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Record W2079828062 · doi:10.1002/adfm.201203808

Niobium Nanowire Yarns and their Application as Artificial Muscles

2013· article· en· W2079828062 on OpenAlexaff
Seyed M. Mirvakili, Alexey Pazukha, William K. A. Sikkema, Chad W. Sinclair, Geoffrey M. Spinks, Ray H. Baughman, John D. W. Madden

Bibliographic record

VenueAdvanced Functional Materials · 2013
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceNiobiumComposite materialNanowireUltimate tensile strengthCarbon nanotubeWaxModulusParaffin waxComposite numberEtching (microfabrication)YarnDeformation (meteorology)NanotechnologyLayer (electronics)Metallurgy

Abstract

fetched live from OpenAlex

Abstract Metal nanowires are twisted to form yarns that are strong (0.4 to 1.1 GPa), pliable, and more conductive (3 × 106 S m−1) than carbon nanotube yarns. Niobium nanowire fibers are extracted by etching a copper‐niobium nano‐composite material fabricated using the severe plastic deformation process. When impregnated with paraffin wax, the niobium (Nb) nanowire yarns produce fast rotational actuation as the wax is heated. The heated wax expands, untwisting the yarn, which then re‐twists upon cooling. Normalized to yarn length, 12 deg mm−1 of torsional rotation was achieved along with twist rates in excess of 1800 rpm. Tensile modulus of 19 ± 5 GPa was measured for the Nb yarns, which is very similar to those of carbon multiwalled nanotubes.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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
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

Citations101
Published2013
Admission routes1
Has abstractyes

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