Accounting for the effects of non-ideal minor structures on the optical properties of black carbon aerosols
Bibliographic record
Abstract
Abstract. Black carbon (BC) aerosol is the strongest sunlight-absorbing aerosol, and its optical properties are fundamental to radiative forcing estimations and retrievals of its size and concentration. During incomplete combustion, BC particles exist as aggregate structures with small monomers, which are widely represented by the idealized fractal aggregate model formed by monodisperse spherical monomers in point-contact. In reality, BC particles possess complex and non-ideal minor structures besides the overall aggregate structure, altering its optical properties in unforeseen ways. This study introduces a parameter volume variation to quantify and unify different minor structures, and develops an empirical relation to account for their effects on BC optical properties from those of ideal aggregates. Minor structures considered are the polydispersity of monomer size, irregularity and coating of individual monomer, and necking and overlapping among monomers. The discrete dipole approximation is used to calculate the optical properties of aggregates with these minor structures. Minor structures result in scattering cross section enhancement slightly more than that of absorption cross section, and their effects become weaker with the increase of wavelength. Their effects on the angular-dependent phase matrix as well as asymmetry factor are negligible. Our results suggest that a correction ratio of 1.05 is necessary to account for the mass/volume normalized absorption and scattering of non-ideal aggregates in comparison to ideal ones. In other words, minor structures tend to enhance the BC mass absorption and scattering by 5 %, which also applies to aggregates with multiple minor structures. The simulations of optical properties of non-ideal aggregates are greatly simplified, because they can be directly obtained from those of the corresponding ideal aggregates. We expect this generalized correction to find wide use for modeling realistic BC aggregates due to the simplicity involved in generating ideal fractal aggregates, and it is of great value for not only interpretation of measurements but also practical modeling that requires large amount of simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".