Coexistence of three liquid phases in atmospheric aerosol particles
Bibliographic record
Abstract
Aerosol particles are ubiquitous in the atmosphere and play an important role for air quality and Earth’s climate. Primary organic aerosol (POA), secondary organic aerosol (SOA), and secondary inorganic aerosol (SIA) constitute a significant mass fraction of these particles. POA, SOA, and SIA can become internally mixed within the same particle though different processes such as coagulation, gas–particle partitioning. To predict the role of these internally mixed particles in climate and air quality information on their phase behaviour is needed, i.e. information on the number and type of phases present within these particles. As an example, a particle with a single homogeneous liquid phase can have different radiative properties, reaction rates, uptake kinetics, and potential to change cloud microphysical properties by activating into a cloud droplet, compared to a particle with multiple liquid or solid phases. In the current study we used Nile red, a solvatochromic dye, and fluorescence microscopy in order to determine the phase behaviour of POA+SOA+SIA particles. Squalane was used as a proxy of POA, ammonium sulfate was used as SIA and 1 of 23 different oxidized organic molecules were used as proxies of SOA. We demonstrate that three liquid phases often coexist within individual particles. We find that the phase behaviour strongly depends on the oxygen-to-carbon ratio of the SOA proxies. Experiments with SOA generated by dark ozonolysis of α-pinene in an environmental chamber are consistent with these observations. We also used thermodynamic and kinetic modelling to investigate the atmospheric implications of our experimental results.
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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 | 0.000 |
| 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".