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Record W3204065504 · doi:10.1115/1.4052520

Weight Gain and Hydrogen Absorption in Supercritical Water At 500 °C of Chromium-Coated Zirconium-Based Alloys: Transverse Versus Longitudinal Direction

2021· article· en· W3204065504 on OpenAlexaffabout
Kittima Khumsa-Ang, Stephane Rousseau, Oksana Shiman

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2021
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsCorrosionMaterials scienceZirconium alloyCladding (metalworking)Supercritical fluidZirconiumCoatingHydrogenChromiumUraniumMetallurgyComposite materialChemistry

Abstract

fetched live from OpenAlex

Abstract Canadian Nuclear Laboratories has an on-going Research & Development program to support the development of a scaled–down 300 MWe version of the Canadian Super-Critical Water Reactor concept. The 300 MWe and 170–channel reactor core concept uses low enriched uranium fuel and features a maximum cladding temperature of 500 °C. Our goal is to test surface-modified zirconium alloys for use as fuel cladding. Zirconium alloys are attractive as they offer low neutron cross section thereby allowing the use of low enriched fuel. In this paper, we report on the results of general corrosion experiments used to evaluate chromium-coated zirconium-based alloys in the two chemistries (630 μg/kg O2 in both de-aerated and lithiated supercritical water). These experiments were conducted in a refreshed autoclave at 500 °C and 23.5 MPa. After exposure, the weight gain and the hydrogen absorption were examined. At adequate coating thickness, longitudinal and transverse coupons show similar corrosion behavior with improved corrosion resistance compared to uncoated coupons. The measured concentrations of hydrogen absorption are higher for the transverse coupons. Alkaline treatment resulted in higher weight gains than was found in pure oxygenated supercritical water.

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 score0.030
Threshold uncertainty score0.244

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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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