Preparation and characterization of tea tree oil-β-cyclodextrin microcapsules with super-high encapsulation efficiency
Bibliographic record
Abstract
This study aimed to prepare tea tree oil-β-cyclodextrin microcapsules using an optimized co-precipitated method. The impact of the volume fraction of ethanol in the solvent system for microencapsulation on encapsulation efficiency was investigated and analyzed sophisticatedly. Super-high encapsulation efficiency was achieved when a 40% volume fraction of ethanol was used for the microencapsulation procedure, where the recovery yield of microcapsules and the embedding fraction of tea tree oil in microcapsules were as high as 88.3% and 94.3%, respectively. Additionally, considering the operation cost, including time and energy consumption, an economical preparation was validated so that it would be viable for large-scale production. Based on the results of morphological and X-ray diffraction analysis, the crystal structure appeared to differ before and after microencapsulation. The results of gas chromatography-mass spectrometry and Fourier transform infrared spectroscopy confirmed the successful formation of microcapsules. Furthermore, the antibacterial activity of the fabricated microcapsules was assessed by a simple growth inhibition test using Bacillus subtilis as the study object, and the hydrophilic property was proved by a water contact angle measurement.
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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".