An analytical approach to calculate the charge density of biofunctionalized graphene layer enhanced by artificial neural networks

Graphene, a purely two-dimensional sheet of carbon atoms, as an attractive substrate for plasmonic nanoparticles is considered because of its transparency and atomically thin nature. Additionally, its large surface area and high conductivity make this novel material an exceptional surface for studying adsorbents of diverse organic macromolecules. Although there are plenty of experimental studies in this field, the lack of analytical model is felt deeply. Comprehensive study is done to provide more information on understanding of the interaction between graphene and DNA bases. The electrostatic variations occurring upon DNA hybridization on the surface of a graphene-based field-effect DNA biosensor is modeled theoretically and analytically. To start with modeling, a liquid field effect transistor (LGFET) structure is employed as a platform, and graphene charge density variations in the framework of linear Poisson- Boltzmann theories are studied under the impact induced by the adsorption of different values of DNA concentration on its surface. At last, the artificial neural network is used for improving the curve fitting by adjusting the parameters of the proposed analytical model