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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 OpenAlex
Seyed M. Mirvakili, Alexey Pazukha, William K. A. Sikkema, Chad W. Sinclair, Geoffrey M. Spinks, 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

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 × 10 6 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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.026
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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