Synthesis and Characterization of β-Cyclodextrin-Essential Oil Inclusion Complexes for Tick Repellent Development
Bibliographic record
Abstract
Essential oils (EOs) are used in several pest management applications. Due to their volatility, EOs may experience bioactivity reduction, thus requiring protection to extend their properties. In the present study, we investigated the inclusion complex formation (IC) of β-cyclodextrin (β-CD) with selected EOs with known tick repellent activity using two co-precipitation methods. ICs were characterized by evaluating EO mass concentration and inclusion efficiency (% IE) and other instrumental methods. Co-precipitation method 2 yielded the highest EO mass concentration (88 ± 6 μg/mg β-CD) for the 1:1 molar ratio geranium Egyptian EO IC. The EO volatile release over time from the ICs was investigated by headspace SPME/GC-MS analysis. ICs were also tested in tick repellency bioassays. ICs reported significant tick repellent activity, with lemongrass IC performing best overall. Method 1 showed the best combination of high mass concentration EO, controlled volatile release, and tick repellency with lemongrass EO. The results demonstrated that β-CD had selectively encapsulated different EOs. Moreover, the formation of ICs may improve EO tick repellent properties protecting the active ingredients and providing a better, long-lasting repellent action. These findings will allow the development of more effective naturally derived repellent products to protect individuals from tick bites and prevent tick-borne illnesses.
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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) | 0.001 | 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".