Exploring Memristive Biosensing Dynamics: A COMSOL Multiphysics Approach

TL;DR

This study develops a COMSOL-based modeling approach for memristive biosensors, analyzing their performance metrics and integrating AI for enhanced classification accuracy, thereby advancing biosensing technology.

Contribution

It introduces a novel COMSOL modeling methodology for memristive biosensing and demonstrates AI integration with high classification accuracy.

Findings

Simulation data effectively trains SVM classifier with 97% accuracy.

Insights into antigen-antibody binding and resistance variations.

AI integration enhances biosensing performance.

Abstract

This paper presents a novel methodology for modeling memristive biosensing within COMSOL Multiphysics, focusing on critical performance metrics such as antigen-antibody binding concentration and output resistive states. By studying the impact of increasing inlet concentrations, insights into binding concentration curve and output resistance variations are uncovered. The resultant simulation data effectively trains a support vector machine classifier (SVMC), achieving a remarkable accuracy rate of 97%. The incorporation of artificial intelligence (AI) through SVM demonstrates promising strides in advancing AI-based memristive biosensing modeling, potentially elevating their performance standards and applicability across diverse domains.