Microencapsulation of red and white thyme oil in poly(lactic‐co‐glycolic) acid: Assessment of encapsulation efficiency and antimicrobial capacity of the produced microcapsules
Bibliographic record
Abstract
Abstract In this work, microcapsules were formed by coating thyme oil with a biodegradable polymer, poly(lactic‐co‐glycolic) acid (PLGA), through a coacervation process recently developed at our laboratory and previously studied for poly lactic acid (PLA). The coacervation method involves dissolution of the polymer (PLGA 50:50) in dimethylformamide. After adding this solution to the oil/water (o/w) emulsion, and due the insolubility of the polymer in water, polymer deposition occurs around the oil droplets and microcapsule formation starts. PLGA was chosen due to its easy biodegradation and biocompatibility. The active principle, thyme oil, is characterized by excellent antimicrobial activity ascribed to the presence of thymol and carvacrol, its major components. Two types of thyme oil (red and white) were microencapsulated and the produced microcapsules were characterized using optical microscopy, particle size analysis, and gas chromatography (used to evaluate encapsulation efficiency). Antimicrobial activity was preliminarily evaluated following ASTM E2149‐01. Microscopy and particle size analysis confirmed the existence of microcapsules with round shapes, smooth surfaces, particle diameters between ∼45–49 μm, and wall thicknesses ∼3.5 μm. Global encapsulation efficiencies of thyme oil (both red and white) were 70 % and 57 %, respectively. The produced microcapsules exhibited a sustained oil release that ensures a level of antimicrobial activity maintenance desirable for cosmetic 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".