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Record W2080191826 · doi:10.1115/1.3212820

Modeling the Instability of Carbon Nanotubes: From Continuum Mechanics to Molecular Dynamics

2009· article· en· W2080191826 on OpenAlexafffund
Wenhui Duan, Qing Wang, Quan Wang, K.M. Liew

Bibliographic record

VenueJournal of Nanotechnology in Engineering and Medicine · 2009
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Manitoba
FundersCanada Research Chairs
KeywordsCarbon nanotubeBucklingNanomechanicsContinuum mechanicsMaterials scienceInstabilityMolecular dynamicsMechanical properties of carbon nanotubesCarbon fibersNanotechnologyMechanicsComposite materialNanotubePhysicsComputational chemistryComposite numberChemistry

Abstract

fetched live from OpenAlex

A hybrid continuum mechanics and molecular mechanics model is developed in this paper to predict the critical strain, stress, and buckling load of the inelastic buckling of carbon nanotubes. With the proposed model, the beamlike and shell-like buckling behavior of carbon nanotubes can be analyzed in a unified approach. The buckling solutions from the hybrid model are verified from molecular dynamics simulations via the MATERIALS STUDIO software package and from available research findings. The existence of the optimum diameter, at which the buckling load reaches its maximum, and the correlation of the diameter with the length of carbon nanotubes, as predicted by Liew et al. (2004, “Nanomechanics of Single and Multiwalled Carbon Nanotubes,” Phys. Rev. B, 69(11), pp. 115429), are uncovered by the hybrid model. The simplicity and effectiveness of the proposed model are not only able to reveal the chiral and size-dependent buckling solutions for carbon nanotubes, but also enable a thorough understanding of the stability behavior of carbon nanotubes in potential applications.

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)0.001
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 score0.445
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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

Citations15
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Nanotechnology in Engineering and MedicineSame topicCarbon Nanotubes in CompositesFrench-language works237,207