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Record W2089827684 · doi:10.1149/2.032408jes

Modeling the Radiolytic Corrosion of Fractured Nuclear Fuel under Permanent Disposal Conditions

2014· article· en· W2089827684 on OpenAlexaff
Linda Wu, Nazhen Liu, Zack Qin, David W. Shoesmith

Bibliographic record

VenueJournal of The Electrochemical Society · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsWestern University
Fundersnot available
KeywordsCorrosionRadiolysisSpent nuclear fuelPelletsMaterials scienceDecompositionRedoxRadioactive wasteNuclear fuelMetallurgyChemistryComposite materialIrradiationNuclear chemistryPhysicsNuclear physics

Abstract

fetched live from OpenAlex

A two-dimensional model has been developed to simulate the corrosion of nuclear fuel pellets under permanent waste disposal conditions in a steel vessel with a corrosion-resistant copper shell. The primary emphasis was on the corrosion behavior within cracks with various dimensions. It was shown that a simplified α-radiolysis model which only accounts for the radiolytic production of H2O2 and H2 provides a reasonably accurate simulation and is a time-efficient alternative to the use of a model including a full α-radiolysis reaction set. Both radiolytic H2O2 and H2 can accumulate inside the cracks. However, the [H2O2] is regulated by its reaction with UO2 to cause corrosion and especially its decomposition to O2 and H2O. This leads to [H2] much greater than [H2O2] within the cracks. The critical [H2], [H2]crit, required to completely suppress corrosion has been calculated for various crack widths and depths. The maximum [H2]crit is only ∼ 12 times that required on a planar surface irrespective of the dimensions of the crack. The build up of H2 within cracks is effectively a shift to more reducing conditions. As a consequence, the redox conditions within cracks begin to decouple from the external redox conditions. This makes the fuel corrosion process at these locations less sensitive than might be expected to the influences of the H2 and Fe2+ produced by corrosion of the steel vessel.

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 score0.034
Threshold uncertainty score0.208

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 science0.0000.000
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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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