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Efficient computational model of the in-flow capturing of magnetic nanoparticles by a cylindrical magnet for cancer nanomedicine. | LitMetric

Efficient computational model of the in-flow capturing of magnetic nanoparticles by a cylindrical magnet for cancer nanomedicine.

Phys Rev E

Institute for Computational Mechanics, Technical University of Munich, TUM School of Engineering and Design, Department of Engineering Physics & Computation, Garching bei München, 85748 Garching, Germany.

Published: June 2024

AI Article Synopsis

  • Magnetic nanoparticles show promise for enhancing cancer treatments, but many fail in trials due to transport issues in the body.
  • To tackle this problem, researchers developed a computational model to study these nanoparticles’ behavior both in the lab and within the body.
  • The study provides an analytical model for how magnetic nanoparticles interact with cylindrical magnets, which can help optimize magnet designs and improve the understanding of nanoparticle behavior in various scenarios.

Article Abstract

Magnetic nanoparticles have emerged as a promising approach to improving cancer treatment. However, many nanoparticle designs fail in clinical trials due to a lack of understanding of how to overcome the in vivo transport barriers. To address this shortcoming, we develop a computational model aimed at the study of magnetic nanoparticles in vitro and in vivo. In this paper, we present an important building block for this overall goal, namely an efficient computational model of the in-flow capture of magnetic nanoparticles by a cylindrical permanent magnet in an idealized test setup. We use a continuum approach based on the Smoluchowski advection-diffusion equation, combined with a simple approach to consider the capture at an impenetrable boundary, and derive an analytical expression for the magnetic force of a cylindrical magnet of finite length on the nanoparticles. This provides a simple and numerically efficient way to study different magnet configurations and their influence on the nanoparticle distribution in three dimensions. Such an in silico model can increase insight into the underlying physics, help to design prototypes, and serve as a precursor to more complex systems in vivo and in silico.

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Source
http://dx.doi.org/10.1103/PhysRevE.109.065309DOI Listing

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