Liquid–liquid phase separation and morphologies in organic particles consisting of <i>α</i> -pinene and <i>β</i> -caryophyllene ozonolysis products and mixtures with commercially available organic compounds
Bibliographic record
Abstract
Liquid–liquid phase separation (LLPS) in organic aerosol particles can impact several properties of atmospheric particulate matter, such as cloud condensation nuclei (CCN) properties, optical properties, and gas-to-particle partitioning. Yet, our understanding of LLPS in organic aerosols is far from complete. Here, we report on the LLPS of one-component and two-component organic particles consisting of α -pinene- and β -caryophyllene-derived ozonolysis products and commercially available organic compounds of relevance to atmospheric organic particles. In the experiments involving single-component organic particles, LLPS was observed in 8 out of 11 particle types studied. LLPS almost always occurred when the oxygen-to-carbon elemental ratio (O:C) was ≤0.44 but did not occur when O:C was >0.44. The phase separation occurred by spinodal decomposition as well as the nucleation and growth mechanism, and when LLPS occurred, two liquid phases coexisted up to ∼100 % relative humidity (RH). In the experiments involving two-component organic particles, LLPS was observed in 23 out of 25 particles types studied. LLPS almost always occurred when the average was O:C ≤0.67 but never occurred when the average O:C was >0.67. The phase separation occurred by spinodal decomposition as well as the nucleation and growth mechanism. When LLPS occurred, two liquid phases coexisted up to ∼100 % RH. These results provide further evidence that LLPS is likely a frequent occurrence in organic aerosol particles in the troposphere, even in the absence of inorganic salts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".