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Record W1987206899 · doi:10.1117/12.2046188

Mechanism of stroke enhancement by coiling in carbon nanotube hybrid yarn artificial muscles (presentation video)

2014· article· en· W1987206899 on OpenAlexaff
Carter S. Haines, Márcio D. Lima, Na Li, Geoffrey M. Spinks, Javad Foroughi, John D. W. Madden, Shaoli Fang, Mônica Jung de Andrade, Fatma Göktepe, Özer Göktepe, Seyed M. Mirvakili, Sina Naficy, Xavier Lepró, Jiyoung Oh, Mikhail E. Kozlov, Seon‐Jeong Kim, Xiuru Xu, Gordon G. Wallace, Ray H. Baughman

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceYarnUltimate tensile strengthComposite materialCoil springArtificial muscleMechanism (biology)Carbon nanotubeElectromagnetic coilTwistFiberActuatorSpring (device)Mechanical engineeringComputer scienceGeometryElectrical engineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Twisted carbon nanotube yarns have been shown to develop useful torsional and tensile actuation. Particularly useful are those hybrid yarns that incorporate a volume-changing guest material into the yarn pore space. Changing guest volume causes concomitant untwisting and shortening of the twisted yarn. Intriguingly, the magnitude of the tensile actuation can be increased by an order of magnitude by inserting such high twist into the fiber as to cause coiling. The mechanism of coil-induced stroke enhancement is investigated using ordinary spring mechanics and it is shown that tensile actuation can be adequately predicted from the coil and yarn geometries.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.008
GPT teacher head0.217
Teacher spread0.208 · 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 designBench or experimental
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Materials and MechanicsFrench-language works237,207