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Record W2073305201 · doi:10.1142/s1758825112500391

NONLINEAR FREE VIBRATION ANALYSIS OF AN EMBEDDED DOUBLE LAYER GRAPHENE SHEET IN POLYMER MEDIUM

2012· article· en· W2073305201 on OpenAlexafffund
Mohammad Mahdavi, Liying Jiang, Xueliang Sun

Bibliographic record

VenueInternational Journal of Applied Mechanics · 2012
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceNonlinear systemvan der Waals forceGrapheneVibrationHarmonic balanceNormal modeDeflection (physics)Composite materialMechanicsPhysicsClassical mechanicsNanotechnologyAcoustics

Abstract

fetched live from OpenAlex

This paper investigates the nonlinear free vibration of a double layer graphene sheet (DLGS) embedded in a polymer matrix aroused by the nonlinear van der Waals (vdW) interactions based on the classic Kirchhoff plate theory. Harmonic balance method is used to predict the nonlinear relation between deflection amplitudes and resonant frequencies of the DLGS. The embedded DLGS presents a hardening nonlinearity in both in-phase vibration (IPV) and anti-phase vibration (APV) modes. The surrounding polymer medium is found to have significant effect on the resonant frequency, especially for the IPV mode. For example, the variation of the resonant frequencies of an embedded DLGS is less dependent on the graphene aspect ratio and mode numbers as compared with a free-standing one. Uni-axial and bi-axial in-plane load effects upon the vibrational behavior of DLGS are also investigated. It is concluded that due to the influence of the nonlinear interlayer and interfacial vdW forces on the DLGS, prediction on both linear and nonlinear resonant frequencies for the embedded DLGS is quite different from that for a free-standing one. This study is expected to be useful for understanding the nonlinear mechanical behavior of graphenes with their potential applications in nanocomposites.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.282
Teacher spread0.267 · 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

Citations11
Published2012
Admission routes2
Has abstractyes

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