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Formulation of paracetamol−loaded poly(ε−caprolactone) nanoparticles by anti-solvent displacement method using glass capillary microfluidics: Statistical optimization and characterization
Encapsulation of paracetamol (PCM) onto poly(ɛ-caprolactone) (PCL) polymer matrix by anti-solvent displacement method was investigated using a glass capillary microfluidics. Five (5) independent parameters involved PCL concentration (A), microfluidics orifice size (B), flow rate ratios (C), surfactant concentration (D) and paracetamol percentage loading (E) were investigated. The responses were percentage of encapsulation efficiency (Y1), % drug loading (Y2) and particle mean size, Zave (Y3). The variables were screened with the aid of a 25-full factorial design. An analysis of variance (ANOVA) was utilized to examine the optimized conditions for Ace-loaded PCL nanoparticles (NPs) formulation. The physicochemical properties of PCM-PCL NPs were evaluated using particle size analyser, field emission gun scanning electron microscope (FEG-SEM), differential scanning calorimetry and X-ray diffractometry. The optimum formulation was successfully attained at 5.96 mg.ml−1 of PCL (A), 60 µm orifice size (B), Qaq/Qor (C) of 10, 0.1 (% w/w) PVP surfactant (D) and 20 % (w/w) of paracetamol loading (E). The PCM-PCL NPs were produced at 268 nm mean size with 49.25 % encapsulation efficiency and 6.96 % of drug loading, which statistically in agreement to the predicted results. A spherical shape of PCM-PCL NPs were well acquired from FEG-SEM images.
- Aeronautical, Automotive, Chemical and Materials Engineering
- Chemical Engineering
Published inAIP Conference Proceedings
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Publisher statementThis article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Rahimah Othman, Goran T. Vladisavljević, and Zoltan K. Nagy , "Formulation of paracetamol−loaded poly(ε−caprolactone) nanoparticles by anti-solvent displacement method using glass capillary microfluidics: Statistical optimization and characterization", AIP Conference Proceedings 2541, 050008 (2022) https://doi.org/10.1063/5.0117360 and may be found at https://doi.org/10.1063/5.0117360.