Resonant core-shell magnetoelectric nanoparticles as sensors of neural magnetic activity: a computational study

Measuring the weak magnetic fields generated by spontaneous biological activity, such as those produced in the brain or heart, offers complementary information to conventional electrophysiological techniques, as electroencephalography and electrocardiography. Nevertheless, the widespread clinical use of biomagnetic sensing is hindered by the bulky and costly technology currently available, including SQUID-based and optically pumped magnetometers. In recent years magnetoelectric materials have been explored as highly sensitive, room-temperature magnetic field sensors, offering a compelling alternative to conventional approaches. Here, we investigate the feasibility of using resonant magnetoelectric nanoparticles (MENPs) as nanoscale magnetic sensors by exploiting the delta-E effect, in which magnetic-field-induced changes in elastic properties, i.e. Young's modulus, shift the nanoparticle's resonance frequency. Using a computational modeling approach, we first developed and characterized a core-shell MENP model. We then identified its natural resonance frequencies in the GHz range, evaluated its sensitivity to external magnetic field variations, and determined the optimal bias static magnetic field and core radius for maximum sensitivity. Finally, we assessed the performance of the optimized nanoparticle in detecting neural-level magnetic fields. Our simulations demonstrate that MENP can achieve a maximum sensitivity of 2.59 Hz/nT for a core diameter of 50 nm under a bias static magnetic field of 1000 Oe. These findings highlight both the feasibility of exploiting the delta-E effect in MENPs and the tunability of their structural parameters, which could be tailored for specific applications. In conclusion, this work set the theoretical groundwork for the development of cutting-edge, wireless and non-invasive nanoscale magnetic sensors for neural interfacing and biomedical signals sensing.