# Modeling and experimental verification of polycaprolactone nanoparticle precipitation

## TL;DR

A new model accurately predicts the size of polycaprolactone nanoparticles during their formation, improving efficiency and control for biomedical uses.

## Contribution

The model incorporates finite coalescence time, a novel factor in nanoparticle formation predictions.

## Key findings

- The model shows strong agreement with experimental nanoparticle size data.
- It improves control over size distribution and reduces aggregation in nanoparticle synthesis.
- The framework is adaptable to other polymers and formulation conditions.

## 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.

The online version contains supplementary material available at 10.1038/s41598-026-35286-y.