Coexistence of three liquid phases in individual atmospheric aerosol particles
Bibliographic record
Abstract
Individual atmospheric particles can contain mixtures of primary organic aerosol (POA), secondary organic aerosol (SOA), and secondary inorganic aerosol (SIA). To predict the role of such complex multicomponent particles in air quality and climate, information on the number and types of phases present in the particles is needed. However, the phase behavior of such particles has not been studied in the laboratory, and as a result, remains poorly constrained. Here, we show that POA+SOA+SIA particles can contain three distinct liquid phases: a low-polarity organic-rich phase, a higher-polarity organic-rich phase, and an aqueous inorganic-rich phase. Based on our results, when the elemental oxygen-to-carbon (O:C) ratio of the SOA is less than 0.8, three liquid phases can coexist within the same particle over a wide relative humidity range. In contrast, when the O:C ratio of the SOA is greater than 0.8, three phases will not form. We also demonstrate, using thermodynamic and kinetic modeling, that the presence of three liquid phases in such particles impacts their equilibration timescale with the surrounding gas phase. Three phases will likely also impact their ability to act as nuclei for liquid cloud droplets, the reactivity of these particles, and the mechanism of SOA formation and growth in the atmosphere. These observations provide fundamental information necessary for improved predictions of air quality and aerosol indirect effects on climate.
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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 | 0.001 | 0.001 |
| 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".