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Record W2746940293 · doi:10.3390/app7080854

Irradiation Induced Defect Clustering in Zircaloy-2

2017· article· en· W2746940293 on OpenAlexafffund
Zhongwen Yao, Mark R. Daymond, Sali Di, Yasir Idrees

Bibliographic record

VenueApplied Sciences · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsQueen's University
FundersArgonne National LaboratoryDivision of Materials ResearchOffice of ScienceNatural Sciences and Engineering Research Council of CanadaMitacsUniversity Network of Excellence in Nuclear EngineeringU.S. Department of Energy
KeywordsIrradiationMaterials scienceNucleationDislocationCrystallographic defectYield (engineering)Vacancy defectIonMicrostructureZirconium alloyCrystallographyAnalytical Chemistry (journal)MetallurgyComposite materialThermodynamicsZirconiumChemistryNuclear physics

Abstract

fetched live from OpenAlex

The effect of irradiation temperature and alloying elements on defect clustering behaviour directly from the cascade collapse in Zircaloy-2 is examined. The in-situ ioWn irradiation technique was employed to study the formation of <a>-type dislocation loops by Kr ion irradiation at 573 K and 773 K, while the dependence of dislocation loop formationon the presence of alloying elements was investigated by comparing with the defect microstructures of pure Zr irradiated under similar conditions. The experimentally observed temperature dependence of defect clustering was further investigated using molecular dynamics (MD) simulations near the experimental irradiation temperatures. We particularly concentrate on yield and morphology of small defect clusters formed directly from cascade collapse at very low ion doses. Smaller loop size and higher defect yield (DY) in Zircaloy-2 as compared to pure Zr suggests that the presence of the major alloying element Sn increases the number of nucleation sites for the defect clusters but suppresses the point defect recombination. MD simulations at 600 and 800 K revealed that the production of both vacancy and interstitial clusters drops significantly with an increase of irradiation temperature, which is reflected in experimentally collected DY data.

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 score0.233
Threshold uncertainty score0.705

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.063
GPT teacher head0.285
Teacher spread0.222 · 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

Citations17
Published2017
Admission routes2
Has abstractyes

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