Optimization of the Nanoparticle Yield and Physicochemical Properties by a Structured Study of Nanoprecipitation Process Parameters

High Resolution Image Download MS PowerPoint Slide Nanoprecipitation is a widely used and highly effective method for synthesizing polymer nanoparticles, especially in pharmaceutical and biomedical applications, due to its reproducibility and ease of use. However, controlling the nanoparticle yield, size, size distribution, and surface charge remains a significant challenge. To overcome these challenges and optimize the nanoprecipitation method, it is critical to understand the effects of process parameters on the yield and physicochemical properties of nanoparticles. To address these challenges, we systematically investigated five critical process parameters: surfactant (Pluronic F-127) concentration, stirring speed, solvent evaporation duration, organic-phase flow rate, and ultrasonication time. These parameters were optimized to enhance the yield of poly( d, l -lactic- co -glycolic acid) (PLGA) nanoparticles while maintaining controlled physicochemical properties. Twenty different process conditions were evaluated for the yield, hydrodynamic size, polydispersity index (PDI), and ζ-potentials. Further, empirical modeling using a nonlinear polynomial fit was performed to fit the reaction yield and physicochemical properties. Empirical second- and third-order polynomial models described ( R 2 > 0.90 for most parameters) the process parameter response relationships with the reaction yield, size, PDI, and ζ-potential sufficiently well. Finally, we identified that process condition PC10 (7.5 h solvent evaporation, 1500 rpm stirring speed, 0.01 g/mL Pluronic F-127 (PF-127), 0.3 mL/min organic-phase flow rate, no ultrasonication) achieved a yield of 90.35%, a 9-fold improvement over the conventional method (10.06 ± 0.67%). This work provides a quantitative framework for rational PLGA nanoparticle production, enabling high-yield, monodisperse, and stable formulations, and supporting future translational pipelines.