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Record W2069004923 · doi:10.1063/1.2001147

Nanomechanical resonance studies of carbon nanotube peapod bundles

2005· article· en· W2069004923 on OpenAlexaff
Papot Jaroenapibal, Satishkumar B. Chikkannanavar, David E. Luzzi, Stéphane Evoy

Bibliographic record

VenueJournal of Applied Physics · 2005
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceTransmission electron microscopyDiffractionResonance (particle physics)Aspect ratio (aeronautics)Electron diffractionComposite materialModulusFullereneMechanical resonanceBendingNanotechnologyOpticsChemistryVibrationAtomic physicsPhysics

Abstract

fetched live from OpenAlex

Filled carbon nanotubes represent a class of tunable nanoscale materials that could provide both high-quality resonance and sensing specificity for nanoresonator-based devices. We have studied the mechanical properties of C60-filled single-walled carbon nanotube bundles through observation of their mechanical resonances in a transmission electron microscope. X-ray diffraction was used to qualitatively study the filling of C60 in the bulk material. Electron diffraction was used to confirm the filling of each bundle prior to the measurement of individual mechanical resonance frequencies. The electron-diffraction pattern revealed a C60 spacing periodicity of 9.97 Å within the lumen of the nanotubes, which is close to the theoretical equilibrium spacing of R0=10.05Å in bulk C60. An average ratio of (Eb∕ρ)1∕2=13230±3187m∕s was observed for the unfilled bundles, compared with a ratio of (Eb∕ρ)1∕2=19002±2307m∕s for C60-filled bundles. Such values indicate an increase of the average bending modulus by as much as 170% in C60-filled bundles. A dependence of this ratio on the diameter of the structures was also observed. This dependence is explained by the increased importance of intertube slipping in bundles of larger diameter.

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 score0.010
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.024
GPT teacher head0.279
Teacher spread0.255 · 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

Citations21
Published2005
Admission routes1
Has abstractyes

Explore more

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