Physical Characterization of Drug Loaded Microcapsules and Controlled In Vitro Release Study
Bibliographic record
Abstract
Microencapsulation of model drug, acetylsalicylic acid into bio-based polymer, alginate-pectin matrix and chitosan had been undertaken in this work to characterize the microcapsules based on their composition.Alginate-pectin with the proportion of 40:60 solution was prepared with 0.3 g of drug.This mix was homogenized and atomized using nitrogen gas into 1.0M calcium chloride solution to form sol-gel microcapsules.0.3 g concentration of the drug solution was prepared with 2% glacial acetic acid and 2 g of chitosan was mixed into that and homogenized using sonicator.Microcapsules were prepared after crosslinking the drug encapsulated chitosan using glutaraldehyde.Drug loaded microcapsules were dried using microwave energy under vacuum at low temperature.Scanning electron microscopy graphs showed that microcapsules have porous and rough surfaces.Fourier Transform Infrared Spectroscopy analysis on the microcapsules confirmed the presence of drug in the polymer matrix.X-ray diffraction pattern showed that the microstructure was more like an amorphous pattern.Drug release of the microcapsules was tested in three different pH levels of 1.2, 7.4 and 8.2.Slow and controlled release of drug was observed at all the pH levels.Over all, the drug release percentage was higher in acidic pH and lower in alkali pH.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".