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Record W2908958579 · doi:10.1038/s41467-018-08031-x

In situ atomistic observation of disconnection-mediated grain boundary migration

2019· article· en· W2908958579 on OpenAlexaff
Qi Zhu, Guang Cao, Jiangwei Wang, Chuang Deng, Jixue Li, Ze Zhang, Scott X. Mao

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsUniversity of Manitoba
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsNucleationGrain boundaryCrystalliteNanocrystalline materialMaterials scienceShear (geology)DisconnectionIn situNanotechnologyComposite materialChemistryMicrostructureMetallurgy

Abstract

fetched live from OpenAlex

Shear-coupled grain boundary (GB) migration is of general significance in the deformation of nanocrystalline and polycrystalline materials, but comprehensive understanding of the migration mechanism at the atomic scale remains largely lacking. Here, we systematically investigate the atomistic migration of Σ11(113) coherent GBs in gold bicrystals using a state-of-art in situ shear testing technique combined with molecular dynamic simulations. We show that shear-coupled GB migration can be realised by the lateral motion of layer-by-layer nucleated GB disconnections, where both single-layer and double-layer disconnections have important contributions to the GB migration through their frequent composition and decomposition. We further demonstrate that the disconnection-mediated GB migration is fully reversible in shear loading cycles. Such disconnection-mediated GB migration should represent a general deformation phenomenon in GBs with different structures in polycrystalline and nanocrystalline materials, where the triple junctions can act as effective nucleation sites of GB disconnections.

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 score0.005

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 integrity0.0000.001
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 head0.270
Teacher spread0.252 · 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

Citations203
Published2019
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicMicrostructure and mechanical propertiesFrench-language works237,207