Effect of Sodium Dodecyl Benzene Sulfonate on the Production of Cloud Condensation Nuclei from Breaking Waves
Bibliographic record
Abstract
While sea spray particles are highly soluble by nature, and are thus excellent seeds for nascent cloud droplets, organic compounds such as surfactants have previously been identified within aerosol particles, bulk seawater, and the sea-surface microlayer in various oceans and seas. As the presence of dissolved surfactants within spray particles may limit their ability to act as cloud condensation nuclei (CCN), and since the abundance of CCN available during cloud formation is known to affect cloud albedo, the presence of surfactants in the marine environment can affect the local radiation balance. In this work, we added a model surfactant commonly used in households and industry (sodium dodecyl benzene sulfonate, SDBS) to a control solution of NaCl and observed its effects on the number of CCN produced by artificial breaking waves. We found that the addition of SDBS modified the number of CCN produced by a breaking wave analogue in three main ways: (I) by reducing the hygroscopicity of the resulting particulate; (II) by producing finer particulates than the control NaCl solution; and (III) by reducing the total number of particles produced overall. In addition, measurements of the absorption of ultraviolet light (λ = 224 nm) were used to quantify the concentration of SDBS in bulk water samples and aerosol extracts. We found that SDBS was significantly enriched in aerosol extracts relative to the bulk water even when the concentration of SDBS in the bulk water was below the limit of detection (LOD) of our quantitation methods. Thus, the surfactant studied will influence the production of CCN even when present in minute concentrations.
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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) | 0.001 | 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".