MétaCan
Menu
Back to cohort
Record W2035532464 · doi:10.2202/1542-6580.1279

Carbon Nanotube Synthesis: A Review

2005· review· en· W2035532464 on OpenAlex
Carole E. Baddour, Cedric Briens

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

VenueInternational Journal of Chemical Reactor Engineering · 2005
Typereview
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsWestern University
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceNanotechnologyElectric arcCarbon fibersCatalysisNanomaterialsFluidized bedLaser ablationProcess engineeringWaste managementComposite materialLaserChemistryOrganic chemistryEngineeringElectrode

Abstract

fetched live from OpenAlex

Discovered in 1991, carbon nanotubes (CNTs) have reached the forefront of many industrial research projects. Carbon nanotubes are tubular carbon molecules with remarkable mechanical, chemical, thermal and electrical properties, which make them useful in various applications. This paper reviews three methods of synthesizing the nanomaterial, namely arc-discharge, laser-ablation and fixed bed/fluidized bed catalytic. These methods have generated a large interest in many industrial companies to date. At the moment, the most critical issue faced by industrial companies is determining the best synthesis method, which will give the most economical large-scale production of CNTs. Compared to the other two methods, the catalytic technique to synthesize CNTs is simple, inexpensive, energy-efficient and can produce large CNTs quantities of high yield and purity.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.851
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.307
Teacher spread0.286 · 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