Effects of the Fractal Prefactor on the Optical Properties of Fractal Soot Aggregates
Bibliographic record
Abstract
The effects of prefactor on the optical properties of numerically generated fractal soot aggregates were investigated using the numerically exact generalized multi-sphere Mie-solution method (GMM) and the approximate Rayleigh-Debye-Gans (RDG) theory. The numerically generated fractal aggregates consist of 50 to 400 primary particles of 30 nm in diameter. The considered incident laser wavelength is 266 nm. Attention is paid to the effect of prefactor on the vertical-vertical differential scattering cross section, since such quantity has often been used to infer the fractal dimension and prefactor based on the RDG formulation. The fractal prefactor affects the optical properties of the numerically generated soot aggregates through its influence on the compactness of the structure. Using GMM to calculate the optical properties of the numerically generated aggregates results in a lower aggregate absorption cross section, but a higher total scattering cross section with increasing the prefactor. The difference between the RDG results and those of GMM is primarily caused by multiple scattering and such effect is found significant, especially for the higher value of prefactor considered. The fractal dimension derived from the GMM non-dimensional differential scattering cross section agrees well with the morphological value in the case of the lower prefactor of 1.3 considered; however, the derived fractal dimension is much higher than the morphological value for fractal soot aggregates with a prefactor of 2.3. The light scattering derived prefactor is in general lower than the morphological value, especially when the morphological prefactor is higher.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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 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".