Predictions of diffusion rates of organic molecules in secondary organic aerosols using the Stokes-Einstein and fractional Stokes-Einstein relations
Bibliographic record
Abstract
Abstract. Information on the rate of diffusion of organic molecules within secondary organic aerosol (SOA) is needed to accurately predict the effects of SOA on climate and air quality. Often, researchers have predicted diffusion rates of organic molecules within SOA using measurements of viscosity and the Stokes-Einstein relation (D ∝ 1/η where D is the diffusion coefficient and η is viscosity). However, the accuracy of this relation for predicting diffusion in SOA remains uncertain. We measured diffusion coefficients over eight orders in magnitude in proxies of SOA including citric acid, sorbitol, and a sucrose-citric acid mixture. These results were combined with literature data to evaluate the Stokes-Einstein relation for predicting diffusion of organic molecules in SOA. Although almost all the data agrees with the Stokes-Einstein relation within a factor of ten, a fractional Stokes-Einstein relation (D ∝ C/ηt) with t = 0.93 and C = 1.66 is a better model for predicting diffusion of organic molecules in the SOA proxies studied. In addition, based on the output from a chemical transport model, the Stokes-Einstein relation can over predict mixing times of organic molecules within SOA by as much as one order of magnitude at an altitude ~ 3 km, compared to the fractional Stokes-Einstein relation with t = 0.93 and C = 1.66. These differences can be important for predicting growth, evaporation, and reaction rates of SOA in the middle and upper part of the troposphere. These results also have implications for other areas where diffusion of organic molecules within organic-water matrices is important.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".