Liquid–liquid phase separation in particles containing secondary organic material free of inorganic salts
Bibliographic record
Abstract
Abstract. Particles containing secondary organic material (SOM) are ubiquitous in the atmosphere and play a role in climate and air quality. Recently, research has shown that liquid–liquid phase separation (LLPS) occurs at high relative humidity (RH) (greater than ∼ 95 %) in α-pinene-derived SOM particles free of inorganic salts, while LLPS does not occur in isoprene-derived SOM particles free of inorganic salts. We expand on these findings by investigating LLPS at 290 ± 1 K in SOM particles free of inorganic salts produced from ozonolysis of β-caryophyllene, ozonolysis of limonene, and photo-oxidation of toluene. LLPS was observed at greater than ∼ 95 % RH in the biogenic SOM particles derived from β-caryophyllene and limonene while LLPS was not observed in the anthropogenic SOM particles derived from toluene. This work combined with the earlier work on LLPS in SOM particles free of inorganic salts suggests that the occurrence of LLPS in SOM particles free of inorganic salts is related to the oxygen-to-carbon elemental ratio (O : C) of the organic material. These results help explain the difference between the hygroscopic parameter κ of SOM particles measured above and below water saturation in the laboratory and field, and have implications for predicting the cloud condensation nucleation properties of SOM particles.
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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".