Speaker
Description
In the pursuit of advanced brain-machine interfaces, the development of non-invasive techniques for stimulating neurons has garnered substantial interest. One form of such a neural stimulator is magnetoelectric nanoparticles (MENPs). A MENP made up of two-phase, spherical structure of cobalt ferrite (core) and barium titanate (shell) can convert an input magnetic field to an output electric polarization which can be utilized to selectively stimulate neurons. This energy conversion relies on the property of magnetostriction exhibited by cobalt ferrite and piezoelectricity of barium titanate. These properties are in turn a function of other material properties such as elastic constants, magnetostriction, piezoelectricity etc. of the constituent phases. Therefore, in order to quantify the magnetic to electric energy i.e. the magnetoelectric co-efficient of a MENP, it is important to determine these material properties. However, classical experimental methods often fall short when investigating these materials at the nanoscale as they are generally tailored to bulk properties or their exists difficulties in preparing nanoscale samples.
To address this challenge, we employed Density Functional Theory (DFT) simulations using Quantum Espresso to probe the fundamental characteristics of ME nanoparticles, in particular the elastic constants of the individual phases, which are crucial for understanding the mechanical behaviour of nanoparticles and the evaluation of piezoelectric constants for barium titanate. The knowledge gained through these computational insights and supplemented by experimentally obtainable nanoscale parameters allowed us to inform a Finite Element Analysis (FEA) Model for the simulation of a MENP under an applied magnetic field. To validate our findings, we evaluated the stimulation from the generated electric polarization of the MENPS against in-vivo experimental data.
We used this methodology of combining DFT calculated properties with finite element analysis to study two other types of nanoscale neural stimulators: magnetomechanical ion channel opening (Gregurec, D., et al., ACS Nano, 2020), and piezoelectric electrical stimulation (Ciofani, G., et al., ACS Nano, 2010), using magnetic and ultrasound input carrier signals, respectively.
We envision our method as a foundation for the straightforward evaluation in the field of novel neural stimulation technologies, thus fostering the advancement of nanoscale devices and their potential applications in brain-machine interfaces.
