Formulation of acoustoelectric phenomena in ionic media via the Nernst-Planck equation

Objective. Acoustoelectric (AE) phenomena, such as the acousto-electric interaction (AEI), involve the generation of electric fields induced by acoustic waves. The ultrasound-induced AEI effect holds potential for non-invasive electroanatomical mapping of excitable tissues. Prior to experimental measurement, numerical simulations are commonly employed to predict the weak AEI signals. While simulations based on the conventional AEI equation describes the effect adequately, it does not capture other concurrent AE phenomena and relies on phenomenological mapping between the acoustic and electric fields.Approach. This work aims to describe the AE phenomena reported in medical ultrasonics from the perspective of ion-mediated electrokinetics with the Nernst-Planck equation (NPE). A finite element model was developed using COMSOL^®to compute the NPE-based AE effects.Main results. The simulations revealed two distinct electric potential components: the AEI signal and additional liquid junction potentials (LJPs). The LJP component dominated in configurations approaching supply-receive lead orthogonality under weak current densities, thereby highlighting its value in predicting the complete nature of experimentally-measured AE signals. The species quotient,Q, was derived and shown to be an ion-dependent parameter determining the LJP amplitude. The dependence of the AEI and LJP signal amplitudes on the lead angle in our simulations aligned well with literature-reported experimental data. As biological tissues contain mixtures of ions, the extracellular fluid environment was simulated to demonstrate the flexibility of our model to accommodate mixed ionic compositions, mimicking the generation of AE phenomena in biological media. The technique is generalizable to 3D, and post-simulation signal-to-noise ratio analyses provided estimated tolerable noise powers for practical measurement.Significance. The proposed electrokinetics-based model computes the ion dynamics behind AE signals studied in medical ultrasonics. The model predicts the emergence of additional current-independent LJP signals, providing a more complete composition of raw AE signals induced under insonification, and has the potential to guide AE studies.