Towards Accurate Size Predictions of Magnetic Nanoparticles Using Support Vector Regression

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Abstract
This study explores the size of magnetic nanoparticles (MNP) for applications in Magnetic Resonance Imaging (MRI) and Magnetic Particle Imaging (MPI). Emphasizing the critical role of MNP size on their response to alternating magnetic fields, the study unveils a regression model to optimize MNP synthesis towards tailored sizes of MNP. With a limited and broadly distributed data set at hand, the feasibility of building an accurate predictive model based on Support Vector Machines is shown. Integrating such a model into a continuous synthesis setup establishes a feedback loop, enabling real-time control and adaptation of synthesis parameters.

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