Abstract A numerical model based on the diffusion equation was developed to predict the size of polycaprolactone (PCL) nanoparticles produced via nanoprecipitation. The model requires minimal input data, making it cost-effective and experimentally efficient. It accounts for both diffusion-driven growth and the finite coalescence time of particles, a factor often overlooked in nanoparticle formation. Nanoparticles were synthesized under controlled variation of polymer concentration, surfactant amount, and mixing method, including microfluidics. The model demonstrated strong agreement with experimental data, yielding higher predictive accuracy than prior diffusion-limited models. It also enabled optimization of process parameters, improving control over size distribution and reducing aggregation. The proposed framework enhances nanoprecipitation scalability and reproducibility while lowering resource consumption. Its modular structure allows adaptation to other polymers and formulation conditions. This approach offers a practical and computationally efficient tool for the rational design of polymeric nanoparticles, with broad relevance to biomedical applications, including targeted drug delivery and nanomedicine.
Rybak et al. (2026) studied this question.