Calculating Equilibrium Phase Distribution during the Formation of Secondary Organic Aerosol Using COSMO<i>therm</i>
Bibliographic record
Abstract
Challenges in the parametrization of compound distribution between the gas and particle phase contribute significantly to the uncertainty in the prediction of secondary organic aerosol (SOA) formation and are rooted in the complexity and variability of atmospheric condensed matter, which includes water, salts, and a multitude of organic oxidation products, often in two separated phases. Here, we explore the use of the commercial quantum-chemistry-based software COSMOtherm to predict equilibrium partitioning and Setchenow coefficients of a suite of oxidation products of α-pinene ozonolysis in an aerosol that is assumed to separate into an organic-enriched phase and an electrolyte-enriched aqueous phase. The predicted coefficients are used to estimate the phase distribution of the organic compounds, water and ammonium sulfate, the resulting phase composition, and the SOA yield. Four scenarios that differ in terms of organic loading, liquid water content, and chemical aging are compared. The organic compounds partition preferentially to the organic phase rather than the aqueous phase for the studied aerosol scenarios, partially due to the salting-out effect. Extremely low volatile organic compounds are predicted to be the dominant species in the organic aerosols at low loadings and an important component at higher loadings. The highest concentration of oxidation products in the condensed phase is predicted for a scenario assuming the presence of non-phase-separated cloud droplets. Partitioning into an organic aerosol phase composed of the oxidation products is predicted to be similar to partitioning into a phase composed of a single organic surrogate molecule, suggesting that the calculation procedure can be simplified without major loss of accuracy. COSMOtherm is shown to produce results that are comparable to those obtained using group contribution methods. COSMOtherm is likely to have a much larger application domain than those group contribution methods because it is based on fundamental principles with little calibration.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".