Functionalized Cellulose Nanocrystals with Enhanced Mucoadhesive Properties
Bibliographic record
Abstract
Salmon farms across Canada face millions of dollars in losses every year due to sea lice infestations, which is a cause for growing concern. The current treatment methods are limited and have adverse effects on the aquatic ecosystem. Sustainable mucoadhesive drug delivery systems offer a viable alternative to existing treatments. The primary goal of the work presented in this thesis was to fabricate cellulose nanocrystal-based mucoadhesive materials for targeted delivery to fish mucosal membranes. \nCellulose nanocrystals (CNCs) can interact with mucin glycoproteins via hydrogen bonding. However, their mucoadhesive properties are weak compared to other well-known mucoadhesives. CNCs were modified with natural compounds such as tannic acid (CNC-TA) and catechol (CPC-cat) to enhance their mucoadhesive capabilities. The fabricated nanomaterials were colloidally stable at pH 7 and had small particle sizes ranging from 200 to 300 nm. Turbidity titrations and rheological measurements revealed that the modified CNCs had stronger interactions with mucin compared to pristine CNCs. Modification with tannic acid introduced additional functional groups for hydrogen bond formation, resulting in a slight (2.5-fold) increase in the relative viscosity compared to CNCs. CPC-cat nanoparticles displayed the strongest mucoadhesion, with a 60-fold enhancement in the relative viscosity, which was attributed to electrostatic interactions and possible covalent bond formation. The enhanced mucoadhesive capabilities of these materials show great promise for sustainable drug delivery practices in aquaculture.
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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".