0.249
Teacher spread0.239 · 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

Citations34
Published2017
Admission routes3
Has abstractyes

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