Nanocellulose-stabilized Pickering emulsions : fabrication, stabilization, and food applications
Bibliographic record
Abstract
Pickering emulsions have been widely studied due to their good stability and potential applications. Nanocellulose including cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and bacterial cellulose nanofibrils (BCNFs) has emerged as sustainable stabilizers/emulsifiers in food-related Pickering emulsions due to their favorable properties such as renewability, low toxicity, amphiphilicity, biocompatibility, and high aspect ratio. Nanocellulose can be widely obtained from different sources and extraction methods and can effectively stabilize Pickering emulsions via the irreversible adsorption onto oil-water interface. The synergistic effects of nanocellulose and other substances can further enhance the interfacial networks. The nanocellulose-based Pickering emulsions have potential food-related applications in delivery systems, food packaging materials, and fat substitutes. In this review, we highlight key fundamental work and recent reports on nanocellulose-based Pickering emulsion systems. The sources and extraction of nanocellulose and the fabrication of nanocellulose-based Pickering emulsions are briefly summarized. Furthermore, the synergistic stability and food-related applications of nanocellulose-stabilized Pickering emulsions are spotlighted.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".