Bibliographic record
Abstract
Abstract. Atmospheric aerosol particles span orders of magnitude in size. In ultrafine particles, the energetic contributions of surfaces and interfaces to the Gibbs energy become significant and increase in importance as particle diameter decreases. For these particles, the thermodynamic equilibrium state depends on size, composition, and temperature. Various aerosol systems have been observed to undergo liquid–liquid phase separation (LLPS), impacting equilibrium gas–particle partitioning, modifying physicochemical properties of the particle phases, and influencing cloud droplet activation. Numerous laboratory experiments have characterized the onset relative humidity of LLPS in larger aerosol particles and macroscopic bulk systems. However, in sufficiently small particles, the interfacial tension between two liquid phases constitutes an energetic barrier that may prevent the formation of an additional liquid phase. Determining said small-size limit is a key question. We introduce a predictive droplet model based on the Aerosol Inorganic–Organic Mixtures Functional groups Activity Coefficients model. This model enables size-dependent computations of surface and interfacial tension effects on bulk–surface partitioning within phase-separated and single-phase particles. We evaluate four approaches for computing interfacial tension in multicomponent droplets, including a new method introduced in this work. Of the approaches tested, Antonov's rule best matches observed liquid–liquid interfacial tensions in highly immiscible mixtures, while a modified Butler equation fits well in more miscible systems. We find that two approaches substantially lower the onset relative humidity of LLPS for the studied systems.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.009 |
| Insufficient payload (model declined to judge) | 0.319 | 0.243 |
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".