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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 OpenAlex

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

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.263
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.005
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