Images reveal that atmospheric particles can undergo liquid–liquid phase separations
Bibliographic record
Abstract
A large fraction of submicron atmospheric aerosol particles contains both organic material and inorganic salts. As the relative humidity cycles in the atmosphere and the water content of the particles correspondingly changes, these mixed particles can undergo a range of phase transitions, possibly including liquid-liquid phase separation. If liquid-liquid phase separation occurs, the gas-particle partitioning of atmospheric semivolatile organic compounds, the scattering and absorption of solar radiation, and the reactive uptake of gas species on atmospheric particles may be affected, with important implications for climate predictions. The actual occurrence of liquid-liquid phase separation within individual atmospheric particles has been considered uncertain, in large part because of the absence of observations for real-world samples. Here, using optical and fluorescence microscopy, we present images that show the coexistence of two noncrystalline phases for real-world samples collected on multiple days in Atlanta, GA as well as for laboratory-generated samples under simulated atmospheric conditions. These results reveal that atmospheric particles can undergo liquid-liquid phase separations. To explore the implications of these findings, we carried out simulations of the Atlanta urban environment and found that liquid-liquid phase separation can result in increased concentrations of gas-phase NO(3) and N(2)O(5) due to decreased particle uptake of N(2)O(5).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".