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Record W4380343888 · doi:10.6000/1929-5995.2020.09.10

A Review on Carbon Nanotubes: Preparation, Properties and Applications

2020· review· en· W4380343888 on OpenAlexvenueno aff
Md Nur Karim, M.A. Sayed Patwary, Samad Abedin, Md Riaj Hossen, Md Saifur Rahman

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2020
Typereview
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceGrapheneNanotechnologyChemical vapor depositionLaser ablationCarbon nanobudCarbon fibersPotential applications of carbon nanotubesCarbon nanofiberCarbon nanotube actuatorsOptical properties of carbon nanotubesElectric arcMechanical properties of carbon nanotubesComposite materialLaserNanotubeComposite numberElectrode

Abstract

fetched live from OpenAlex

Carbon nanotubes(CNTs) have achieved attention in recent times because of their extraordinary physicochemical properties like strength, flexibility, sensors, conducting etc. Carbon nanotubes(CNTs) are known as nano-architectured allotropes of carbon, having graphene sheets which are rolled up into cylinder that forms carbon nanotubes. In the field of nanotechnology, carbon nanotubes are the one of the most unique invention. The eye-catching features of carbon nanotubes are their electronic, mechanical, optical and chemical characteristics, which open a way to future applications. Carbon nanotubes can be single walled and multi walled which can be produced in various ways. The most common techniques used nowadays are: arc discharge, laser ablation and chemical vapour deposition. In this review article,the applications of CNTs in various technologically important fields are discussed in detail.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.005

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 head0.103
GPT teacher head0.425
Teacher spread0.322 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Research Updates in Polymer ScienceSame topicCarbon Nanotubes in CompositesFrench-language works237,207