Waterborne cellulose acetate pickering emulsion generation mediated by cellulose nanocrystals for paper coating applications
Bibliographic record
Abstract
There is a continually increasing demand for alternatives to single-use, non-degradable, and synthetic plastic packaging. Environmentally friendly paper products offer a potential solution, but typically do not meet stringent demands for barrier and other physical property performance unless coated with polymeric films, usually polyolefins. Replacing conventional plastic film coatings with bio-based polymer alternatives, such as cellulose acetate, can provide competitive barrier property performance while also providing sustainability benefits. Furthermore, water can be used as a coating medium for added environmental and coatability advantages. In this study, 10 wt% Pickering emulsions of cellulose acetate dispersed in water and stabilized via cellulose nanocrystals (CNC) were generated. Rheological behavior, particle size, stability, and particle morphology were analyzed, as was the effect of modifying the CNCs using (2-Dodecen-1-yl)succinic anhydride over several weeks. Unbleached kraft paper was coated and compared to polyolefin coated and uncoated paper. Water vapor permeability, grease resistance, water absorption, and wet mechanical properties were all investigated, displaying promising properties for barrier paper coating. The grease kit test yielded a result of 10/12 for the coated paper versus 0/12 for uncoated, and water Cobb value showed a 60 % improvement. Lastly, the performance of the coated paper as a food takeout container coating was explored, with convincing results demonstrating a strong avenue of applicability. Overall, the waterborne cellulose acetate Pickering emulsion formation and its paper coating application yielded promising results for sustainable packaging development. • Biobased polymer paper coating enhances paper packaging sustainability. • Waterborne bio-based polymer coating can be prepared post polymer production. • Cellulose nanocrystal (CNC) and its modified counterpart provided Pickering emulsions. • CNC enabled Pickering emulsion displayed excellent stability and rheological properties.
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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".