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Detailed Numerical Simulation of Single-Walled Carbon Nanotube Synthesis in a Radio-Frequency Induction Thermal Plasma System

2012· article· en· W2011897605 on OpenAlexaff
Sanaz Arabzadeh Esfarjani, Seth B. Dworkin, J. Mostaghimi, K S Kim, Christopher T. Kingston, Benoît Simard, Gervais Soucy

Bibliographic record

VenueJournal of Physics Conference Series · 2012
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversité de SherbrookeNational Research Council CanadaToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsCarbon nanotubeYield (engineering)Radio frequencyPlasmaCombingMaterials scienceThermalChemical reactionRF probeChemical engineeringChemistryNanotechnologyThermodynamicsRF power amplifierOrganic chemistryOptoelectronicsPhysicsComputer scienceComposite materialEngineering

Abstract

fetched live from OpenAlex

2D axisymmetric numerical calculations are conducted to model the thermo-fluid fields and chemical reactions leading to the formation of SWCNTs in an RF plasma system. A modified version of the SWCNT "reduced" chemical model is used to estimate the formation of SWCNT in an RF plasma system for the first time. The "reduced" model incorporates 14 species and 36 chemical reactions to predict the formation of metal and carbon clusters and SWCNTs. By combing the chemistry model into the RF plasma CFD code, the formation and development of carbon and metal catalyst clusters and their reactions which produce SWCNTs are shown. The chemistry model is shown to under-predict the yield rate of SWCNT. In order to better predict the yield rate, a sensitivity analysis is performed to modify the dominant reaction rates. The modified model predicts the yield of SWCNTs correctly within the range reported experimentally. However, more studies should be conducted to validate the accuracy of the model for different operating conditions.

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)0.000
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 score0.012
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.028
GPT teacher head0.243
Teacher spread0.215 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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