Interfacial Stabilization of Green and Food-Safe Emulsions through Complexation of Tannic Acid and Nanochitins
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Nanochitins exhibit unique structural attributes that confer distinct physical properties to multiphase systems. Amphiphilic tannic acid (TA) serves as an excellent candidate for interfacial modification via electrostatic adsorption and complexation with nanochitin. In this study, we developed green and food-safe strategies to enhance the stabilizing and functional performance of complexes formed through the coassembly of chitin nanofibers (ChNF) and TA. Their interactions were systematically investigated using spectroscopy, rheological measurements, and molecular simulations, all confirming strong interfacial binding primarily through hydrogen bonding. X-ray diffraction analysis further revealed TA-induced changes in ChNF crystallinity. The resulting ChNF–TA complexes effectively stabilized high internal phase Pickering emulsions (HIPPEs), which exhibited long-term stability and were successfully applied in direct ink writing. The exceptional stability of the HIPPEs was attributed to the synergistic effects of electrostatic charge neutralization and interfacial tension reduction. Quartz crystal microgravimetry demonstrated rapid complexation, with TA binding to ChNF thin films at a level of approximately 450 ng/cm 2 . The resulting HIPPEs remained stable for at least two months and readily formed cryogels upon freeze-drying. Owing to their enhanced stability and viscoelastic properties, HIPPEs stabilized with ChNF–TA complexes offer a promising platform for the development of sustainable emulsions, with the potential for customization in personalized food and related fields.
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.
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".