Aesculus hippocastanum L. as a Stabilizer in Hemp Seed Oil Nanoemulsions for Potential Biomedical and Food Applications
Bibliographic record
Abstract
Nanoemulsion systems receive a significant amount of interest nowadays due to their promising potential in biomedicine and food technology. Using a two-step process, we produced a series of nanoemulsion systems with different concentrations of hemp seed oil (HSO) stabilized with Aesculus hippocastanum L. extract (AHE). Water and commercially-available low-concentrated hyaluronic acid (HA) were used as the liquid phase. Stability tests, including an emulsifying index (EI), and droplet size distribution tests performed by dynamic light scattering (DLS) proved the beneficial impact of AHE on the emulsion’s stability. After 7 days of storage, the EI for the water-based system was found to be around 100%, unlike the HA systems. The highest stability was achieved by an emulsion containing 5% HSO and 2 g/L AHE in water, as well as the HA solution. In order to obtain the detailed characteristics of the emulsions, UV-Vis and FTIR spectra were recorded, and the viscosity of the samples was determined. Finally, a visible microscopic analysis was used for the homogeneity evaluation of the samples, and was compared with the DLS results of the water system emulsion, which showed a desirable stability. The presented results demonstrate the possible use of oil emulsions based on a plant extract rich in saponins, such as AHE. Furthermore, it was found that the anti-inflammatory properties of AHE provide opportunities for the development of new emulsion formulations with health benefits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".