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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 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 score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
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 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

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