Increased UV–Visible Particle Absorption via Evaporation and Drying of Aqueous Droplets Containing Catechol/HONO Solutions
Bibliographic record
Abstract
The role of cloud droplet evaporation in brown carbon (BrC) formation is poorly established with previous studies focusing on carbonyl/(NH 4 ) 2 SO 4 solutions and secondary organic aerosols (SOA). Here, by mixing dry air into an aerosol flow reactor, we examine whether droplet evaporation may affect wildfire BrC by studying the aqueous nitration of catechol by HONO and darkening of the soluble component of wood smoke. Using online aethalometry and offline UV–vis analysis of particle filter extracts, we observe for the catechol/HONO system that droplet evaporation leads to significantly increased mass absorption coefficients and enhanced ratios of visible to UV absorption compared to bulk solutions, consistent with offline mass spectrometric analysis of dried particles that indicates aromatic nitration and oligomer formation. Differences between the aethalometer and UV–vis filter measurements are attributed to reactions on the dry filter that enhance BrC formation. Evaporation of wood smoke extract droplets also leads to darkening reactions but much less significantly than with catechol/HONO, as expected if only a select fraction of the wood smoke molecules react via concentration enhancement. These findings underscore droplet evaporation/drying as important to BrC atmospheric evolution and suggest that dryer usage may affect particle composition in field measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".