Model-Measurement Comparisons for Surfactant-Containing Aerosol Droplets
Bibliographic record
Abstract
Surfactants are important components of atmospheric aerosols, potentially impacting their hygroscopic growth and eventual activation into cloud droplets. By adsorbing at the air-water interface, surfactants lower the surface tension of aqueous systems. However, in microscopic aerosol droplets, the bulk surfactant concentration can become depleted because of the droplets' high surface-area-to-volume ratio, reducing the bulk surfactant concentration at equilibrium and increasing droplet surface tension. Partitioning models have been developed to account for the concentration- and size-dependencies of surface tension, but these models have rarely been assessed against experimentally measured droplet surface tensions. Here, we directly compare surface tension predictions made using a simple kinetic partitioning model and a thermodynamic monolayer partitioning model against experimentally measured picoliter droplet surface tensions for 12 surfactant-cosolute systems. Surface tension predictions were also made across 8 orders of magnitude in droplet radius. The largest differences between model predictions were associated with the predicted onset of bulk depletion. The quality of the isotherm or parametrization fit to the macroscopic data most strongly influenced a model's ability to accurately predict droplet surface tension. These results highlight the importance of validating partitioning models against droplet surface tension measurements in size ranges where bulk depletion is expected to occur and motivate collection of high-quality macroscopic surface tension data sets that serve as model inputs. The results also validate both models' abilities to predict aerosol surface tension across size and composition, which will facilitate their eventual incorporation into cloud parcel models to explore the impact of surface tension assumptions on cloud droplet number concentration.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".