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Record W3186929577 · doi:10.1557/s43578-022-00613-2

Deformation behaviour of ion-irradiated FeCr: A nanoindentation study

2022· article· en· W3186929577 on OpenAlexaff
Kay Song, Hongbing Yu, Phani Karamched, Kenichiro Mizohata, David E.J. Armstrong, Felix Hofmann

Bibliographic record

VenueJournal of materials research/Pratt's guide to venture capital sources · 2022
Typearticle
Languageen
FieldMaterials Science
TopicFusion materials and technologies
Canadian institutionsCanadian Nuclear Laboratories
FundersH2020 European Research CouncilEngineering and Physical Sciences Research CouncilUniversity of Oxford
KeywordsNanoindentationMaterials scienceNucleationIrradiationDislocationPlasticityDeformation (meteorology)Composite materialHardening (computing)IonWork hardeningCrystallographyMicrostructureChemistryNuclear physicsLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract Understanding the mechanisms of plasticity in structural steels is essential for the operation of next-generation fusion reactors. This work on the deformation behaviour of FeCr, focusses on distinguishing the nucleation of dislocations to initiate plasticity, from their propagation through the material. Fe3Cr, Fe5Cr, and Fe10Cr were irradiated with 20 MeV Fe3+ ions at room temperature to doses of 0.008 dpa and 0.08 dpa. Nanoindentation was then carried out with Berkovich and spherical indenter tips. Our results show that the nucleation of dislocations is mainly from pre-existing sources, which are not significantly affected by the presence of irradiation defects or Cr%. Yield strength, an indicator of dislocation mobility, increases with irradiation damage and Cr content, while work hardening capacity decreases mainly due to irradiation defects. The synergistic effects of Cr and irradiation damage in FeCr appear to be more important for the propagation of dislocations than for their nucleation. Graphical abstract

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 score0.006
Threshold uncertainty score0.019

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.030
GPT teacher head0.339
Teacher spread0.308 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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Same venueJournal of materials research/Pratt's guide to venture capital sourcesSame topicFusion materials and technologiesFrench-language works237,207