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Carbon Nanotube Yarns as High Load Actuators and Sensors

2008· article· en· W2020642317 on OpenAlex
Tissaphern Mirfakhrai, Ji Young Oh, Mikhail E. Kozlov, Shaoli Fang, Mei Zhang, Ray H. Baughman, John D. W. Madden

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in science and technology · 2008
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceSpinningActuatorComposite materialCarbon nanotube actuatorsYarnVoltageTension (geology)NanotechnologyUltimate tensile strengthCarbon fibersNanotubeMechanical properties of carbon nanotubesComposite numberElectrical engineering

Abstract

fetched live from OpenAlex

Carbon nanotubes have attracted extensive attention in the past few years because of their appealing mechanical and electronic properties. Yarns made through spinning multiwall carbon nanotubes (MWNTs) have been reported. Here we study the application of these yarns as electrochemical actuators, and as force sensors. MWNT yarns are mechanically strong with tensile strengths reaching one GPa. When charge is stored in the yarns they change in length. This is thought to be because of a combination of electrostatic and quantum chemical effects. We report strains up to 0.6 %. The charged yarns can also generate current and change in voltage in response to a change in the applied tension. Electrostatic and quantum effects contributing to actuation are introduced along with the effect of the yarn geometry on actuation and other contributing factors.

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.

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: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
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 head0.265
Teacher spread0.257 · 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