Production of Monodisperse Large Drop Emulsions by Means of High Internal Phase Pickering Emulsions─Processing and Formulation
Bibliographic record
Abstract
High internal phase Pickering emulsions (HIPPE) have received significant research attention in the last two decades due to their potential for a wide range of applications. The appropriate processing of such high-viscosity emulsions, hundreds of times more viscous than that of the continuous phase, and the control of the final droplet size remain challenges to be tackled. Our research targeted this knowledge gap by examining the influence of the emulsion formulation and the processing conditions on the final droplet size. The dispersed phase fraction (100 cSt silicon oil) ranged between 75 and 80%. The emulsions were produced in a regular mixing tank equipped with a helical ribbon impeller rotating at a low speed (100-150 rpm). The effective viscosity of the continuous phase was obtained from the experimental torque measurements. The droplet size distributions were measured after emulsification and dilution in the continuous phases. It is shown that the capillary number obtained from the observed emulsification performance can help predict the final droplet size. Our approach provides a straightforward methodology to generate concentrated Pickering emulsions with controlled and predictable droplet size.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".