Extension of the AIOMFAC Model for Atmospheric Aerosols Containing Partially Dissociating Organosulfates and Dicarboxylic Acids
Bibliographic record
Abstract
Organosulfates (OS) are emerging as a prominent secondary organic aerosol component, which can significantly alter the physicochemical properties and thus broader impacts of atmospheric aerosols. Despite their importance, OS-containing mixtures have yet to be studied using a detailed thermodynamic model which can account for the nonideal mixing among all species. In this work, we have extended the Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients (AIOMFAC) model, a robust thermodynamic model that predicts activity coefficients in aerosol mixtures, to support OS-containing mixtures, including solving for the partial dissociation of OS. Simultaneously, we have extended AIOMFAC to support the partial dissociation of dicarboxylic acids (DA). DA are a prevalent class of compounds in tropospheric aerosols, whose pH-dependent dissociation can significantly impact aerosol physicochemical properties. We show that, for simple OS-containing and DA-containing systems, AIOMFAC is able to predict water activity and acidity (pH) behaviors that are physically reasonable and agree well with measurements, including new water activity and pH measurements performed for this study. To date, partial dissociation support in AIOMFAC is limited to select OS (methyl sulfate, ethyl sulfate, isoprene-OS-3, and isoprene-OS-4) and select DA (malonic acid, succinic acid, and glutaric acid) in simple single-phase mixtures. However, as more thermodynamic data become available, AIOMFAC's treatment of organic acids can be further refined and expanded, enabling it to predict how the partial dissociation of organic acids affects the physicochemical properties of realistic multicomponent, multiphase aerosol systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".