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Record W2738577321 · doi:10.3390/met7080287

Precipitate Stability in a Zr–2.5Nb–0.5Cu Alloy under Heavy Ion Irradiation

2017· article· en· W2738577321 on OpenAlexafffund
Qingshan Dong, Zhongwen Yao, Qiang Wang, Hongbing Yu, Mark A. Kirk, Mark R. Daymond

Bibliographic record

VenueMetals · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsQueen's University
FundersOffice of ScienceUniversity Network of Excellence in Nuclear EngineeringArgonne National LaboratoryNatural Sciences and Engineering Research Council of CanadaQueen's University
KeywordsMaterials scienceIrradiationRedistribution (election)AlloyMicrostructureMetallurgyAmorphous solidIonZirconium alloyCrystallographyChemistryNuclear physics

Abstract

fetched live from OpenAlex

The stability of precipitates in Zr–2.5Nb–0.5Cu alloy under heavy ion irradiation from 100 °C to 500 °C was investigated by quantitative Chemi-STEM EDS analysis. Irradiation results in the crystalline to amorphous transformation of Zr2Cu between 200 °C and 300 °C, but the β–Nb remains crystalline at all temperatures. The precipitates are found to be more stable in starting structures with multiple boundaries than in coarse grain structures. There is an apparent increase of the precipitate size and a redistribution of the alloying element in certain starting microstructures, while a similar size change or alloying element redistribution is not detected or only detected at a much higher temperature in other starting microstructures after irradiation.

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 score0.005
Threshold uncertainty score0.010

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 head0.064
GPT teacher head0.281
Teacher spread0.217 · 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

Citations22
Published2017
Admission routes2
Has abstractyes

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