Preparation of Pickering emulsions stabilized by metal organic frameworks using oscillatory woven metal micro-screen
Bibliographic record
Abstract
Uniform Pickering emulsions stabilized by metal organic frameworks (MOFs) MIL-101 and ZIF-8 nanoparticles (NPs) were successfully prepared using an oscillatory woven metal microscreen (WMMS) emulsification system in the presence and the absence of surfactants. The effects of operating and system parameters including the frequency and amplitude of oscillation, the type of nano-particle and/or surfactant on the droplet size and coefficient of variance of the prepared emulsions are investigated. The results showed that both the hydrodynamics of the system and the hydrophobic/hydrophilic nature of the NP influenced the interfacial properties of the oil-water interface during droplet formation and after detachment, which in turn affected the final droplet size and distribution. Comparison between the measured and predicted droplet size using a simple torque balance (TB) model is discussed.
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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 | 0.000 | 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 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".