Relative Humidity Dependence of Soot Aggregate Restructuring Induced by Secondary Organic Aerosol: Effects of Water on Coating Viscosity and Surface Tension
Bibliographic record
Abstract
Soot aggregates have a significant warming effect on climate, and their structural and optical properties may evolve in the presence of coatings. Here, the relative humidity (RH) dependence of soot aggregate restructuring induced by secondary organic aerosol (SOA) coatings was investigated in a series of photo-oxidation experiments. Burner-generated soot aggregates were classified by mobility diameter and injected into a smog chamber, where they were exposed to oxidation products of p -xylene; coated aggregates were subsequently conditioned at one of the following RHs: <12%, 20%, 40%, 60%, or 85%. Changes in diameter and mass were monitored using differential mobility and centrifugal particle mass analyzers, respectively. At RH < 12%, the SOA coating was too viscous to induce restructuring, so the particle diameter increased uniformly with coating mass. At RH ≥ 20%, the SOA coating induced restructuring, and the degree of restructuring increased with RH, indicating that the decreased viscosity and increased surface tension of SOA have significant implications on SOA-induced restructuring of soot aggregates. At RH ≥ 60%, appreciable water uptake occurred, and the hygroscopicity parameter of the SOA coating was derived. Our results provide crucial insights into the complex interactions between soot, SOA, and water in the atmosphere.
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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".