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Record W4409464844 · doi:10.1016/j.corsci.2025.112948

Correlating nanoporosity, Nb oxidation states and deuterium transport in the oxide layer formed on a Zr-2.5 Nb alloy

2025· article· en· W4409464844 on OpenAlexafffundabout
Adil Shaik, S.Y. Persaud, Mark R. Daymond

Bibliographic record

VenueCorrosion Science · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaEngineering and Physical Sciences Research CouncilUniversity Network of Excellence in Nuclear EngineeringHenry Royce Institute
KeywordsAlloyOxideCorrosionMaterials scienceLayer (electronics)DeuteriumChemical engineeringMetallurgyNanotechnologyNuclear physics

Abstract

fetched live from OpenAlex

In Canada Deuterium Uranium reactors, Zr-2.5 Nb alloys are used as a pressure boundary material and understanding corrosion and deuterium transport is critical. The objective of this study is to characterize the oxide layer formed on Zr-2.5 Nb alloy at the nanoscale and understand the diffusion of deuterium from the water-oxide interface to the base metal. Results indicate that defects, nanoporosity and microcracks in the oxide govern the oxidation tendency of Nb, which has +5, +4, +2, or partially oxidized states. Deuterium transport through the oxide layer is also directly correlated with defect density. The concentration of deuterium is highest at the water-oxide interface and lowest at the metal-oxide interface. This correlates with a change in defect density and Nb oxidation state, which also decreases between these two interfaces. The present findings provide a mechanistic understanding of oxidation and deuterium ingress in Zr-2.5 Nb alloys, helping to ensure the long-term performance of the material for reactor applications. • Nanopore density increases with exposure duration from 600 to 1799 days. • Deuterium concentration varies in the oxide layer and is influenced by nanoporosity. • β-phase impedes deuterium diffusion, impacting hydrogen pickup in Zr-2.5 Nb alloy.

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)0.000
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 score0.081
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.269
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 routes3
Has abstractyes

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