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Record W2064416688 · doi:10.1109/tmtt.2013.2280120

Microwave Characterization of Carbon Nanotube Yarns For UWB Medical Wireless Body Area Networks

2013· article· en· W2064416688 on OpenAlexfundno aff
Syed Muzahir Abbas, Oya Sevimli, Michael Heimlich, Karu P. Esselle, Bahram Kimiaghalam, Javad Foroughi, Farzad Safaei

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2013
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsnot available
FundersUniversity of Victoria
KeywordsCarbon nanotubeMaterials scienceYarnResistive touchscreenCharacterization (materials science)Composite materialMicrowaveNanotechnologyThermal conductivityNanoscopic scaleConductivityElectrical resistivity and conductivityWirelessElectrical engineeringComputer scienceEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Carbon nanotube (CNT) yarns are novel CNT-based materials that extend the advantages of CNT from the nanoscale to macroscale applications. In this study, we have modeled CNT yarns as potential data transmission lines. Test structures have been designed to measure electrical properties of CNT yarns, which are attached to these test structures using gold paste. DC testing and microwave S-parameter measurements have been conducted for characterization. The observed frequency independent resistive behavior of the CNT yarn is a very promising indicator that this material, with its added values of mechanical resilience and thermal conductivity, could be invaluable for a range of applications such as body area networks. A model is developed for the CNT yarn, which fits the measured data collected and agrees in general with similar data for non-yarn CNTs.

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)0.000
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: none
Teacher disagreement score0.697
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.202
Teacher spread0.196 · 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
Published2013
Admission routes1
Has abstractyes

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