We are pleased to announce the publication of our paper, “Dual-functional magnetic nanoparticles: A stochastic Langevin study of imaging-heating trade-offs”, in Physical Review B.
The study examines how the intrinsic properties of magnetic nanoparticles govern their performance in integrated magnetic particle imaging (MPI) and magnetic hyperthermia. Combining these modalities in a single platform could enable MPI to map nanoparticle distributions before treatment and guide the localized delivery of hyperthermia. However, nanoparticle properties that improve imaging do not necessarily maximize heating efficiency, creating important design trade-offs.
Using a stochastic Langevin model, the work evaluates nanoparticles with core sizes of 20, 25, and 30 nm across broad ranges of magnetic anisotropy and saturation magnetization. MPI performance is characterized through spatial resolution and signal quality, while hyperthermia performance is assessed using the specific absorption rate. Gaussian Process surrogate models and multi-objective optimization are then used to identify particle-property combinations that balance these competing imaging and heating objectives.
The results reveal clear, core-size-dependent trade-offs among MPI spatial resolution, signal-to-noise ratio, and hyperthermia efficiency. By identifying optimal magnetic-property regions for each core size, the study provides a systematic and extensible framework for designing dual-functional nanoparticles for MPI-guided magnetic hyperthermia and related theranostic applications.