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Record W4304136476 · doi:10.3390/met12101692

Swelling and He-Embrittlement of Austenitic Stainless Steels and Ni-Alloys in Nuclear Reactors

2022· article· en· W4304136476 on OpenAlexaff
M. Griffiths, Steven X. Xu, Juan E. Ramos Nervi

Bibliographic record

VenueMetals · 2022
Typearticle
Languageen
FieldMaterials Science
TopicFusion materials and technologies
Canadian institutionsKinectrics (Canada)Carleton UniversityQueen's University
Fundersnot available
KeywordsEmbrittlementMaterials scienceAusteniteMetallurgySwellingVoid (composites)Grain boundaryMicrostructureBrittlenessIrradiationComposite materialNuclear physics

Abstract

fetched live from OpenAlex

Rate theory models have been developed for the swelling and He-embrittlement of austenitic stainless steels and Ni-alloys in nuclear reactors. The models illustrate how microstructure evolution during irradiation affects the rate of change of mechanical properties and the dimensional stability. He-stabilised cavity accumulation on grain boundaries, which causes brittle failure at low stresses and strains known as He-embrittlement, is shown to be strongly dependent on the irradiation temperature and the rate of production of Frenkel pairs and He atoms. The results show that the accumulation of cavities on grain boundaries falls into two regimes: (i) that dictated by matrix bubble swelling at low temperatures; and (ii) that dictated by matrix void swelling at high temperatures.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.018
GPT teacher head0.230
Teacher spread0.213 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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