Multiscale Modeling of Magnetoelectric Nanoparticles for the Analysis of Spatially Selective Neural Stimulation

The growing field of nanoscale neural stimulators offers a potential alternative to larger scale electrodes for brain stimulation. Nanoelectrodes made of magnetoelectric nanoparticles (MENPs) can provide an alternative to invasive electrodes for brain stimulation via magnetic-to-electric signal transduction. However, the magnetoelectric effect is a complex phenomenon and challenging to probe experimentally. Consequently, quantifying the stimulation voltage provided by MENPs is difficult, hindering precise regulation and control of neural stimulation and limiting their practical implementation as wireless nanoelectrodes. The work herein develops an approach to determine the stimulation voltage for MENPs in a finite element analysis (FEA) model. This model is informed by atomistic material properties from ab initio Density Functional Theory (DFT) calculations and supplemented by experimentally obtainable nanoscale parameters. This process overcomes the need for experimentally inaccessible characteristics for magnetoelectricity, and offers insights into the effect of the more manageable variables, such as the driving magnetic field. The model's voltage is compared to in vivo experimental data to assess its validity. With this, a predictable and controllable stimulation is simulated by MENPs, computationally substantiating their spatial selectivity. This work proposes a generalizable and accessible method for evaluating the stimulation capability of magnetoelectric nanostructures, facilitating their realization as wireless neural stimulators in the future.