Synthesis, Characterization and Stress-Testing of a Novel Quillaja Saponin Stabilized Oil-in-Water Phytocannabinoid Nanoemulsion
Bibliographic record
Abstract
This study describes the design, optimization, and stress-testing of a novel phytocannabinoid nanoemulsion generated using high-pressure homogenization. QNaturale®, a plant-derived commercial emulsifier containing quillaja saponin, was used to stabilize the lipid phase droplets in water. Optimization studies revealed that after 10 homogenization cycles at a pressure of 30,000 psi in the presence of 10 wt% QNaturale® (1.5 wt% quillaja saponin), average nanoemulsion droplet diameters were ca. 120 nm and average droplet surface zeta-potentials were ca. -30 mV for a lipid phase comprising 16.6 wt% CBD-enriched cannabis extract and 83.4 wt% carrier (soybean) oil. The optimized nanoemulsion 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 under 2 and moderately high salt concentrations (> 100 mM), however, destabilized 0the CD CBD nanoemulsion, eventually leading to phase separation. Cannabis potency, determined by HPLC, was detrimentally affected by any changes in the nanoemulsion phase stability. 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 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".