Effective density and packing of compacted soot aggregates
Bibliographic record
Abstract
The mass concentration of soot aggregates is often estimated from mobility size distributions using a mobility-based effective density, ρeff. This ρeff changes with aggregate morphology. In particular, the ρeff of a soot core increases when it becomes compacted by the surface tension of condensing coatings, such as combustion-related vapours, secondary aerosols, and cloud water. The extent of this compaction is a function of coating volume, up to an asymptotic limit of complete compaction. While complete compaction has previously been shown to correspond to a universal, scale-invariant packing factor for sufficiently large aggregates, it has not previously been explicitly quantified. Here, we critically reanalyze multiple datasets compiled from the literature on the ρeff of completely compact soot. We show that, regardless of the coating material, soot aggregates generally become completely compacted following a 5-fold increase in volume. The final aggregate shape is more simply described by ρeff than by mobility diameter or shape factor. Below 140 nm diameter (about 20 aggregate monomers), the compacted ρeff obeys a power law; above 140 nm, it reaches a constant value of 651 ± 8 kg m–3. This ρeff is 3 times larger than that of freshly-produced soot at 140 nm. We provide a parameterization of the compacted ρeff for the estimation of soot mass concentration after coating, or, conversely, for use as a benchmark to estimate the extent of aggregate restructuring. Our parameterization can be easily adapted to other nanoparticle aggregates whose material density is known.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".