Graphene is another allotrope of carbon with two-dimensional monolayer honeycomb. Owing to its special characteristics including electrical, physical and optical properties, graphene is known as a more suitable candidate compared to other materials to be used in the sensor application. It is possible, moreover, to use biosensor by using electrolyte-gated field effect transistor based on graphene (GFET) to identify the alterations in charged lipid membrane properties. The current article aims to show how thickness and charges of a membrane electric can result in a monolayer graphene-based GFET while the emphasis is on the conductance variation. It is proposed that the thickness and electric charge of the lipid bilayer (LLP and QLP) are functions of carrier density, and to find the equation relating these suitable control parameters are introduced. Artificial neural network algorithm as well as support vector regression has also been incorporated to obtain other models for conductance characteristic. The results comparison between analytical models, artificial neural network and support vector regression with the experimental data extracted from previous work show an acceptable agreement.
Graphene is an attention-grabbing material in electronics, physics, chemistry, and even biology because of its unique properties such as high surface-area-to-volume ratio. Also, the ability of graphene-based materials to continuously tune charge carriers from holes to electrons makes them promising for biological applications, especially in lipid bilayer-based sensors. Furthermore, changes in charged lipid membrane properties can be electrically detected by a graphene-based electrolyte-gated graphene field effect transistor (GFET). In this paper, a monolayer graphene-based GFET with a focus on the conductance variation caused by membrane electric charges and thickness is studied. Monolayer graphene conductance as an electrical detection platform is suggested for neutral, negative, and positive electric-charged membrane. The electric charge and thickness of the lipid bilayer (Q LP and L LP) as a function of carrier density are proposed, and the control parameters are defined. Finally, the proposed analytical model is compared with experimental data which indicates good overall agreement.
Graphene has attracted great interest because of unique properties such as high sensitivity, high mobility, and biocompatibility. It is also known as a superior candidate for pH sensing. Graphene-based ion-sensitive field-effect transistor (ISFET) is currently getting much attention as a novel material with organic nature and ionic liquid gate that is intrinsically sensitive to pH changes. pH is an important factor in enzyme stabilities which can affect the enzymatic reaction and broaden the number of enzyme applications. More accurate and consistent results of enzymes must be optimized to realize their full potential as catalysts accordingly. In this paper, a monolayer graphene-based ISFET pH sensor is studied by simulating its electrical measurement of buffer solutions for different pH values. Electrical detection model of each pH value is suggested by conductance modelling of monolayer graphene. Hydrogen ion (H+) concentration as a function of carrier concentration is proposed, and the control parameter (Ƥ) is defined based on the electro-active ions absorbed by the surface of the graphene with different pH values. Finally, the proposed new analytical model is compared with experimental data and shows good overall agreement.