Bibliographic record
Abstract
Abstract. Atmospheric aerosol particles and their interactions with clouds are among the largest sources of uncertainty in global climate modeling. Aerosol particles in the ultrafine size range with diameters less than 100 nm have very high surface area to volume ratios, with a substantial fraction of molecules occupying the air–droplet interface. The partitioning of surface-active species between the interior bulk of a droplet and the interface with the surrounding air plays a large role in the physicochemical properties of a particle and in the activation of ultrafine particles, especially those of less than 50 nm diameter, into cloud droplets. In this work, a novel and thermodynamically rigorous treatment of bulk–surface equilibrium partitioning is developed through the use of a framework based on the Aerosol Inorganic–Organic Mixtures Functional groups Activity Coefficients (AIOMFAC) model in combination with a finite-depth Guggenheim interface region on spherical, finite-volume droplets. We outline our numerical implementation of the resulting modified Butler equation, including accounting for challenging extreme cases when certain compounds have very limited solubility in either the surface or bulk phase. This model, which uses a single, physically constrained interface thickness parameter, is capable of predicting the size-dependent surface tension of complex multicomponent solutions containing organic and inorganic species. We explore the impacts of coupled surface tension changes and changes in bulk–surface partitioning coefficients for aerosol particles ranging in diameters from several µm to as small as 10 nm and across atmospherically relevant relative humidity ranges. The treatment of bulk–surface equilibrium leads to deviations from classical cloud droplet activation behavior as modeled by simplified treatments of the Köhler equation that do not account for bulk–surface partitioning. The treatments for bulk–surface partitioning laid out in this work, when applied to the Köhler equation, are in agreement with measured critical supersaturations of a range of different systems. However, we also find that challenges remain in accurately modeling the growth behavior of certain systems containing small dicarboxylic acids, especially in a predictive manner. Furthermore, it was determined that the thickness of the interfacial phase is sensitive parameter in this treatment; however, constraining it to a meaningful range allow for predictive modeling of aerosol particle activation into cloud droplets, including cases with consideration of co-condensation of semivolatile organics.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.025 | 0.025 |
| Insufficient payload (model declined to judge) | 0.115 | 0.081 |
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".