Chitosan-Based Conventional and Pickering Emulsions with Long-Term Stability
Bibliographic record
Abstract
Chitosan-based conventional and Pickering oil-in-water (O/W) emulsions with very fine droplet size (volume average diameter, dv, as low as 1.7 μm) and long-term stability (up to 5 months) were ultrasonically generated at different pH values without the addition of any surfactant or cross-linking agent. The ultrasonication treatment was found to break and disperse chitosan agglomerates effectively (particularly at pH ≥ 4.5) and also reduce the chitosan molecular weight, benefiting its emulsification properties. The emulsion stability and emulsion type could be controlled by chitosan solution pH. Increasing pH from 3.5 to 5.5 led to the formation of conventional emulsions with decreasing droplet size (dv from 14 to 2.1 μm) and increasing emulsion stability (from a few days to 2 months). These results can be explained by the increase of dynamic interfacial pressure, which results from the conformation transition of chitosan molecules from an extended state to a more flexible structure as pH increases. At pH = 6.5 (the acid dissociation constant (pKa) of chitosan), the chitosan molecules self-assembled into well-dispersed nanoparticles (dv = 82.1 nm) with the assistance of ultrasonication, which resulted in a Pickering emulsion with the smallest droplet size (dv = 1.7 μm) and highest long-term stability (up to 5 months) because of the presence of chitosan solid nanoparticles at the oil/water interface. The key originality of this study is the elucidation of the role of pH in the formation of conventional and Pickering chitosan-based O/W emulsions with the assistance of ultrasonication. Our results suggest that chitosan possesses great potential to be used as an effective pH-controlled emulsifier and stabilizer without the need of other additives.
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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) | 0.000 | 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 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".