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Record W2138731819 · doi:10.1039/c4nr05656f

Giant piezoresistivity in aligned carbon nanotube nanocomposite: account for nanotube structural distortion at crossed tunnel junctions

2014· article· en· W2138731819 on OpenAlexafffund
Zheng Zhu

Bibliographic record

VenueNanoscale · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsCarbon nanotubeMaterials scienceNanotubePercolation thresholdNanocompositePercolation (cognitive psychology)Composite materialPiezoresistive effectQuantum tunnellingTube (container)Molecular dynamicsNanotechnologyPolymerElectrical resistivity and conductivityOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

High piezoresistivity is critical for multifunctional carbon nanotube polymer composites with sensing capability. By developing a new percolation network model, this work reveals theoretically that a giant piezoresistivity in the composites can be potentially achieved by controlled nanotube alignment resulting from field based alignment techniques. The tube-tube and/or tube-matrix interaction in conjunction with the aligned carbon nanotube networks are fully considered in the newly proposed model. The structural distortion of nanotubes is determined self-consistently by minimizing the pseudo-potential energy at crossed-tube junctions based on the Lennard-Jones potential and simulation of coarse grain molecular dynamics. The tunneling transport through crossed-tube junctions is calculated by the Landauer-Büttiker formula with empirical fitting by first-principle calculation. The simulation results also reveal that the piezoresistivity can be further improved by using low carbon nanotube loadings near the percolation threshold, carbon nanotubes with a small aspect ratio, high intrinsic conductivity and polymers with a small Poisson's ratio. This giant piezoresistive effect offers a tremendously promising future, which needs further thorough exploration.

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 categoriesMeta-epidemiology (narrow)
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 score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.249
Teacher spread0.238 · 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.

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

Citations34
Published2014
Admission routes2
Has abstractyes

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