Bio-Nano Sensor utilizing Single-Layer Graphene for the Detection of Iridovirus

Context: Graphene and its related compounds have remarkable optical, electrical, and chemical characteristics that make them suitable for biosensing. Nondestructive biological molecule identification is made possible by biosensors based on graphene and its derivatives. The field of biological sensors is expanding to meet the demand for sensitive early detection of disorders. The aim of the present investigation is to develop a sensor by analyzing the vibrational responses of single layer graphene sheets (SLGS) with attached microorganisms, specifically Iridoviridae. Graphene-based virus sensors typically rely on the interaction between the virus and the graphene surface, which lead to changes in the frequency response of graphene. This change can be measured and used to detect the presence of the virus. Its high surface-to-volume ratio and sensitivity to changes in its frequency make it a highly sensitive platform for virus detection. Methods: The atomistic finite element method (AFEM) has been used to carry out for dynamic analysis of SLG. Molecular structural analysis has been performed for single-layer graphene. Bridged and simply supported with roller support boundary conditions applied at the ends of SLG structure. Simulations have been performed to see how SLG behaves when used as sensors for biological creatures. A single-layer graphene armchair SLG (5, 5) with 50 nm length, exhibits its highest frequency vibration at 8.66 x 10 ⁶ Hz, with a mass of 1.2786 Zg. In contrast, a zigzag- SLG with a (18,0) configuration has its lowest frequency vibration at 2.82 x 10 ⁵ Hz, observed at a length of 10 nm. Finite Element Method (FEM) analysis is utilized to forecast the performance of single-layer graphene (SLG) biosensors under simply supported with roller support and bridged boundary conditions. This aids in comprehending the thresholds of detection and the influence of factors such as size, chirality, and boundary conditions on sensor effectiveness. These biosensors can be especially helpful in biological sciences and the medical field since they can considerably improve the treatment of patients, cancer early diagnosis, and pathogen identification when used in clinical environments. By simulating sensor behavior using FEM, researchers can reduce the need for costly and time-consuming experimental testing, speeding up the development process.