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Record W2173328185 · doi:10.1149/1.2998527

Carbon Nanotube Yarn Actuators: An Electrochemical Impedance Model

2008· article· en· W2173328185 on OpenAlexaff
Tissaphern Mirfakhrai, Jiyoung Oh, Mikhail E. Kozlov, Shaoli Fang, Mei Zhang, Ray H. Baughman, John D. W. Madden

Bibliographic record

VenueECS Transactions · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceCarbon nanotubeCapacitanceDielectric spectroscopyElectrodeComposite materialYarnCapacitorActuatorElectrochemistryResistorElectrolyteElectrical impedancePorosityNanotechnologyVoltageElectrical engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

Twist-spun yarns made of carbon nanotubes have been shown to work as electrochemical actuators and force sensors. Deep understanding of yarn electrochemical behavior has so far not been possible, in part because of complicated yarn geometry. The electrochemical response of these yarns at different bias potentials was studied using Electrochemical Impedance Spectroscopy and compared with theoretical results for a cylindrical porous electrode. In the evaluated model, distributed capacitors and resistors are used to represent charge storage, electrolyte resistance and parasitic reactions. Resulting predictions are compared with experimental results and the capacitance per unit surface area of the MWNT bundles in the yarn is estimated. The model provides a foundation that can be elaborated upon for engineering 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.216
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2008
Admission routes1
Has abstractyes

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