Comparing microfluidics and ultrasonication as formulation methods for developing hempseed oil nanoemulsions for oral delivery applications
Why this work is in the frame
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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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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 it