The mobility diameter of soot determines its angular light scattering distribution
Bibliographic record
Abstract
Characterization of soot agglomerates often relies on the angular distribution of their light scattering cross-section, C, that is based on the structure factor exponent, Ds, and asymmetry parameter, g, and depend on agglomerate mobility, dm, and constituent primary particle, dp, size distributions. Here, discrete element modeling is interfaced with discrete dipole approximation to determine C in the range of dm = 60–450 nm with mean dp = 9–26 nm that are most prevalent in fire detection, air pollution and climate change. Increasing dm reduces the effective density, ρeff, and drastically increases Ds and g (by a factor of about 2–20), while increasing dp or its geometric standard deviation increases ρeff but only slightly decreases Ds (10–20%). Thus, the angular light scattering distribution of soot is largely determined by its dm. Currently, rather constant Ds and g are obtained for large agglomerates (dm ≥ 250 nm) by laser diagnostics based on the Rayleigh Debye Gans theory and used in climate models. This overestimates the dm for small soot agglomerates by up to a factor of four and underestimates their radiative forcing efficiency by 10%. So, relations between Ds, g and dm are derived here and validated with data from our premixed flames and literature diffusion flame and field data. These relations cover the evolution of Ds and g with dm and nicely converge to the constant Ds for dm ≥ 250 nm. As such, they can facilitate the characterization of soot agglomerates by light scattering and help to quantify accurately the soot contribution to global warming.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".