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Record W2040057501 · doi:10.1002/cjce.20561

Optimisation of reaction conditions for the synthesis of single‐walled carbon nanotubes using response surface methodology

2011· article· en· W2040057501 on OpenAlexvenueaboutno aff
Weiwen Liu, Azizan Aziz, Siang‐Piao Chai, Abdul Rahman Mohamed, Ching Thian Tye

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon nanotubeMethaneCarbon fibersResponse surface methodologyNitrogenRaman spectroscopyMaterials scienceVolumetric flow rateAnalytical Chemistry (journal)Intensity (physics)ChemistryChemical engineeringNanotechnologyComposite numberThermodynamicsComposite materialChromatographyOrganic chemistryPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract The effects of three preparation variables, i.e. reaction temperature, reaction time and reaction gas (methane/nitrogen) flow rate, on the ratio of the intensity of the Raman D band to the intensity of the G band (ID/IG), carbon mass and the presence of radial breathing mode (RBM) peaks were investigated by using a central composite design to develop two linear models. The most influential factor in each experimental design‐response was identified using the analysis of variance. The predicted ID/IG ratio, carbon mass and presence of RBM peaks determined during the process optimisation were found to agree satisfactorily with the experimental values. The optimum conditions for synthesising single‐walled carbon nanotubes were determined to be a reaction temperature of 900°C, a reaction time of 59 min and a reaction gas flow rate of 54 mL/min. © 2011 Canadian Society for Chemical Engineering

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.073
GPT teacher head0.266
Teacher spread0.193 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations18
Published2011
Admission routes2
Has abstractyes

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Same venueThe Canadian Journal of Chemical EngineeringSame topicCarbon Nanotubes in CompositesFrench-language works237,207