Relationship between Coating-Induced Soot Aggregate Restructuring and Primary Particle Number
Bibliographic record
Abstract
The restructuring of monodisperse soot aggregates due to coatings of secondary organic aerosol (SOA) was investigated in a series of photo-oxidation chamber experiments. Soot aggregates were generated by one of three sources (an ethylene premixed burner, a methane inverted diffusion burner, or a diesel generator), treated by denuding, size-selected by a differential mobility analyzer, and injected into a smog chamber, where they were exposed to the photo-oxidation products of p-xylene, which partitioned to form SOA coatings. The evolution of aggregates from their initial to final morphologies was investigated in situ by mobility and mass measurements and ex situ by transmission electron microscopy. At a given initial aggregate mobility diameter, diesel aggregates are less dense and composed of smaller primary particles than those generated by the two burners, and they restructure to a smaller final mobility diameter. Remarkably, the final degrees of restructuring of aggregates from all three sources exhibit the same linear dependence on the number of primary particles per aggregate. The observed linear relationship, valid for the atmospherically relevant SOA coating investigated here, could allow modelers to predict the evolution of aggregate morphology based on a single property of the aggregates.
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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 | 0.000 | 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".