Ultrastable High Internal Phase Pickering Emulsions: Forming Mechanism, Processability, and Application in 3D Printing
Bibliographic record
Abstract
High internal phase Pickering emulsions (HIPPEs) are versatile platforms for various applications owing to their low-density, solid-like structure, and large specific surface area. Here, naturally occurring polysaccharide-protein hybrid nanoparticles (PPH NPs) were used to stabilize HIPPEs with an internal phase fraction of 80% at a PPH NP concentration of 1.5%. The obtained HIPPEs displayed a gel-like behavior with excellent stability against centrifugation (10000 g, 10 min), temperature (4–121 °C), pH (1.0–11.0), and ionic strength (0–500 mM). Confocal laser scanning microscope and cryo-scanning electron microscopy results showed that PPH NPs contributed to the stability of HIPPEs by effectively adsorbing and anchoring on the surface of the emulsion droplets layer by layer to form a dense 3D network barrier to inhibit droplet coalescence. The rheological analysis showed that the HIPPEs possessed a higher viscosity and lower frequency dependence with increasing PPH NP concentration, suggesting the potential application of such HIPPEs in three-dimensional (3D) printing, which was subsequently confirmed by a 3D printing experiment. This work provides highly stable and processable HIPPEs, which can be developed as facile and reusable materials for numerous applications. They can also be directly used for future food manufacturing, drug and nutrient delivery, and tissue reconstruction.
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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) | 0.000 | 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".