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Computational-Aided Approach for the Optimization of Microfluidic-Based Nanoparticles Manufacturing Process.

Marco Bellotti1, Enrica Chiesa2, Bice Conti2

  • 1Department of Civil Engineering and Architecture, Università degli Studi di Pavia, Via Ferrata 3, Pavia, Italy. marco.bellotti01@universitadipavia.it.

Annals of Biomedical Engineering
|August 4, 2024
PubMed
Summary
This summary is machine-generated.

Microfluidic nanoparticle (NP) production offers efficiency and control. This study introduces a computational fluid dynamics pipeline to optimize NP manufacturing by predicting formation regions and size heterogeneity, reducing experimental costs.

Keywords:
ManufacturingMicrofluidicsNanoparticlesNumerical simulation

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Area of Science:

  • * Nanotechnology and Materials Science
  • * Chemical Engineering
  • * Pharmaceutical Sciences

Background:

  • * Microfluidic production of nanoparticles (NPs) presents advantages over conventional methods like nanoprecipitation, offering higher efficiency, lower variability, and better control.
  • * Optimizing NP formulations for microfluidic platforms currently demands extensive, costly, and time-consuming experimental trials.
  • * Bridging the gap between microfluidic NP production and desired characteristics requires advanced predictive tools.

Purpose of the Study:

  • * To develop and validate a computational fluid dynamics (CFD) pipeline for simulating microfluidic solvent-anti-solvent mixing.
  • * To introduce novel variables for assessing nanoparticle formation regions and precipitation dynamics under varying hydraulic conditions.
  • * To computationally predict and experimentally validate the impact of flow rate ratios on NP size and heterogeneity.

Main Methods:

  • * Development of a CFD pipeline to model the mixing dynamics of acetonitrile (solvent) and TRIS-HCl (anti-solvent).
  • * Validation of the CFD model using a specifically designed experimental setup.
  • * Introduction and analysis of new parameters to characterize the NP precipitation zone and its response to flow conditions.
  • * Correlation of numerical predictions with experimental results on NP size and heterogeneity.

Main Results:

  • * The numerical approach accurately predicted a reduction in the nanoprecipitation region with increased flow rate ratio.
  • * Experimental validation confirmed that higher flow rate ratios lead to increased NP size heterogeneity.
  • * The CFD pipeline effectively captured the relationship between fluid dynamics and NP formation characteristics.

Conclusions:

  • * The developed CFD pipeline is a powerful tool for optimizing microfluidic nanoparticle manufacturing processes.
  • * Computational modeling can significantly reduce the experimental burden in developing new NP formulations.
  • * This approach enables better control over NP size and morphology, leading to more efficient production.