MétaCan
Menu
Back to cohort
Record W4403228175 · doi:10.1038/s41598-024-74019-x

On the nature of nano twin-induced shear band formation in a dispersion strengthened copper alloy under micro-mechanical loading

2024· article· en· W4403228175 on OpenAlexafffund
Shadab Sarmast-Ghahfarokhi, Ali Ghatei-Kalashami, A.P. Gerlich, Michael J. Benoit, Y. Zhou

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsUniversity of Waterloo
FundersInternational Zinc AssociationNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAlloyCopperMaterials scienceNano-Dispersion (optics)Shear (geology)Shear bandMetallurgyComposite materialPhysicsOptics

Abstract

fetched live from OpenAlex

A key challenge in metallic alloys with high ductility is understanding how microstructural inhomogeneities influence shear localization, leading to localized plastic deformation and material failure. In this study, we explore the phenomenon of shear localization in coarse-grained copper, a material traditionally regarded as having low susceptibility to such behavior. Contrary to conventional understanding, our findings reveal that microstructural inhomogeneities play a pivotal role in inducing extensive strain localization during low strain rate micro-mechanical loading. The presence of fine precipitate particles leads to strain localization at small strains and low strain rates by activating shear localization driven by void formation mechanisms. Furthermore, nanoscale precipitates act as sites for dislocation pile-up within deformed structures of shear bands, leading to the formation of nano twins within these bands. Consequently, precipitate particles serve a dual role: contributing to strain localization and potential cracking, while also enhancing the alloy's strength and ductility through nano-twinning mechanisms. This investigation offers a novel perspective on the interplay between material microstructure and deformation behavior, challenging existing paradigms in materials science.

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)0.002
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 score0.010
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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

Citations10
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueScientific ReportsSame topicMicrostructure and mechanical propertiesFrench-language works237,207