Molecular Insights into Gas–Particle Partitioning and Viscosity of Atmospheric Brown Carbon
Bibliographic record
Abstract
Biomass burning organic aerosol (BBOA), containing brown carbon chromophores, plays a critical role in atmospheric chemistry and climate forcing. However, the effects of evaporation on BBOA volatility and viscosity under different environmental conditions remain poorly understood. This study focuses on the molecular characterization of laboratory-generated BBOA proxies from wood pyrolysis emissions. The initial mixture, “pyrolysis oil (PO 1 )”, was progressively evaporated to produce more concentrated mixtures (PO 1.33, PO 2, and PO 3 ) with volume reduction factors of 1.33, 2, and 3, respectively. Chemical speciation and volatility were investigated using temperature-programmed desorption combined with direct analysis in real-time ionization and high-resolution mass spectrometry (TPD-DART-HRMS). This novel approach quantified saturation vapor pressures and enthalpies of individual species, enabling the construction of volatility basis set distributions and the quantification of gas–particle partitioning. Viscosity estimates, validated by poke-flow experiments, showed a significant increase with evaporation, slowing particle-phase diffusion and extending equilibration times. These findings suggest that highly viscous tar ball particles in aged biomass burning emissions form as semivolatile components evaporate. The study highlights the importance of evaporation processes in shaping BBOA properties, underscoring the need to incorporate these factors into atmospheric models for better predictions of BBOA aging and its environmental impact.
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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 | 0.001 |
| 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".