Rational Design, Synthesis, and Characterization of a Solid Δ9-Tetrahydrocannabinol Nanoformulation Suitable for “Microdosing” Applications
Bibliographic record
Abstract
Background: This article highlights the formulation of a solid Δ 9 -tetrahydrocannabinol (THC)-loaded ingestible prepared from pure THC distillate. Methods: A THC-containing ethanol-assisted cannabinoid nanoemulsion (EACNE) was created using a solvent displacement technique. Subsequently, the EACNE was converted to a solid powdery material while still retaining its THC potency, a format uniquely suited for “microdosing” applications. Results: EACNE had an average lipid droplet size of ∼190 nm, with a polydispersity index of 0.15, and an average droplet ζ potential of −49±10 mV. The nanoemulsion (NE) was colloidally stable for at least 6 weeks, with no meaningful change in cannabinoid potency over the experimental period, as determined by high-performance liquid chromatography analysis. The EACNE remained stable when subjected to physical stresses such as heat, freeze/thaw cycles, carbonation, dilution to beverage concentrations, high sucrose concentrations, and a pH range between 5 and 8. The microencapsulated EACNE demonstrated limited free-flowing behavior but was freely redispersible in water without any visible phase separation. Conclusions: We report the design, creation, and characterization of a THC NE generated without the use of specialized equipment, such as a microfluidizer or a high-pressure homogenizer. This emulsion could readily be converted to a water-redispersible powder. This embodiment is particularly suited for THC “microdosing,” a practice that might decouple the health benefits of THC from its psychotropic effects.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".