Assessing relative contributions of PAHs to soot mass by reversible heterogeneous nucleation and condensation
Bibliographic record
Abstract
Given the recent EURO 6 regulations, which include limits on particle number density (and hence size) for soot emissions from land vehicles, soot models must be capable of accurately predicting soot particle sizes. Previous modeling work has demonstrated the importance of the relative strengths of nucleation and condensation in predicting soot primary particle size. Due to this importance, a fundamental reversible model for nucleation and condensation, called the reversible PAH clustering (RPC) model, was developed in previous work through the use of statistical mechanics and the results from several recent works. In the present work, the RPC model is enhanced to include multiple nucleation (or dimerization) events from 6 different PAH size groups, resulting in 21 unique dimer pairs. In addition, a soot PAH tracking model is developed to track the amount of each PAH size group within soot particles. The addition of this model resulted in reduced computation times and the ability to investigate PAH-PAH reactions within soot particles. The results of the enhanced RPC model demonstrate that smaller PAHs are most important for the nucleation process, while small and large PAHs are important for the condensation process. These results are shown to be due to the relatively lower reversibility of condensation versus the nucleation process. These findings are discussed in light of recent experimental results in the literature and are shown to be well supported. Keywords: reversibility, PAH nucleation, PAH condensation, laminar diffusion flame, soot model
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".