Dried and Redispersible Cellulose Nanocrystal Pickering Emulsions
Bibliographic record
Abstract
The effect of tannic acid (TA) and water-soluble cellulose derivatives on the properties of Pickering emulsions stabilized by cellulose nanocrystals (CNCs) was investigated. The potential to both fully dry CNC-stabilized emulsions and to redisperse the dried emulsions in water is demonstrated. When CNCs are mixed with excess adsorbing polymer, either methyl cellulose or hydroxyethyl cellulose, followed by emulsification with corn oil, oil-in-water emulsions can be transformed without oil leakage into solid dry emulsions via freeze-drying. However, these dry emulsions exhibit droplet coalescence within the solid matrix and thus cannot be redispersed. Addition of TA (after emulsification) imparts dispersibility to the dried emulsions due to complexation between the cellulose derivatives and TA which condenses the "shell" around the oil droplets. When dried emulsions with TA are placed in water, the emulsion droplets redisperse readily without the need for high energy mixing, and minimal change in emulsion droplet size is observed by Mastersizer and confocal microscopy. Therefore, the simple addition of two sustainable components to CNC Pickering emulsions (i.e., TA and methyl cellulose or hydroxyethyl cellulose) has led to the first dried and redispersible CNC-based emulsions with oil content as high as 94 wt %. These processing abilities will likely extend the use of these surfactant-free, "green", and potentially edible emulsions to new food, cosmetic, and pharmaceutical 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 | 0.001 |
| 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".