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Record W3195730369 · doi:10.1149/ma2021-0210579mtgabs

(Corrosion Division Morris Cohen Graduate Student Award Address) Galvanic Corrosion of Copper-Coated Carbon Steel for Used Nuclear Fuel Containers

2021· article· en· W3195730369 on OpenAlexaffabout
Thalia E. Standish, Lindsay Braithwaite, Dmitrij Zagidulin, S. Ramamurthy, Peter Keech, Rebecca Sarazen, David W. Shoesmith, James J. Noël

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsNuclear Waste Management OrganizationWestern University
Fundersnot available
KeywordsCorrosionMaterials scienceMetallurgyGalvanic corrosionGalvanic cellCoatingCarbon steelScanning electron microscopeSpent nuclear fuelRaman spectroscopyComposite materialNuclear chemistryChemistry

Abstract

fetched live from OpenAlex

Carbon steel vessels coated with ∼3 mm of Cu have been proposed for the permanent disposal of used nuclear fuel in a deep geological repository (DGR) in Canada. In the event that a container is emplaced in the DGR with an undetected defect in the Cu coating that exposes the steel substrate, galvanically accelerated corrosion of steel is, in principle, possible. To investigate this scenario, the progression of steel corrosion at the base of novel simulated through-coating defects was monitored electrochemically and imaged non-destructively using X-ray micro-computed tomography (micro-CT) as a function of time, O 2 availability (including anoxic conditions), and coating method (cold spray deposition (with and without heat treatment) and electrodeposition). The corrosion products and surface damage were analyzed using Raman spectroscopy and scanning electron microscopy (SEM)/energy dispersive X-ray spectroscopy (EDX). These analyses showed how the corrosion damage to steel evolved over time and how it was affected by the method used to coat steel with Cu and the amount of O 2 available. The results showed that steel exposed at the base of a through-Cu coating defect corroded and became covered by corrosion products, while there was no visible loss of Cu. The supply of O 2 to the sample surface governed the corrosion rate, while the distribution of damage to steel at the base of the defect depended on the Cu coating method and the resulting quality of the Cu/steel interface. Cold spray Cu-coated steel specimens exhibited a radial spread of corrosion along the Cu/steel interface, while electrodeposited Cu/steel specimens experienced preferential interfacial corrosion in the direction in which the steel substrate was machined prior to the electrodeposition of Cu. An example of the progression of corrosion observed by micro-CT is shown in Figure 1. Less extensive corrosion along the Cu/steel interface was observed on samples that were shown by electron backscatter diffraction (EBSD) and adhesion tests to have a less stressed, more uniform, and more well-adhered interface. However, less extensive corrosion was typically at the expense of deeper penetration into the steel. In the absence of O 2 , the quality of the Cu/steel interface greatly affected both the overall amount of corrosion damage to steel and the distribution of damage. The corrosion rates were significantly lower under anoxic conditions than under oxic conditions, and tended to decrease over time. The influence of a wide range of cathode:anode area ratios and Cl − concentrations, and the availability of O 2 , was evaluated by monitoring the galvanic current passing between separate Cu and steel electrodes, connected through a zero-resistance ammeter (ZRA), and the galvanic potential of the couple. The galvanic corrosion of steel was most severe when it was exposed to air-sparged solution with a moderate [Cl − ] as part of the couple with the largest Cu:steel area ratio. Figure 1

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.293
Threshold uncertainty score0.979

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2930.131

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 head0.047
GPT teacher head0.299
Teacher spread0.252 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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