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Record W4400317775 · doi:10.3390/reactions5030022

Carbon Nanotubes: A Review of Synthesis Methods and Applications

2024· review· en· W4400317775 on OpenAlexaff
Arash Yahyazadeh, Sonil Nanda, Ajay K. Dalai

Bibliographic record

VenueReactions · 2024
Typereview
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsDalhousie UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsCarbon nanotubeSurface modificationNanotechnologyMaterials scienceLaser ablationChemical vapor depositionCharacterization (materials science)Electric arcCarbon fibersFabricationChemical engineeringLaserChemistryComposite materialElectrodeEngineering

Abstract

fetched live from OpenAlex

Carbon nanotubes (CNTs) are cylindrical-shaped materials composed of hexagonally arranged hybridized carbon atoms with versatility in synthesis methods and diverse applications. This review is focused on the fabrication, physicochemical and spectroscopic characterization, and industrial applications of CNTs. This review discusses some promising synthesis methods for the preparation of CNTs such as catalytic chemical vapor deposition, arc discharge, and laser ablation. A comparative discussion is made between these synthesis methods in terms of strengths, opportunities and challenges. Furthermore, functionalization and purification of CNTs’ surface leading to improved functionality has also been highlighted in this article. Finally, the analytical techniques employed to shed light on the physicochemical and morphological properties of CNTs are described.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.047
GPT teacher head0.412
Teacher spread0.365 · 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

Citations91
Published2024
Admission routes1
Has abstractyes

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