Synthesis, characterization and stress-testing of a robust quillaja saponin stabilized oil-in-water phytocannabinoid nanoemulsion
Bibliographic record
Abstract
BACKGROUND: This study describes the design, optimization, and stress-testing of a novel phytocannabinoid nanoemulsion generated using high-pressure homogenization. [Formula: see text], a plant-derived commercial emulsifier containing quillaja saponin, was used to stabilize the lipid phase droplets in water. Stress-testing was performed on this nanoemulsion in order to evaluate its chemical and colloidal stability under the influence of different environmental factors, encompassing both physical and chemical stressors. METHODS: Extensive optimization studies were conducted to arrive at an ideal nanoemulsion formulation. A coarse emulsion containing 16.6 wt% CBD-enriched cannabis distillate and 83.4 wt% carrier (soybean) oil dispersed in 10 wt% [Formula: see text] (1.5 wt% quillaja saponin) solution after 10 homogenization cycles at a pressure of 30,000 psi produced a stable nanoemulsion. This nanoemulsion was then subjected to the stress studies. RESULTS: The optimized nanoemulsion had an average droplet diameter of ca. 120 nm and average droplet surface ζ potentials of ca. -30 mV. It was imaged and characterized by a variety of protocols. It proved to be stable to droplet agglomeration and phase separation upon storage under ambient conditions for 6 weeks, as well as under a variety of physical stressors such as heat, cold, dilution, and carbonation. pH values ≤2 and moderately high salt concentrations (> 100 mM), however, destabilized the nanoemulsion, eventually leading to phase separation. Cannabis potency, determined by HPLC, was detrimentally affected by any changes in the nanoemulsion phase stability. CONCLUSIONS: Quillaja saponin stabilized cannabidiol(CBD)-enriched nanoemulsions are stable, robust systems even at low emulsifier concentrations, and are therefore significant from both a scientific as well as a commercial perspective.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".