Hierarchical Electrochemical Modeling and Simulation of Bio-Hybrid Interfaces

TL;DR

This paper develops a hierarchical set of mathematical models using PDEs and ODEs to simulate biophysical phenomena at bio-hybrid interfaces, enabling detailed analysis of ion transport and electric potential in electrolyte systems connecting biological cells and solid-state devices.

Contribution

It introduces a novel hierarchy of PDE/ODE models with advanced numerical methods for simulating bio-electronic interfaces, bridging detailed PDE models and simplified lumped parameter approaches.

Findings

Simulation results agree with analytical solutions and experimental data.

The models accurately capture ion concentration and electric potential dynamics.

Numerical methods effectively solve complex PDE/ODE systems.

Abstract

In this article we propose and investigate a hierarchy of mathematical models based on partial differential equations (PDE) and ordinary differential equations (ODE) for the simulation of the biophysical phenomena occurring in the electrolyte fluid that connects a biological component (a single cell or a system of cells) and a solid-state device (a single silicon transistor or an array of transistors). The three members of the hierarchy, ordered by decreasing complexity, are: (i) a 3D Poisson-Nernst-Planck (PNP) PDE system for ion concentrations and electric potential; (ii) a 2D reduced PNP system for the same dependent variables as in (i); (iii) a 2D area-contact PDE system for electric potential coupled with a system of ODEs for ion concentrations. The backward Euler method is adopted for temporal semi-discretization and a fixed-point iteration based on Gummel's map is used to decouple system equations. Spatial discretization is performed using piecewise linear triangular finite elements stabilized via edge-based exponential fitting. Extensively conducted simulation results are in excellent agreement with existing analytical solutions of the PNP problem in radial coordinates and experimental and simulated data using simplified lumped parameter models.