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Record W4411473876 · doi:10.1115/1.4068990

The Potential of Using Chromium Plating to Reduce Ingress of Hydrogen Isotopes in CANDU Reactor Rolled Joints

2025· article· en· W4411473876 on OpenAlexaff
G.A. McRae, C.E. Coleman, Mui Hoon Nai, Leigh Corrigall

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsCarleton University
Fundersnot available
KeywordsCorrosionHydrogenZirconiumMetallurgyZirconium alloyMaterials scienceGalvanic cellElectrolyteChromiumPlating (geology)Chrome platingGalvanic corrosionChemistryElectroplatingComposite materialElectrodeGeology

Abstract

fetched live from OpenAlex

Abstract Some recent measurements of hydrogen isotope concentrations in zirconium pressure tubes at the rolled joints in CANDU reactors have been surprisingly high reaching equivalent hydrogen values above the current license limit of 120 μg H/g Zr. These high concentrations have been attributed, in part, to corrosion and concomitant ingress of hydrogen isotopes into the pressure tube. The rolled joint consists of a stainless-steel end fitting into which a zirconium tube is rolled to form a leak-free joint. Chromium plating of the bore of the end fittings is being considered to lessen hydrogen isotope concentrations in the zirconium by reducing galvanic corrosion and by reducing direct contact between the end fitting and the pressure tube. The experiments described in this study were all done dry, without electrolyte present. The benefit of Cr plating under dry conditions was observed to be the same as when an electrolyte is present; hence, ingress of hydrogen isotopes from the end fittings to the pressure tubes is not because of corrosion because corrosion requires an electrolyte. Instead, ingress of hydrogen isotopes happens when hydrogen gas can move between metals when their surface oxides fail under anoxic conditions. The high concentrations are caused by permeation, not corrosion.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.262
Teacher spread0.249 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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