Encapsulation and controlled release of vitamin C in modified cellulose nanocrystal/chitosan nanocapsules
Bibliographic record
Abstract
Vitamin C (VC), widely used in food, pharmaceutical and cosmetic products, is susceptible to degradation, and new formulations are necessary to maintain its stability. To address this challenge, VC encapsulation was achieved via electrostatic interaction with glycidyltrimethylammonium chloride (GTMAC)-chitosan (GCh) followed by cross-linking with phosphorylated-cellulose nanocrystals (PCNC) to form VC-GCh-PCNC nanocapsules. The particle size, surface charge, degradation, encapsulation efficiency, cumulative release, free-radical scavenging assay, and antibacterial test were quantified. Additionally, a simulated human gastrointestinal environment was used to assess the efficacy of the encapsulated VC under physiological conditions. Both VC loaded, GCh-PCNC, and GCh-Sodium tripolyphosphate (TPP) nanocapsules were spherical with a diameter of 450 ± 8 and 428 ± 6 nm respectively. VC-GCh-PCNC displayed a higher encapsulation efficiency of 90.3 ± 0.42% and a sustained release over 14 days. The release profiles were fitted to the first-order and Higuchi kinetic models with R2 values greater than 0.95. VC-GCh-PCNC possessed broad-spectrum antibacterial activity with a minimum inhibition concentration (MIC) of 8–16 μg/mL. These results highlight that modified CNC-based nano-formulations can preserve, protect and control the release of active compounds with improved antioxidant and antibacterial properties for food and nutraceutical 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) | 0.000 | 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".