Alireza Zeinalinezhad

Analytical investigations of gas-sensor using methane decomposition system

AbstractThis paper reports on a set of experiments designed to develop a workable gas sensor prototype using an electronic system with methane. The current is found to be sensitive to the presence of methane gas, which is a conduit for a variety of gas sensors. The sensitivity is shown to depend on pointed or broad electrode configurations. Scanning electron microscopy images show the area of conductance that determines the quality of the electrodes in three configurations. Data processing is performed with a support vector regression algorithm in conjunction with statistical analysis for error and quality control. The reported results can be adapted to a broad range of industrial applications for enhanced productivity, safety, innovation, data processing, and overall total quality management.

Escherichia coli bacteria detection by using graphene-based biosensor

Graphene is an allotrope of carbon with two-dimensional (2D) monolayer honeycombs. A larger detection area and higher sensitivity can be provided by graphene-based nanosenor because of its 2D structure. In addition, owing to its special characteristics, including electrical, optical and physical properties, graphene is known as a more suitable candidate compared to other materials used in the sensor application. A novel model employing a field-effect transistor structure using graphene is proposed and the current-voltage (I-V) characteristics of graphene are employed to model the sensing mechanism. This biosensor can detect Escherichia coli (E. coli) bacteria, providing high levels of sensitivity. It is observed that the graphene device experiences a drastic increase in conductance when exposed to E. coli bacteria at 0-10(5) cfu/ml concentration. The simple, fast response and high sensitivity of this nanoelectronic biosensor make it a suitable device in screening and functional studies of antibacterial drugs and an ideal high-throughput platform which can detect any pathogenic bacteria. Artificial neural network and support vector regression algorithms have also been used to provide other models for the I-V characteristic. A satisfactory agreement has been presented by comparison between the proposed models with the experimental data.

Analytical investigation of carrier concentration effect on one-dimensional graphene nanoscroll

In recent years, carbon nanoscrolls (CNSs) that have a tubular structure similar to that of the open multi-wall carbon nanotube have received significant attention. Graphene nanoscrolls (GNSs) have also attracted significant attention, owing to their unique properties. These nanoscrolls are obtained by systematically winding graphene sheets into a spiral, and are expected to have high mechanical strength, high carrier mobility, and high thermal conductivity. In the present work, an analytical model was used to determine the band energy of (16,0) GNS, and the normalized Fermi energies in the degenerate and non-degenerate regimes are modeled. The model revealed that degenerate and non-degenerate approximations can be used for normalized Fermi energies higher than 3 and lower than −3, respectively.

ISVR modeling of an interferon gamma (IFN-γ) biosensor based on graphene

Nowadays the detection of proteins plays a crucial role for the early diagnosis of diseases. The combination of biosensor application with nanotechnology has offered new alternatives for clinical diagnostic techniques. One of the major public health problems in many developing countries is tuberculosis (TB) susceptibility and Interferon-gamma (IFN-γ) can be used in the diagnosis of this infectious disease. In this study, a prototype graphene based FET structure was employed as a biosensor. Additionally, a PDMS layer was deployed beneath the graphene as a dielectric layer. As a result of the changeability of Ids (drain–source current), the carrier concentration would change when the IFN-γ molecules attach to the surface of graphene. To acquire another pattern for the I–V (current–voltage characteristic), the Incremental Support Vector Regression (ISVR) algorithm was also employed. The comparative study based on the outcomes of the ISVR and pre-existing analytical models with experimental data found that there was acceptable agreement, which was able to substantiate the proposed models. Moreover, the ISVR showed that the proposed method remarkably improved the accuracy of prediction.

Detection of bilayer lipid with graphene nanoribbon

Single-layer graphene consists of sp2-bonded carbon atoms arranged in a two-dimensional (2D) hexagonal lattice comprising a thin layer of single carbon atoms. Owing to its special characteristics including electrical, physical, and optical properties, graphene is considered more suitable for sensor applications than other materials. Moreover, it is possible to produce biosensors using electrolyte-gated field-effect transistors based on graphene (GFETs) to identify the alterations in charged lipid membrane properties. This paper illustrates how membrane thickness and electrical charge can result in a monolayer GFET, with emphasis on conductance variation. It is proposed that the thickness and electrical charge of the lipid bilayer are functions of carrier density, and equations relating these suitable control parameters were derived. Adaptive neuro fuzzy inference system (ANFIS) has been incorporated to obtain other model for conductance characteristic. The comparison between the analytical models and ANFIS with the experimental data extracted from previous work show an acceptable agreement.