Simulation of Chemical Engineering Memristive Biosensor

TL;DR

This paper presents a simulation approach for a low-power memristive biosensor designed for biological analyte detection, emphasizing high sensitivity and potential AI integration for portable biosensing applications.

Contribution

It introduces a dynamic memristive model for simulating nanosensing, enabling development of low-cost, high-sensitivity biosensors for efficient biological detection.

Findings

Validated the memristive model's suitability for chemical sensing

Demonstrated potential for AI integration in biosensing

Supported development of portable low-power biosensors

Abstract

This paper introduces a perspective approach for simulating a memristive sensor tailored for low power biological analyte detection. The necessity for such innovation stems from the increasing demand for efficient biosensing technologies that can operate with minimal poxer consumption. Within this study, a numerical dynamic memristive model serves as a basis platform for implementing enhanced nanosensing method characterized by low cost and high sensitivity. Numerous simulations were conducted to validate the suitability of the dynamic memristive model's behaviour for emulating a chemical sensing approach. The simulated data is collected for deploying an AI application to ensure an advanced predictable biosensing intake function. All in all, this work paves the way for developing compact numerical models of memristive biosensors, addressing the pressing need for portable lox poxer consumption biosensing solutions.