MétaCan
Menu
Back to cohort
Record W2019723130 · doi:10.2202/1542-6580.1170

Dodecane Decomposition in a Radio-Frequency (RF) Plasma Reactor

2005· article· en· W2019723130 on OpenAlexaff
Laura Merlo-Sosa, Gervais Soucy

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCarbon blackCarbon fibersPlasma torchPyrolysisChemical engineeringPlasmaMaterials scienceDodecaneDecompositionAnalytical Chemistry (journal)ChemistryNuclear chemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

The research outlined here includes a study of the production of carbon black (CB) in an inductive plasma reactor, using dodecane (C12H26) as the starting material. Thermodynamic and kinetic studies were carried out to predict the species concentrations likely to be obtained in the pyrolysis process at plasma temperatures. A thorough statistical experimental design was undertaken to investigate the influence of the different operational conditions such as: the feed rates of dodecane, the reactor pressure, the plate power applied to the plasma torch and the composition of the plasma gas, on the production of carbon black and gaseous acetylene-like compounds. Thermodynamic and kinetic models were compared with experimental results, a kinetic reaction model best representing the experimental results. Morphological analysis of the solid product using Transmission Electron Microscopy (TEM) indicates that a high plasma temperature is the most important factor affecting the final morphology of the carbon black formed during the reaction. Carbon black with average particle size of 10-30 nm and specific surface of 130 m2/g was obtained. Morphological analyses also demonstrated the presence of new structures ranging from carbon black to fullerenes and including certain “graphitized” carbon molecules.

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)0.000
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 score0.045
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.005
GPT teacher head0.224
Teacher spread0.219 · 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

Citations3
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Chemical Reactor EngineeringSame topicFiber-reinforced polymer compositesFrench-language works237,207