Heteroaggregation effects on Pickering stabilization using oppositely charged cellulose nanocrystal and nanochitin
Bibliographic record
Abstract
Pickering emulsions are stabilized using complexes of cellulose nanocrystals (CNC) and nanochitin (NCh). The colloidal behavior and heteroaggregation in aqueous media are studied in relation to complex formation and net charge. The complexes are remarkably effective in stabilizing oil-in-water Pickering emulsions under conditions of slightly net positive or negative charges, as determined by the CNC/NCh mass ratio. Close to charge neutrality (CNC/NCh ~5), large heteroaggregates form, resulting in unstable emulsions. By contrast, under net cationic conditions, interfacial arrest of the complexes leads to non-deformable emulsion droplets with high stability (no creaming for 9 months). At given CNC/NCh concentrations, emulsions with up to 50% oil fraction are produced. This study shows how to control emulsion properties beyond consideration of the typical formulation variables, for instance, through adjusting CNC/NCh ratio or charge stoichiometry. We highlight the possibilities that are available for emulsion stabilization by using a combination of polysaccharide nanoparticles.
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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".