Development and evaluation of drug-loaded niosomes fabricated by flow chemistry: A novel vortex tube reactor approach
Bibliographic record
Abstract
Continuous flow technology has been increasingly applied in the fabrication of nanoparticle drug delivery systems due to its ability to provide enhanced process control, scalability, and improved uniformity in particle size. Therefore, this study aims to utilize a newly designed flow chemistry vortex tube reactor for the preparation of ketoconazole-loaded niosomes, with the goal of enhancing mixing efficiency and increasing production rates. The experiment was designed using a central composite design to investigate the effects of key preparation parameters, including total flow rate, surfactant concentration, and cholesterol content, on particle size, size distribution, zeta potential, entrapment efficiency, and drug loading percentage. The optimized formulation (Span 80 = 25 mg, Cholesterol = 50 mg) was achieved using a total flow rate of 20 mL/min. The resulting niosomes exhibited a particle size of 212.3 nm, a zeta potential of 40.2 mV, a polydispersity index of 0.282, an entrapment efficiency of 50.84 %, a drug loading of 0.58 %, and a productivity of 70.67 mg/min. Moreover, ketoconazole-loaded niosomes prepared using the newly designed flow chemistry vortex tube reactor demonstrated prolonged inhibition of Candida albicans growth compared to ketoconazole solution. A comparison with batch synthesis revealed that flow chemistry produces smaller particles with a narrower size distribution and significantly improved productivity. These findings indicate the potential for further development of the vortex reactor for industrial-scale production of nanovesicular drug delivery systems.
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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".