Liquid-liquid phase separation and viscosity in biomass burning organic aerosol and climatic impacts
Bibliographic record
Abstract
Smoke particles generated by burning biomass consist mainly of organic aerosol, referred to as biomass-burning organic aerosol (BBOA). BBOA influences the climate by scattering and absorbing solar radiation or acting as nuclei for cloud formation. The viscosity and the phase behavior (i.e. the number and type of phases present in a particle) are properties of BBOA that are expected to impact several climate-relevant processes but remain highly uncertain. We studied the phase behavior of BBOA using fluorescence microscopy, and showed that BBOA particles comprise two organic phases (a hydrophobic and a hydrophilic phase) across a wide range of atmospheric relative humidity (RH). We determined the viscosity of the two phases using a photobleaching method, and showed that the two phases possess different RH-dependent viscosities. The viscosity of the hydrophobic phase is largely independent of the RH from 0 to 95%. For temperatures less than 230 K, the hydrophobic phase is glassy (viscosity > 1012 Pa s) at RHs below 95%, with possible implications for heterogeneous reaction kinetics and cloud formation in the atmosphere. Using a kinetic multi-layer model (KM-GAP), we investigated the effect of two phases on the atmospheric lifetime of brown carbon within BBOA, which is a climate-warming agent. We showed that the presence of two phases can increase the lifetime of brown carbon in the planetary boundary layer and polar regions compared to previous modelling studies. Hence, liquid-liquid phase separation can lead to an increase in the predicted warming effect of BBOA 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 | 0.001 |
| 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) | 0.000 | 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".