Influence of gas-to-particle partitioning on the hygroscopic and droplet activation behaviour of α-pinene secondary organic aerosol
Bibliographic record
Abstract
Hygroscopic properties of secondary organic aerosol (SOA) formed by photooxidation of different concentrations (10-27 or 220-270 ppb) of alpha-pinene precursor were investigated at different relative humidities (RH) using a hygroscopicity tandem differential mobility analyzer (HTDMA, RH=95-97%) and using the mobile version of the Leipzig Aerosol Cloud Interaction Simulator (LACIS-mobile, RH=98-99.3%). In addition, the cloud condensation nuclei (CCN) activity was measured applying two CCN counters (CCNC). An apparent single-hygroscopicity parameter, kappa, of approximately 0.09, approximately 0.07-0.13, and approximately 0.02-0.04 was derived from CCNC, HTDMA and LACIS data, respectively, assuming the surface tension of pure water. Closure between HTDMA and CCNC data was achieved within experimental uncertainty, whereas closure between LACIS and CCNC was only achieved by assuming a concentration-dependent surface tension reduction, consequently resulting in lower CCNC-derived kappa values. Comparing different experimental techniques at varying precursor concentrations in more detail reveals further open questions. Varying precursor concentration influences hygroscopic growth factors at subsaturated RH, while it has no effect on the CCN activation. This difference in behaviour might be caused by precursor concentration-dependent surface tension depression or changing droplet solution concentration dependence of the water activity coefficient with varying SOA composition. Furthermore, evidence was found that the SOA might need several seconds to reach the equilibrium growth factor at high RH.
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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".