Secondary organic aerosol from biomass burning phenolic compounds and nitrate radicals can be highly viscous over a wide relative humidity range
Bibliographic record
Abstract
Biomass burning events, including wildfires, can emit large amounts of phenolic compounds such as guaiacol. These phenolic compounds can undergo oxidation by nitrate radicals (NO3) to form secondary organic aerosol (SOA). Viscosity and hygroscopicity are key properties that affect SOA’s role in atmospheric chemistry, air quality, and climate. However, these properties have not been quantified for SOA formed from the reaction of phenolic compounds with NO3. We used the poke-flow technique and a quartz crystal microbalance (QCM) to measure the viscosity and hygroscopicity of SOA particles generated from the reaction of NO3 with guaiacol, termed guaiacol-NO3 SOA. The viscosity of this SOA is extremely high (≳ 5×10^7 Pa s) at RH ≲ 70% and drastically higher than previously reported for other SOA types investigated with the poke-flow technique at RH ≳ 40%. The high viscosity for guaiacol-NO3 SOA can be attributed, at least in part, to the low hygroscopicity measured by the QCM. From the viscosity results, we calculated the mixing times of organic molecules within guaiacol-NO3 SOA. The results suggest that mixing times within guaiacol-NO3 SOA particles with atmospherically relevant sizes exceed 1 hour for most tropospheric conditions, with possible implications for predicting the size, mass, and long-range transport of pollutants in phenolic SOA.
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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".