A review of high internal phase Pickering emulsions: Stabilization, rheology, and 3D printing application
Bibliographic record
Abstract
High internal phase Pickering emulsion (HIPPE) is renowned for its exceptionally high-volume fraction of internal phase, leading to flocculated yet deformed emulsion droplets and unique rheological behaviors such as shear-thinning property, viscoelasticity, and thixotropic recovery. Alongside the inherent features of regular emulsion systems, such as large interfacial area and well-mixture of two immiscible liquids, the HIPPEs have been emerging as building blocks to construct three-dimensional (3D) scaffolds with customized structures and programmable functions using an extrusion-based 3D printing technique, making 3D-printed HIPPE-based scaffolds attract widespread interest from various fields such as food science, biotechnology, environmental science, and energy transfer. Herein, the recent advances in preparing suitable HIPPEs as 3D printing inks for various applied fields are reviewed. This work begins with the stabilization mechanism of HIPPEs, followed by introducing the origin of their distinctive rheological behaviors and strategies to adjust the rheological behaviors to prepare more eligible HIPPEs as printing inks. Then, the compatibility between extrusion-based 3D printing and HIPPEs as building blocks was discussed, followed by a summary of the potential applications using 3D-printed HIPPE-based scaffolds. Finally, limitations and future perspectives on preparing HIPPE-based materials using extrusion-based 3D printing were presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".