Overview of methods to characterize the mass, size, and morphology of soot
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it