Issue 17, 2024

Improving MPI and hyperthermia performance of superparamagnetic iron oxide nanoparticles through fractional factorial design of experiments

Abstract

Superparamagnetic iron oxide nanoparticles (SPIONs) are widely used for biomedical applications, including magnetic particle imaging (MPI) and magnetic hyperthermia. The co-precipitation method is one of the most common synthetic routes to obtain SPIONs, since it is simple and does not require extreme conditions, such as high temperatures. Despite its prevalence, however, the co-precipitation synthesis presents some challenges, most notably the high batch-to-batch variability, as multiple factors can influence nanoparticle growth. In this study, we utilized a fractional factorial design of experiments to identify key factors influencing SPION growth, properties, and performance in MPI and magnetic hyperthermia, namely Fe3+ content, pH, temperature, stirring, and atmosphere. Notably, our study unveiled secondary interactions, particularly between temperature and Fe3+ content, as well as pH and Fe3+ content, for which simultaneous changes of both parameters promoted greater effects than the sum of each factor effect alone, emphasizing the impact of synergistic effects on SPION growth and performance. These findings provide a deeper understanding of the growth mechanism of SPIONs, reconcile discrepancies in the existing literature, and underscore the importance of characterizing secondary interactions to improve the performance of SPIONs for biomedical applications.

Graphical abstract: Improving MPI and hyperthermia performance of superparamagnetic iron oxide nanoparticles through fractional factorial design of experiments

Supplementary files

Article information

Article type
Communication
Submitted
06 May 2024
Accepted
17 Jul 2024
First published
23 Jul 2024
This article is Open Access
Creative Commons BY license

Nanoscale Adv., 2024,6, 4352-4359

Improving MPI and hyperthermia performance of superparamagnetic iron oxide nanoparticles through fractional factorial design of experiments

Y. Li, R. Zhang, R. Barmin, E. Rama, M. Schoenen, F. Schrank, V. Schulz, I. Slabu, F. Kiessling, T. Lammers and R. M. Pallares, Nanoscale Adv., 2024, 6, 4352 DOI: 10.1039/D4NA00378K

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