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Updated: Feb 16, 2026
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Flash NanoPrecipitation for the Encapsulation of Hydrophobic and Hydrophilic Compounds in Polymeric Nanoparticles
Published on: January 7, 2019
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Finding key nanoprecipitation variables for achieving uniform polymeric nanoparticles using neurofuzzy logic
Miguel O Jara1, Johanna Catalan-Figueroa1, Mariana Landin2
1Department of Pharmaceutical Science and Technology, School of Chemical and Pharmaceutical Sciences, University of Chile, Santos Dumont 964, 4to piso, Of. 09, Independencia, 8380494, Santiago, Chile.
Drug Delivery and Translational Research
|December 31, 2017
Summary
This study optimized nanoprecipitation for producing uniform polymeric nanoparticles (Np). Artificial neural networks identified key variables like mixing time and turbulence, enabling single-step, monomodal NpERS production for drug delivery.
Area of Science:
- Materials Science
- Chemical Engineering
- Nanotechnology
Background:
- Nanoprecipitation is a rapid method for polymeric nanoparticle (Np) synthesis.
- Current methods often require post-production filtration, introducing variability.
- Achieving monodisperse Np in a single step is crucial for reproducible applications.
Purpose of the Study:
- To systematically study and optimize the nanoprecipitation method for uniform Np production.
- To identify key process variables and their interactions influencing Np characteristics.
- To develop a predictive model for NpERS synthesis using artificial neural networks.
Main Methods:
- Development of a database for artificial neural network (ANN) modeling.
- Systematic investigation of nanoprecipitation parameters using ANN.
- Synthesis of polymethacrylate derivative nanoparticles (NpERS).
Main Results:
- ANN model demonstrated high predictability (>70%) for NpERS characteristics (size, PDI, zeta potential).
- Identified mixing time and turbulence as critical for polymer supersaturation and monomodal Np formation.
- Polymer concentration and solvent type were found to control NpERS properties.
Conclusions:
- Optimized nanoprecipitation method yields uniform NpERS in a single step.
- ANN-based predictive model aids in understanding and controlling NpERS production.
- This optimized method has potential for scalable polymeric nanoparticle production in biological and drug delivery.
Keywords:
Artificial neural networksHomogeneous nanoparticlesMixing timeNanoparticle productionNanoprecipitationPolymeric nanoparticlesMore Related Videos
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