High Internal Phase Oil-in-Water Pickering Emulsions Stabilized by Chitin Nanofibrils: 3D Structuring and Solid Foam
Bibliographic record
Abstract
Chitin nanofibrils (NCh, ∼10 nm lateral size) were produced under conditions that were less severe compared to those for other biomass-derived nanomaterials and used to formulate high internal phase Pickering emulsions (HIPPEs). Pre-emulsification followed by continuous oil feeding facilitated a "scaffold" with high elasticity, which arrested droplet mobility and coarsening, achieving edible oil-in-water emulsions with internal phase volume fraction as high as 88%. The high stabilization ability of rodlike NCh originated from the restricted coarsening, droplet breakage and coalescence upon emulsion formation. This was the result of (a) irreversible adsorption at the interface (wettability measurements by the captive bubble method) and (b) structuring in highly interconnected fibrillar networks in the continuous phase (rheology, cryo-SEM, and fluorescent microscopies). Because the surface energy of NCh can be tailored by pH (protonation of surface amino groups), emulsion formation was found to be pH-dependent. Emulsions produced at pH from 3 to 5 were most stable (at least for 3 weeks). Although at a higher pH NCh was dispersible and the three-phase contact angle indicated better interfacial wettability to the oil phase, the lower interdroplet repulsion caused coarsening at high oil loading. We further show the existence of a trade-off between NCh axial aspect and minimum NCh concentration to stabilize 88% oil-in-water HIPPEs: only 0.038 wt % (based on emulsion mass) NCh of high axial aspect was required compared to 0.064 wt % for the shorter one. The as-produced HIPPEs were easily textured by taking advantage of their elastic behavior and resilience to compositional changes. Hence, chitin-based HIPPEs were demonstrated as emulgel inks suitable for 3D printing (millimeter definition) via direct ink writing, e.g., for edible functional foods and ultralight solid foams displaying highly interconnected pores and for potential cell culturing applications.
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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) | 0.000 | 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 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".