Modeling and experimental verification of polycaprolactone nanoparticle precipitation

Area of Science:

• Materials Science
• Chemical Engineering
• Biomedical Engineering

Background:

• Nanoparticle synthesis via nanoprecipitation is crucial for drug delivery.
• Accurate prediction and control of nanoparticle size are essential for reproducible results.
• Existing models often overlook particle coalescence, limiting predictive accuracy.

Purpose of the Study:

• To develop a cost-effective and experimentally efficient numerical model for predicting polycaprolactone (PCL) nanoparticle size.
• To incorporate diffusion-driven growth and finite coalescence time into the model.
• To enable rational design and optimization of nanoprecipitation processes.

Main Methods:

• Developed a numerical model based on the diffusion equation.
• Synthesized PCL nanoparticles varying polymer concentration, surfactant amount, and mixing methods (including microfluidics).
• Validated model predictions against experimental data.

Main Results:

• The model showed strong agreement with experimental data.
• Achieved higher predictive accuracy compared to previous diffusion-limited models.
• Successfully optimized process parameters, enhancing size control and reducing aggregation.

Conclusions:

• The developed model offers a practical and computationally efficient tool for designing polymeric nanoparticles.
• The framework improves nanoprecipitation scalability, reproducibility, and reduces resource consumption.
• The model is adaptable to other polymers and formulations, with broad applications in nanomedicine and drug delivery.