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Record W2064293674 · doi:10.4236/jmmce.2014.22017

Effect of Surface Modification on Candidate Alloys for Canadian SCWR Fuel Cladding

2014· article· en· W2064293674 on OpenAlexfundaboutno aff
Jian Li, Pei Liu, Renata Zavadil, T. Malis, Sami Penttilä

Bibliographic record

VenueJournal of Minerals and Materials Characterization and Engineering · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsnot available
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCladding (metalworking)CorrosionSurface modificationSupercritical fluidMetallurgyAusteniteStress corrosion crackingCrackingAustenitic stainless steelComposite materialMicrostructureChemical engineering

Abstract

fetched live from OpenAlex

The Canadian Generation-IV supercritical water reactor (SCWR) requires peak cladding surface temperature of 800℃ for a core outlet temperature of 625℃. Materials selection for high temperature fuel cladding is becoming one of the major challenging tasks. Austenitic stainless steels with excellent corrosion resistance are often susceptible to stress corrosion cracking upon SCW exposure. Low-Cr steels such as P91 exhibit good high-temperature mechanical properties, but the lack of sufficient Cr content makes this group of alloys corrode too fast. One possible solution is to use coatings or surface modification techniques to improve the surface resistance to corrosion. In this study, we investigated the effect of surface modification on commercial 316L stainless steel. Surface modification by mechanical deformation has marked improvement in corrosion resistance during SCW exposure. Possible mechanisms for such improvement are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.991
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2014
Admission routes2
Has abstractyes

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