A One-Dimensional Model for Coagulation, Sintering, and Surface Growth of Aerosol Agglomerates
Bibliographic record
Abstract
The size of the primary particles in aerosol agglomerates is determined in part by the interplay of surface growth, coagulation, and sintering. These processes are modelled by a one-dimensional (1-D) discrete-sectional model, DISGLOM2, which predicts the evolution of agglomerate and primary particle size distributions. DISGLOM2 is an extended version of DISGLOM (Rogak 1997), in which particles smaller than the "melting diameter" were assumed to sinter instantly while bigger particles did not sinter at all. Gradual sintering, "condensational obliteration" (whereby primary particles are lost during heavy surface growth), and diffusional wall deposition have been incorporated into DISGLOM2. Results from DISGLOM2 were comparable with those from 2-D sectional models, but DISGLOM2 was much faster. In addition, DISGLOM2 includes the effects of "condensation" of small spherical particles on large agglomerates, which were not modelled previously. The effect of condensation was shown to be significant at low temperature. DISGLOM2 was used to predict the primary particle diameter of titania particles generated by precursor reaction. By adding gradual sintering, the growth rate of agglomerate particles by coagulation was slightly decreased and the primary particle size considerably increased compared with the results given by DISGLOM. Although DISGLOM2 is an efficient model of the relevant physical processes, the predictions are sensitive to the kinetics of precursor reactions and particle sintering, which can be difficult to characterize in real experimental systems.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".