ELECTROSPINNING OF ST. JOHN'S WORT OIL LOADEDMICROCAPSULES BASED PVA NANOFIBERS
Bibliographic record
Abstract
In this study, it was achieved that the production of St. John's Wort oil loadad Eudragit RS 100/PVA microcapsules by emulsion/solvent evaporation method and the microcapsules were embedded in PVA nanofibers. Morphological analysis was carried out with SEM images of both microcapsules and nanofibers. The presence of St. John's Wort oil, PVA and Eudragit RS 100 polymers were confirmed in the chemical structure of microcapsules and nanofibers by FT-IR. According to experimental studies, microcapsules were produced to have a smooth surface, a spherical shape and a uniform particle size. The PVA concentration was kept constant at 10% wt and microcapsule concentrations were applied as 1, 3, 5, 7, and 9 wt %. Then, polymer solution properties were measured, such as conductivity, viscosity, and surface tension. It was determined that viscosity and surface tension values increased with microcapsule concentration increase, while conductivity did not change significantly. Nanofiber production was realized via the electrospinning method under the optimum process parameters. According to the SEM images and histogram, nanowebs have a fine fiber diameter, smooth surface, high quality and no bead structure. In addition, the average microcapsule size is 30 μm, average fiber diameter is 430 nm and the fiber diameter uniformity coefficient is 1,014. It is thought that this nanofiber surface containing microcapsules embedded in St. John's Wort has the potential to be used as a wound dressing.
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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".