Identification of microbes using single-layer graphene-based nano biosensors.

Abstract

Graphene based nano sensors have huge potential in an era of sensor technology. The objective of this study is to create a sensor by investigating the vibration responses of cantilever and bridged boundary conditioned single layer graphene sheets (SLGS) with various attached microorganisms on the tip and at the centre of the sheet. The Parvoviridae, Flaviviridae, and Polyomaviridae biological substances have been comprehensively investigated here. For the Parvoviridae, Polyomaviridae, and Flaviviridae categories of targeted microbes, the sizes are 21nm, 40nm, and 45nm, respectively. The Parvoviridae family has a maximum frequency of 1.87x107 Hz with a cantilever condition and a mass of 4.2441 Zg, and for a bridged condition, it demonstrates a maximum frequency of 1.23x108 Hz with the same mass on armchair SLG (5 5). The data analysis shows that 3.0041 Zg mass of the Mimivirus has the lowest frequency. It demonstrates explicitly that the rate of frequency decreases as the value of mass increases. When compared to chiral SLG, the armchair single layer graphene sheet performs better. The research indicates that the dynamic properties are significantly influenced by the mass of various biological organisms. The application of this sensor will enable the detection of microorganisms or viruses that can be connected to SLG. In this research, the application of Single Layer Graphene (SLG) as a virus sensing device is explored. Atomistic finite element method (AFEM) has been used to carry out the dynamic analysis of SLG. Molecular dynamic analysis and simulations have been performed to see how SLG behaves when employed as sensors for biological entities and when they are exposed to bridged and cantilever boundary conditions. The frequency analysis was performed using ANSYS APDL software. SLG of various chirality has been utilised in the investigation. By altering the applied mass of a biological object, the difference in frequency observed. The idea behind mass detection employing nano biosensors is built on the concept that the stiffness of a biomolecule changes as its mass changes, making the resonant frequency extremely sensitive to that change. A shift in the resonance frequency results from a change in the associated mass on the graphene sheet. The main challenge in mass detection is estimating the variation in resonant frequency driven by the mass of the connected molecule. The SLG-based biosensor has a specific application in the early identification of diseases. The biosensor investigated in this article is novel, whereas the biosensors that are presently on the market operate using the ionization method. The simulations result shows SLG based biosensor's sensitivity considerably faster than an existing one.