A Framework for Quantifying the Size and Fractal Dimension of Compacting Soot Particles
Bibliographic record
Abstract
Black carbon (BC) is a strongly absorbing component of atmospheric aerosols that has a significant warming effect. BC particles are emitted from combustion sources as open-structured fractal aggregates. After emission, BC is often compacted due to capillary condensation of semivolatile vapors to form coatings. The addition of coatings influences the size and radiative properties of BC, but representing these details in radiative transfer models is computationally difficult and often neglected. Laboratory studies have measured BC restructuring during coating but rarely provide information on changes in particle shape. Here, we combine laboratory measurements of BC compaction with detailed restructuring models to develop a framework for predicting the size and shape of BC as a function of coating volume ratio, a property already tracked in large-scale atmospheric models. The framework predicts the mobility diameter and fractal dimension of BC particles as a function of coating volume throughout compaction with root-mean-squared error (RMSE) values less than 6.8 and 4.3%, respectively. These properties are predicted for both the coated particle and the BC core. Our proposed framework will enable a more complete representation of the evolving size and shape of BC throughout its atmospheric lifetime, thereby improving model accuracy at a low computational cost.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".