Reactive polycyclic aromatic hydrocarbon dimerization drives soot nucleation
Bibliographic record
Abstract
Nucleation is an important, yet poorly understood step in soot formation. Here, the importance of reactive PAH dimerization in reducing soot nucleation reversibility is investigated by simulating soot formation in a so-called "nucleation" flame (P. Desgroux et al., Combust. Flame, 2017, 184, 153-166). There, inception of soot particles is prolonged at minimal subsequent growth. With only reversible PAH dimerization, the simulated soot concentration is negligible. Accounting however for PAH chemical bond formation after physical dimerization, stabilizes dimers by covalent bonds and increases the soot concentration by four orders of magnitude, in good agreement with Laser Induced Incandescence measurements. In particular, dimers of benzene with benzene, phenylacetylene, naphthalene, toluene, acenaphthylene and cyclopentapyrene make significant contributions to the total soot concentration. The abundance of dimers with small PAHs highlights the dominant role of PAH concentration over their size and dispersion forces on dimer formation. Higher collision factors are used for irreversible dimerization models using larger PAHs because of their lower concentrations and not their larger dispersion forces leading to reduced reversibility and more stable dimers. The qualitative trend of main peaks agrees well with stochastic simulations and aerosol mass spectra measured in the above "nucleation" as well as premixed flames highlighting the abundance of PAHs with five-membered rings and substituted aliphatic chains in incipient soot. The predicted number of trimers is very low, i.e. less than 3% of the total soot nuclei formed, indicating that covalently bonded PAH dimers can be the main contributors to soot nucleation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".