Viscosity and physical state of sucrose/ammonium sulfate/H2O droplets
Bibliographic record
Abstract
To explore aerosol chemistry and climate change, information of the physical state of aerosol particles is essential. Herein, we measured viscosities of binary mixtures of sucrose/H2O and ammonium sulfate (AS)/H2O, and ternary mixtures of sucrose/AS/H2O with different organic-to-inorganic dry mass ratios. For sucrose droplets, the viscosity gradually enhanced from ~4 × 10-1 to > ~1 × 108 Pa‧s as the relative humidity (RH) decreased from ~81% to ~24%. This corresponds from liquid to semisolid or solid state. For AS droplets, the viscosity dramatically enhanced at ~ 50% RH upon dehydration; to be < 102 Pa‧s for RH > ~50% (liquid state), and > ~1 × 1012 Pa‧s for RH ≤ ~50% (solid state). In case of the ternary mixtures, remarkable enhancement in viscosity was observed as the inorganic ratio increased at a given RH. All particles studied in this work were observed to exist as a liquid, semi-solid or solid depending on the organic-to-inorganic dry mass ratios and RH. Moreover, the measured viscosities of the binary and ternary mixtures were compared with the calculated viscosities using the Aerosol Inorganic–Organic Mixtures Functional groups Activity Coefficients Viscosity model (AIOMFAC-VISC) predictions with the Zdanovskii–Stokes–Robinson (ZSR)-style organic–inorganic mixing model. It showed excellent model–measurement agreement. The result will be discussed.
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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 | 0.001 |
| 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".