Effect of varying experimental conditions on the viscosity of <i>α</i> -pinene derived secondary organic material
Bibliographic record
Abstract
Abstract. Knowledge of the viscosity of particles containing secondary organic material (SOM) is useful for predicting reaction rates and diffusion in SOM particles. In this study we investigate the viscosity of SOM particles as a function of relative humidity and SOM particle mass concentration, during SOM synthesis. The SOM was generated via the ozonolysis of α-pinene at < 5 % relative humidity (RH). Experiments were carried out using the poke-and-flow technique, which measures the experimental flow time (τexp, flow) of SOM after poking the material with a needle. In the first set of experiments, we show that τexp, flow increased by a factor of 3600 as the RH increased from < 0.5 RH to 50 % RH, for SOM with a production mass concentration of 121 µg m−3. Based on simulations, the viscosities of the particles were between 6 × 105 and 5 × 107 Pa s at < 0.5 % RH and between 3 × 102 and 9 × 103 Pa s at 50 % RH. In the second set of experiments we show that under dry conditions τexp, flow decreased by a factor of 45 as the production mass concentration increased from 121 to 14 000 µg m−3. From simulations of the poke-and-flow experiments, the viscosity of SOM with a production mass concentration of 14 000 µg m−3 was determined to be between 4 × 104 and 1.5 × 106 Pa s compared to between 6 × 105 and 5 × 107 Pa s for SOM with a production mass concentration of 121 µg m−3. The results can be rationalized by a dependence of the chemical composition of SOM on production conditions. These results emphasize the shifting characteristics of SOM, not just with RH and precursor type, but also with the production conditions, and suggest that production mass concentration and the RH at which the viscosity was determined should be considered both when comparing laboratory results and when extrapolating these results to 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 | 0.001 |
| 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 | 0.001 |
| 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".