Alginate and Chitosan Functionalization for Micronutrient Encapsulation
Bibliographic record
Abstract
A new method for encapsulation of micronutrients was successfully developed. The encapsulation matrix consisted of two polymers (alginate and chitosan), which were functionalized by acylation with palmitoyl chloride. The structural modifications of polymers were confirmed by Fourier transform infrared (FTIR) spectroscopy. Beads were formed by ionic gelation, and their mechanical and physical characteristics (puncture strength and deformation, viscoelasticity, water vapor permeability, and rate of gel swelling) were evaluated using beads or films made of bead-forming solutions. Functionalization increased elasticity and water impermeability of polymer films. Stability of selected encapsulated micronutrients (ferrous fumarate, ascorbic acid, and beta-carotene) was also evaluated under two levels of temperature (23 and 45 degrees C) and relative humidity (56 and 100%) for 6 months. Encapsulation strongly increased the stability of micronutrients. No difference was observed in the encapsulated micronutrients' stability between nonfunctionalized and functionalized beads. Finally, a release study in gastrointestinal media was conducted. Results showed that beads were not susceptible to enzymatic and acidic attacks during stomach transit. This research demonstrates the potential of a new encapsulation method to protect bioactive molecules from temperature, moisture, and acidic conditions.
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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) | 0.001 | 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 | 0.001 | 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".