Partitioning of ionic surfactants in aerosol droplets containing glutaric acid, sodium chloride, or sea salts
Bibliographic record
Abstract
Sea spray is the largest contributor to atmospheric aerosol by mass and contains mixtures of inorganic salts and organics. The chemically complex organic fraction can contain soluble, highly surface-active organics, and field studies commonly identify ionic surfactants in aerosol samples. In macroscopic solutions, divalent cations present in sea spray can alter the partitioning of ionic surfactants. Furthermore, the high surface area-to-volume (SA / V) ratio of aerosol droplets may lead to depletion of surfactant from the bulk, requiring more surfactant, relative to its volume, to lower the surface tension of a droplet compared to a macroscopic solution. Here, we investigate the partitioning of model ionic surfactants (sodium dodecyl sulfate, an anionic surfactant, and cetrimonium bromide, a cationic surfactant) in 6–10 µm radius droplets containing glutaric acid, NaCl, or sea spray mimic cosolutes. Surface tension measurements are compared to two independent partitioning models accounting for the SA / V ratio of the droplets. Salting out of the ionic surfactants leads to strong bulk depletion in 6–10 µm radius droplets, with no observable difference in droplet surface tension between NaCl and sea spray mimic cosolutes. The total ionic surfactant concentration required to reach the minimum surface tension for these droplets was 2.0 ± 0.5 mM, consistent with previous observations in droplets containing strong surfactants. Modeling results suggest that surfactant concentrations on the order of tens to hundreds of millimolar are required to significantly reduce surface tension in 100 nm droplets. These results have implications for cloud droplet activation and chemistry occurring at the interface of sea spray aerosol.
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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) | 0.000 | 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 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".