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Record W2977064600 · doi:10.1063/1.5119150

Atomistic investigation of the deformation mechanisms in nanocrystalline Cu with amorphous intergranular films

2019· article· en· W2977064600 on OpenAlexafffund
Afzal Hossain Neelav, Snehanshu Pal, Chuang Deng

Bibliographic record

VenueJournal of Applied Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceNanocrystalline materialNucleationDislocationGrain sizeGrain boundaryIntergranular corrosionAmorphous solidDeformation (meteorology)Grain boundary strengtheningDeformation mechanismCreepComposite materialMetallurgyCrystallographyMicrostructureNanotechnologyThermodynamics

Abstract

fetched live from OpenAlex

Grain boundaries in nanocrystalline (NC) materials are important as they control the microstructural evolution and act as both sinks and sources for dislocation activities. In order to enhance the absorption of dislocations and restrict the crack nucleation and growth, the conventional grain boundaries can be substituted with amorphous intergranular films (AIFs). In the present atomistic study, we investigated the deformation mechanism of bicrystals and NC Cu specimens with AIF under dynamic and static loading conditions with a particular focus on the influence of grain sizes (3 nm–17 nm) and AIF thicknesses (0.5 nm–1.5 nm). We found that the presence of AIF homogenized the interfacial energy irrespective of the grain orientations and decreased its overall value, which posed a strong effect on the strength of the metallic system. In addition, we observed a shift of the deformation mechanism from that dominated by dislocations to interfacial activities due to the presence of AIF as the grain size or AIF thickness changed. Finally, results from high-temperature creep deformation showed that the NC Cu with AIF had excellent thermal stability despite its small grain size.

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)0.000
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 score0.124
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.005
GPT teacher head0.165
Teacher spread0.160 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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