Surfactant-Free Cellulose Filaments Stabilized Oil in Water Emulsions
Bibliographic record
Abstract
Abstract There has been significant interest over recent years in the production and application of sustainable and green materials. Among these, nanocellulose has incurred great interest because of its exceptional properties and wide range of potential applications, including in Pickering emulsions. However, the production cost of these cellulosic materials has limited their application. In this study, the capability of a new type of cheaper cellulosic material, cellulose filaments (CFs), in formulating stable oil in water Pickering emulsions was investigated and compared with three conventional nanocelluloses, namely cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs) and TEMPO-oxidized CNFs (TEMPO-CNFs). Results showed that CFs can provide stable surfactant-free emulsions over wide ranges of salt concentration (0 – 500 mM) and pH (2 – 10), as indicated by the near constant oil droplet size and dewatering index of the emulsions. This is due to the ability of CFs to strongly adsorb to the oil and water interface, as evidenced by visualizing labeled CFs with engineered carbohydrate-binding module (CBM2a) conjugated with green fluorescent protein (CBM2a-eGFP) under fluorescent microscopy. Compared to the emulsions stabilized by other types of nanocelluloses, the CFs-stabilized emulsion demonstrated a larger average droplet size and comparable (with CNFs) or better (than CNCs and TEMPO-CNFs) stability, which is partially attributed to the higher viscosity of continuous phase in the presence of CFs. The results of this study demonstrate the use of CFs as a novel and cheaper cellulosic material for stabilizing emulsions, which opens the door to a range of markets from the food industry to engineering 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) | 0.001 | 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".