Comparing microfluidics and ultrasonication as formulation methods for developing hempseed oil nanoemulsions for oral delivery applications
Bibliographic record
Abstract
Emerging formulation technologies aimed to produce nanoemulsions with improved characteristics, such as stability are attractive endeavors; however, comparisons between competing technologies are lacking. In this study, two formulation techniques that employed ultrasound and microfluidic approaches, respectively, were examined for relative capacity to produce serviceable oil in water nanoemulsions, based on hempseed oil (HSO). The ultrasound method reached > 99.5% entrapment efficiency with nanoemulsions that had an average droplet size (Z-Ave) < 180 nm and polydispersity index (PDI) of 0.15 ± 0.04. Surfactant concentration (% w/v) was found to be a significant factor (p < 0.05) controlling the Z-Ave, PDI and zeta potential of these nanoparticles. On the other hand, the microfluidic approach produced smaller particles compared to ultrasonication, with good stability observed during storage at room temperature. The Z-Ave of < 62.0 nm was achieved for microfluidic nanoemulsions by adjusting the aqueous : organic flow rate ratio and total flow rate at 4:1 and 12 mL/min, respectively. Further analyses including a morphology examination, a simulated gastrointestinal release behavior study, transepithelial transport evaluations and a toxicity test, using a Caco2-cell model, were performed to assess the functionality of the prepared formulations. The results of this study conclude that both approaches of ultrasound and microfluidics have the capability to prepare an HSO-nanoemulsion formulation, with acceptable characteristics and stability for oral delivery applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".