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Record W2275080660 · doi:10.1021/acs.jpclett.5b01815

Dislocation Shielding of a Nanocrack in Graphene: Atomistic Simulations and Continuum Modeling

2015· article· en· W2275080660 on OpenAlexafffund
Fanchao Meng, Cheng Chen, Jun Song

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFaculty of Engineering, McGill University
KeywordsGrapheneElectromagnetic shieldingMolecular dynamicsDislocationMaterials scienceStatistical physicsCondensed matter physicsPhysicsNanotechnologyComposite materialQuantum mechanics

Abstract

fetched live from OpenAlex

Combining atomistic simulations and continuum modeling, we studied dislocation shielding of a nanocrack in monolayer graphene under mode-I loading. Different crack-dislocation configurations were constructed and the shielding effects on the threshold stress intensity for crack propagation were examined. Excellent agreement between simulation results and linear-elastic fracture mechanics (LEFM) predictions was achieved. As the separation between the crack-tip and dislocation, that is, rR, varies (with respect to the crack size a), the shielding effect exhibits two different dependences on rR, scaling as 1/rR 1/2 for rR/a ≪ 1 (near-tip), whereas 1/rR for rR/a ≫ 1 (far-field), respectively. Particularly, the far-field 1/rR scaling was shown to be a direct manifestation of the stress field of dislocation in graphene. Our work presents a systematic study of nanoscale crackdislocation interactions in graphene, providing valuable information on defect engineering of graphene.

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 score0.057
Threshold uncertainty score0.157

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 head0.026
GPT teacher head0.284
Teacher spread0.259 · 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

Citations47
Published2015
Admission routes2
Has abstractyes

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