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Record W4220937449 · doi:10.1080/21663831.2022.2053220

Twin-coupled shear bands in an ultrafine-grained CoCrFeMnNi high-entropy alloy deformed at 77K

2022· article· en· W4220937449 on OpenAlexaff
Xiyao Li, Shijie Sun, Yu Zou, Qi Zhu, Yanzhong Tian, Jiangwei Wang

Bibliographic record

VenueMaterials Research Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsUniversity of Toronto
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsMisorientationMaterials scienceCrystal twinningAnnealing (glass)Shear (geology)Shear bandAlloyCondensed matter physicsGrain boundaryCritical resolved shear stressDislocationHigh entropy alloysComposite materialMicrostructureShear rate

Abstract

fetched live from OpenAlex

Two different types of shear bands generate in ultrafine-grained (UFG) CoCrFeMnNi high-entropy alloys deformed at 77 K, i.e. commonly-observed low angle grain boundary (LAGB) type and unusual twin-coupled type. Twin-coupled shear bands exhibit a misorientation of 56–67° with the matrix, in contrast to the LAGB shear band with a misorientation of 6–12°. Both annealing twins and deformation twins can act as precursor for twin-coupled shear bands, where dislocation accumulation along twin boundaries can tune the orientation of twin lamella. These observations suggest that twin-coupled shear bands can act as an important plastic carrier in materials deformed under extreme conditions.

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 score0.003
Threshold uncertainty score0.006

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 head0.026
GPT teacher head0.280
Teacher spread0.255 · 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

Citations32
Published2022
Admission routes1
Has abstractyes

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