Modeling of Sedimentation and Creaming in Suspensions and Pickering Emulsions
Bibliographic record
Abstract
Suspensions and emulsions are prone to kinetic instabilities of sedimentation and creaming, wherein the suspended particles and droplets fall or rise through a matrix fluid. It is important to understand and quantify sedimentation and creaming in such dispersed systems as they affect the shelf-life of products manufactured in the form of suspensions and emulsions. In this article, the unhindered and hindered settling/creaming behaviors of conventional emulsions and suspensions are first reviewed briefly. The available experimental data on settling/creaming of concentrated emulsions and suspensions are interpreted in terms of the drift flux theory. Modeling and simulation of nanoparticle-stabilized Pickering emulsions are carried out next. The presence of nanoparticles at the oil/water interface has a strong influence on the creaming/sedimentation behaviors of single droplets and swarm of droplets. Simulation results clearly demonstrate the strong influence of three-phase contact angle of nanoparticles present at the oil/water interface. This is the first definitive study dealing with modeling and simulation of unhindered and hindered creaming and sedimentation behaviors of nanoparticle-stabilized Pickering emulsions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".