Measurement and modeling of the multi-wavelength optical properties ofuncoated flame-generated soot
Bibliographic record
Abstract
Abstract. Optical properties of flame-generated black carbon (BC) containing soot particles were quantified at multiple wavelengths for particles produced using two different flames, a methane diffusion flame and an ethylene premixed flame. Measurements were made for: (i) nascent soot particles, (ii) thermally denuded nascent particles, and (iii) particles that were coated then thermally denuded, leading to collapse of the initially lacy, fractal-like morphology. The measured mass absorption coefficients (MAC) depended on soot maturity and generation, but were similar between flames for similar conditions. For mature soot, here corresponding to particles with volume-equivalent diameters > ~ 160 nm, the MAC and absorption Angstrom exponent values were independent of particle collapse while the single scatter albedo increased. The MAC values for these larger particles were also size-independent. Effective, theory-specific complex refractive index (RI) values are derived from the observations with two widely-used methods: Lorenz-Mie theory and the Rayleigh-Debye-Gans (RDG) approximation. Mie theory systematically under-predicts the observed absorption cross-sections at all wavelengths for larger particles (with x > 0.9) independent of the complex RI used, while RDG provides good agreement. Importantly, this implies that the use of Mie theory within air quality and climate models, as is common, likely leads to under-predictions in the absorption by BC, with the extent of under-prediction depending on the assumed BC size distribution and complex RI used. We suggest that it is more appropriate to assume a constant, size-independent (but wavelength-specific) MAC to represent absorption by uncoated BC particles within models.
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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 | 0.000 |
| 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".