Organic Surfactants Protect Dissolved Aerosol Components against Heterogeneous Oxidation
Bibliographic record
Abstract
Oxidative aging alters the composition of organic aerosols over time, in turn affecting the ability of aerosols to seed cloud formation and scatter solar radiation. Here we explore the heterogeneous photooxidation of model organic particles with and without a soluble surfactant coating. Tricarballylic acid (TCA), a proxy for α-pinene oxidation products, serves as a representative small organic solute. Sodium dodecyl sulfate (SDS) was selected as the representative soluble surfactant because its surface properties have been extensively characterized. A flow reactor and aerosol mass spectrometer were used to determine the second-order reaction rate constant ( k = (1.9 ± 0.1) × 10 –11 cm 3 molecule –1 s –1 ) and reactive uptake coefficient (γ = 3.0) for the heterogeneous photooxidation of uncoated TCA particles by gas-phase OH radicals; such a high uptake coefficient implicates radical chain reactions in the oxidation mechanism. SDS dramatically slows the disappearance of TCA: when the SDS concentration approaches monolayer coverage, the rate of reaction of TCA with OH decreases by ∼60% relative to the rate in the absence of SDS. These results indicate that small concentrations of surface-active molecules on atmospheric particles can protect organic solutes in the bulk from oxidative aging. This effect extends the environmental lifetime of dissolved pollutants.
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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".