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Record W2330076417 · doi:10.1166/jnn.2009.1095

Stretchable Carbon Nanosprings Production by a Catalytic Growth Process

2009· article· en· W2330076417 on OpenAlexaff
Sébastien Vaudreuil, Mosto Bousmina

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2009
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceCarbon nanotubeNanotechnologyCurvatureDeformation (meteorology)Carbon fibersOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

Due to their peculiar electromagnetic and mechanical properties, helical carbon nanotubes (HCNT) have important potential applications in micro and nanoelectromechanical fields where they can be used as springs, magnetic field detectors, electromagnets, inductors and actuators. We report here the synthesis of carbon nanosprings through a non-classical catalytic growth process using a nanoporous support, where the regular array of openings allows the control of the diameter and coil pitch of the nanosprings. Exposition of such carbon nanosprings to a laser beam beyond an energy threshold can induce a permanent deformation characterized by the decoiling of the spring. Such permanent deformation results from an internal stresses relief mechanism, where pentagon-heptagon defect pairs reverting back to hexagon rings reduce the curvature of the coiled nanotube. This is expected to have important potential therapeutic applications in which carbon nanosprings can be located specifically in artery channels to remove fatty substances. They can also be located within cancer cells through specific interactions, after which the cells would be destroyed through the use of a more energetic but not harmful laser light. Such energy application is expected to fragment HCNT into digestible carbon for assimilation by the human body.

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)0.001
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 score0.006
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.004
GPT teacher head0.228
Teacher spread0.223 · 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.

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

Citations5
Published2009
Admission routes1
Has abstractyes

Explore more

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