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Record W2054296307 · doi:10.1063/1.4883190

A kinematic study of energy barriers for crack formation in graphene tilt boundaries

2014· article· en· W2054296307 on OpenAlexaff

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGrapheneTilt (camera)Deformation (meteorology)CrystalliteKinematicsArrhenius equationRange (aeronautics)Ultimate tensile strength

Abstract

fetched live from OpenAlex

Recent experimental studies have observed a surprisingly wide range of strengths in polycrystalline graphene. Previous computational investigations of graphene tilt boundaries have highlighted the role of interfacial topology in determining mechanical properties. However, a rigorous characterization of deformation energy barriers is lacking, which precludes direct comparison to the available experimental data. In the current study, molecular dynamics tensile simulations are performed to quantify kinematic effects on failure initiation in a wide range of graphene tilt boundaries. Specifically, the process of crack formation is investigated to provide a conservative estimate of strength at experimental loading rates. Contrary to previous studies, significant strain rate sensitivity is observed, resulting in reductions of crack formation stresses on the order of 7% to 33%. Energy barriers for crack formation are calculated in the range of 0.58 to 2.07 eV based on an Arrhenius relation that is fit to the collected simulation data. Physically, the magnitude of energy barriers in graphene tilt boundaries is found to be linearly correlated to the pre-stress in the critical bonds. Predictions reported in the present study provide a possible explanation for the wide range of strengths experimentally observed in polycrystalline graphene and greatly improve upon current theoretical estimates.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.274
Teacher spread0.258 · 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

Citations20
Published2014
Admission routes1
Has abstractyes

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