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Record W2098704188 · doi:10.1520/jai101292

Chemistry of Waterside Oxide Layers on Pressure Tubes

2008· article· en· W2098704188 on OpenAlexaff
T. Do, M. I. El Saidy, W. H. Hocking

Bibliographic record

VenueJournal of ASTM International · 2008
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsChemistryOxideChemical engineeringOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract The pressure tubes that contain the fuel bundles and the primary coolant within the core of a CANDU heavy-water reactor are fabricated from a Zr-2.5Nb alloy with a complex microstructure. During reactor operation the pressure-tube interior surface is slowly oxidized by heavy water and a fraction of the deuterium that is released through this process enters the underlying alloy and can reduce its fracture toughness. Considerable variability in deuterium ingress has been observed among the pressure tubes within a single reactor as well as between different reactors. These differences are thought to be due not only to metallurgical variables, such as alloy microstructure and composition, but also to variations in the primary coolant chemistry, including pH and dissolved impurities. In the present study, a combination of surface analytical methods has been employed to characterize the microchemistry and oxidation history of the waterside oxide layers grown on two pressure tubes that were removed from different CANDU reactors. The presence of varying concentrations of iron, manganese and uranium, derived mainly from corrosion of feeder pipes and fuel failures, has been found throughout the oxide layers. An increase in the oxidation rate of one pressure tube later in life could be correlated with evidence of greater open porosity, provided by the depth distributions of impurities, extending deep within the oxide layer.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.264
Teacher spread0.254 · 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.

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

Citations2
Published2008
Admission routes1
Has abstractyes

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