A review of high internal phase Pickering emulsions: Stabilization, rheology, and 3D printing application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it