Novel extensional device to efficiently form fine <scp>oil‐in‐water</scp> food emulsions
Bibliographic record
Abstract
Abstract Two immiscible liquids are commonly mixed by mechanically dispersing one into the other to form emulsions. Surfactants or emulsifiers confer stability. Mechanical mixing, in practice, is an energy‐intensive shear flow that is ineffective when the ratio of the dispersed‐phase viscosity to the continuous‐phase viscosity exceeds about four. Extensional flows are not subject to this viscosity ratio limit. This superiority of extensional flow was exploited to fabricate a novel, continuous‐flow, cone‐shaped device with an extensional strain of eight to make fine soybean oil‐in‐water emulsions. A spherical insert having a wall clearance of 25 μm was an effective design factor. Starting with ‘coarse’ 50 wt.% oil emulsions, two stretching episodes were needed for size reduction. The temperature rise was negligible, and the results were independent of the emulsifier type employed. Increasing flow rate and stretching episodes, reducing wall clearance, enhancing emulsifier concentration, and multiple passes through the device gave progressively smaller drops; the volume‐averaged diameter became less than 2 μm, and the number‐averaged diameter reached 0.5 μm, narrowing the size distribution. The emulsions that formed had a high viscosity and were stable. The performance of a scaled‐up device was compared with other mixers. At equivalent energy density and 50 wt.% oil, drop sizes were similar for a valve homogenizer but larger for a rotor‐stator mixer. At 80 wt.% oil, the rotor stator‐mixer again required more energy for the same drop size, but emulsions prepared with the valve homogenizer broke. The findings of this study can help to design industrial‐scale energy‐efficient extensional‐flow dominant devices for the formation of food 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 | 0.000 |
| 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 | 0.000 |
| 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".