Formation of cloud condensation nuclei by oxidative processing: Unsaturated fatty acids
Bibliographic record
Abstract
The ability of submicron oleic acid and linoleic acid particles, or condensation nuclei (CN), to act as cloud condensation nuclei (CCN) has been investigated using a tandem differential mobility analyzer (TDMA) coupled to a flow tube reactor and a thermal gradient diffusion chamber (TGDC). The size change and CCN properties of pure oleic acid, mixed oleic acid/methanol, and pure linoleic acid particles have been investigated as a function of exposure to ozone. Pure oleic and linoleic acid particles were CCN inactive for all particle diameters (≤300 nm) and supersaturations (≤1%) studied. The mixed oleic acid/methanol particles, however, had a critical activation diameter of 188 nm for an experimental water supersaturation of 0.6%. Under low ozone exposures (<1 × 10 −4 atm s), both the oleic acid and linoleic acid particles decreased in size. In particular, oleic acid particles lost 25% of their initial volume, consistent with the loss of nonanal, a volatile reaction product. However, no increase in CCN activity was observed at these exposures. Under conditions of much higher ozone exposure, e.g., 0.42 atm s, the pure oleic acid particles became CCN active, with a critical activation diameter of 161 nm at 0.6% supersaturation. CCN activity for the linoleic acid particles was never observed, even under these high ozone exposures not typically observed in the atmosphere. By contrast, the mixed oleic acid/methanol particles showed enhanced activation under atmospherically relevant ozone exposures (<1 × 10 −4 atm s). These results suggest that the products of the ozone plus unsaturated fatty acid reaction do promote the CCN activity of the particles; however, the degree of activity is dependent on both the level of ozone exposure and the chemical nature of the particle. These results are the first to demonstrate that the CCN properties of pure organic aerosols can be modified through oxidative processing.
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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".