Overview of methods to characterize the mass, size, and morphology of soot
Bibliographic record
Abstract
Combustion and other high-temperature processes can produce solid aerosol nanoparticles with complex morphologies, including fractal-like aggregates of primary particles. Characterizing these morphologies, as well as particle mass, is key to understanding their behavior in natural and engineered systems, and it can provide clues to the origin of the particles. We focus here on the characterization of soot, although most of the techniques apply to other aerosol aggregates. A complete description of these aerosols would include the mass and morphology of every particle. In practice, it is possible to obtain detailed information on individual particles from microscopy of extracted samples. A particular focus of this review, tandem classifier/detector systems can determine 2-dimensional mass and mobility distributions that may be interpreted through the lens of fractal models. Very fast in situ light scattering measurements can be used to determine the structure factor, related to fractal dimension, and the aggregate and primary particle size distributions. These approaches are complementary when there are appropriate models to connect morphological details to optical and transport characteristics of the particles. Over the last few decades these models have become more sophisticated, requiring more information on the particle structure and properties, but also facilitating more sophisticated inferences from in-situ and online measurement techniques.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".