Pickering Emulsions Based on Polysaccharides and Biocompatible Particles: A Review of Bio Application, Stabilization and Application
Bibliographic record
Abstract
Abstract Pickering emulsions (PEs), stabilized by solid particles rather than traditional surfactants, are gaining significant attention due to their superior stability, biocompatibility, and reduced toxicity. This review provides a comprehensive overview of PEs stabilized by Abstract- polysaccharides and other biocompatible particles, focusing on their formation, stabilization mechanisms, and diverse applications. We explore various methods for PE preparation, including rotor-stator homogenization, high-pressure homogenization, and probe sonication. The critical role of particle characteristics—such as size, wettability, surface charge, and concentration—in dictating emulsion type (O/W or W/O) and stability is examined. The review highlights the use of a wide range of natural biopolymers, including cellulose derivatives (nanocrystals and nanofibrils), chitosan, starch, and proteins like zein, as effective stabilizers. Furthermore, it delves into the expanding applications of these biocompatible PEs in the food industry for active packaging and delivery of bioactive compounds, in pharmaceuticals for topical and transdermal drug delivery, and in cosmetics. While PEs offer considerable advantages, challenges related to production scalability, regulatory approval (e.g., GRAS status for nanocellulose), and long-term performance remain. Future research should focus on optimizing particle modification, exploring novel biopolymer sources, and conducting thorough in vivo studies to fully realize the potential of these advanced colloidal systems
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".