Enhancement in Secondary Organic Aerosol Formation in the Presence of Preexisting Organic Particle
Bibliographic record
Abstract
Atmospheric models of secondary organic aerosol (SOA) typically assume organic species form a well-mixed phase. As a result, partitioning of semivolatile oxidation products into the particle phase to form SOA is thought to be enhanced by preexisting organic particles. In this work, the physicochemical properties that govern such enhancement in SOA yield were examined. SOA yields from α-pinene ozonolysis were measured in the presence of a variety of organic seeds which were chosen based on polarity and phase state at room temperature. Yield enhancement was only observed with seeds of medium polarities (tetraethylene glycol and citric acid). Solid hexadecanol seed was observed to enhance SOA yields only in chamber experiments with longer mixing time scales, suggesting that the mixing process for SOA and hexadecanol may be kinetically limited at shorter time scales. Our observations indicate that, in addition to kinetic limitations, intermolecular interactions also play a significant role in determining SOA yields. Here we propose for the first time to use the Hansen solubility framework to determine aerosol miscibility and predict SOA yield enhancement. These results highlight that current models may overestimate SOA formation, and parametrization of intermolecular forces is needed for accurate predictions of SOA formation.
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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) | 0.001 | 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".