MétaCan
Menu
Back to cohort
Record W1975784306 · doi:10.1021/ef050081q

Detailed Kinetic Modeling of Carbonaceous Nanoparticle Inception and Surface Growth during the Pyrolysis of C<sub>6</sub>H<sub>6</sub> behind Shock Waves

2006· article· en· W1975784306 on OpenAlexaff
John Z. Wen, Murray J. Thomson, M.F. Lightstone, Steven N. Rogak

Bibliographic record

VenueEnergy & Fuels · 2006
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of British ColumbiaMcMaster UniversityUniversity of TorontoUniversity of New Brunswick
Fundersnot available
KeywordsSootNucleationCombustionParticle (ecology)Materials scienceAerosolCarbon fibersPyrolysisNanoparticleSupersaturationChemical engineeringThermodynamicsChemistryChemical physicsNanotechnologyPhysical chemistryOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Soot formation in combustion processes is of significant interest due to its influences on both environmental emissions and material synthesis (i.e., the synthesis of fullerences and carbon nanotubes). However, the inception process of the youngest carbonaceous nanoparticles from the gaseous phase is the most poorly understood phenomenon in the study of soot kinetics at the current stage. Recently, researchers have found experimentally the existence of transparent or semi-transparent carbonaceous particles (Krestinin, A. V. Combust. Flame 2000, 121, 513−524) or nanoorganic carbon particles (D'Anna, A.; Rolando, A.; Allouis, C.; Minutolo, P.; D'Alessio, A. Proc. Combust. Inst. 2004, 30, 1449−1456) during soot nucleation, which have not been successfully explained by traditional polycyclic aromatic hydrocarbon (PAH) nucleation mechanisms. Most recently, a more detailed soot kinetic model (Vlasov, P. A.; Warnatz, J. Proc. Combust. Inst. 2002, 29, 2335−2341; Part 2) has been implemented to predict soot formation behind shock waves and to describe the soot nucleation as a combined process of the fast polymerization of supersaturated polyyne vapor and the PAH growth. The lack of a detailed description of fractal particle structures in their aerosol dynamics model, however, restricted the model's accuracy in predicting the particle coagulation rates and, hence, the particle sizes. In the current study, a new comprehensive kinetic model has been developed to describe soot chemical processes in a heterogeneous phase. The nucleation process is described by the formation of the soot precursors and the transformation from those precursors to solid soot particles. The precursors are represented by six sectional bins, which are formed through the detailed PAH nucleation mechanism and polyyne pathways, respectively. The gaseous reaction mechanism has been validated against measurements of polyynes and the C/C 2 /C 3 carbon radicals. Finally, the aforementioned soot kinetic model has been implemented in an advanced aerosol dynamics model to predict the main parameters of soot particle formation in the pyrolysis of C 6 H 6 /Ar mixture. This aerosol dynamics model includes the detailed description of the agglomerate structure of soot particles and calculates the particle coagulation rates according to their sizes and structures. The numerical simulation shows that, during the fuel pyrolysis behind shock waves, both PAH growth and polyynes polymerization play an important role during the soot nucleation process. And the polyynes surface growth model alone is able to predict soot yield as well as averaged particle diameter during the earlier stage of soot formation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.179
Teacher spread0.174 · 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 designSimulation or modeling
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

Citations41
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicAdvanced Combustion Engine TechnologiesFrench-language works237,207