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Analytical investigation of bilayer lipid biosensor based on graphene

Akbari E, Buntat Z, Shahraki E, Parvaz R, Kiani MJ

J Biomater Appl, 2016 Jan;30(6):677-85.
PMID: 26024896 DOI: 10.1177/0885328215585682

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.
Fulltext A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique

Nilashi M, Ahmadi H, Shahmoradi L, Ibrahim O, Akbari E

J Infect Public Health, 2018 10 04;12(1):13-20.
PMID: 30293875 DOI: 10.1016/j.jiph.2018.09.009

BACKGROUND: Hepatitis is an inflammation of the liver, most commonly caused by a viral infection. Supervised data mining techniques have been successful in hepatitis disease diagnosis through a set of datasets. Many methods have been developed by the aids of data mining techniques for hepatitis disease diagnosis. The majority of these methods are developed by single learning techniques. In addition, these methods do not support the ensemble learning of the data. Combining the outputs of several predictors can result in improved accuracy in classification problems. This study aims to propose an accurate method for the hepatitis disease diagnosis by taking the advantages of ensemble learning.
METHODS: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies.
RESULTS: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine.
CONCLUSIONS: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare.
Fulltext Can complementary and alternative medicines be beneficial in the treatment of COVID-19 through improving immune system function?

Nilashi M, Samad S, Yusuf SYM, Akbari E

J Infect Public Health, 2020 Jun;13(6):893-896.
PMID: 32451258 DOI: 10.1016/j.jiph.2020.05.009
Escherichia coli bacteria detection by using graphene-based biosensor

Akbari E, Buntat Z, Afroozeh A, Zeinalinezhad A, Nikoukar A

IET Nanobiotechnol, 2015 Oct;9(5):273-9.
PMID: 26435280 DOI: 10.1049/iet-nbt.2015.0010

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.
Fulltext Sensing and identification of carbon monoxide using carbon films fabricated by methane arc discharge decomposition technique

Akbari E, Buntat Z, Enzevaee A, Yazdi MK, Bahadoran M, Nikoukar A

Nanoscale Res Lett, 2014;9(1):402.
PMID: 25177219 DOI: 10.1186/1556-276X-9-402

Carbonaceous materials have recently received attention in electronic applications and measurement systems. In this work, we demonstrate the electrical behavior of carbon films fabricated by methane arc discharge decomposition technique. The current-voltage (I-V) characteristics of carbon films are investigated in the presence and absence of gas. The experiment reveals that the current passing through the carbon films increases when the concentration of CO2 gas is increased from 200 to 800 ppm. This phenomenon which is a result of conductance changes can be employed in sensing applications such as gas sensors.
Fulltext Analytical modeling of trilayer graphene nanoribbon Schottky-barrier FET for high-speed switching applications

Rahmani M, Ahmadi MT, Abadi HK, Saeidmanesh M, Akbari E, Ismail R

Nanoscale Res Lett, 2013;8(1):55.
PMID: 23363692 DOI: 10.1186/1556-276X-8-55

Recent development of trilayer graphene nanoribbon Schottky-barrier field-effect transistors (FETs) will be governed by transistor electrostatics and quantum effects that impose scaling limits like those of Si metal-oxide-semiconductor field-effect transistors. The current-voltage characteristic of a Schottky-barrier FET has been studied as a function of physical parameters such as effective mass, graphene nanoribbon length, gate insulator thickness, and electrical parameters such as Schottky barrier height and applied bias voltage. In this paper, the scaling behaviors of a Schottky-barrier FET using trilayer graphene nanoribbon are studied and analytically modeled. A novel analytical method is also presented for describing a switch in a Schottky-contact double-gate trilayer graphene nanoribbon FET. In the proposed model, different stacking arrangements of trilayer graphene nanoribbon are assumed as metal and semiconductor contacts to form a Schottky transistor. Based on this assumption, an analytical model and numerical solution of the junction current-voltage are presented in which the applied bias voltage and channel length dependence characteristics are highlighted. The model is then compared with other types of transistors. The developed model can assist in comprehending experiments involving graphene nanoribbon Schottky-barrier FETs. It is demonstrated that the proposed structure exhibits negligible short-channel effects, an improved on-current, realistic threshold voltage, and opposite subthreshold slope and meets the International Technology Roadmap for Semiconductors near-term guidelines. Finally, the results showed that there is a fast transient between on-off states. In other words, the suggested model can be used as a high-speed switch where the value of subthreshold slope is small and thus leads to less power consumption.
Fabrication of biodegradable Zn-Al-Mg alloy: Mechanical properties, corrosion behavior, cytotoxicity and antibacterial activities

Bakhsheshi-Rad HR, Hamzah E, Low HT, Kasiri-Asgarani M, Farahany S, Akbari E, et al.

Mater Sci Eng C Mater Biol Appl, 2017 Apr 01;73:215-219.
PMID: 28183601 DOI: 10.1016/j.msec.2016.11.138

In this work, binary Zn-0.5Al and ternary Zn-0.5Al-xMg alloys with various Mg contents were investigated as biodegradable materials for implant applications. Compared with Zn-0.5Al (single phase), Zn-0.5Al-xMg alloys consisted of the α-Zn and Mg2(Zn, Al)11 with a fine lamellar structure. The results also revealed that ternary Zn-Al-Mg alloys presented higher micro-hardness value, tensile strength and corrosion resistance compared to the binary Zn-Al alloy. In addition, the tensile strength and corrosion resistance increased with increasing the Mg content in ternary alloys. The immersion tests also indicated that the corrosion rates in the following order Zn-0.5Al-0.5Mg
Fulltext An analytical approach to evaluate the performance of graphene and carbon nanotubes for NH3 gas sensor applications

Akbari E, Arora VK, Enzevaee A, Ahmadi MT, Saeidmanesh M, Khaledian M, et al.

Beilstein J Nanotechnol, 2014;5:726-34.
PMID: 24991510 DOI: 10.3762/bjnano.5.85

Carbon, in its variety of allotropes, especially graphene and carbon nanotubes (CNTs), holds great potential for applications in variety of sensors because of dangling π-bonds that can react with chemical elements. In spite of their excellent features, carbon nanotubes (CNTs) and graphene have not been fully exploited in the development of the nanoelectronic industry mainly because of poor understanding of the band structure of these allotropes. A mathematical model is proposed with a clear purpose to acquire an analytical understanding of the field-effect-transistor (FET) based gas detection mechanism. The conductance change in the CNT/graphene channel resulting from the chemical reaction between the gas and channel surface molecules is emphasized. NH3 has been used as the prototype gas to be detected by the nanosensor and the corresponding current-voltage (I-V) characteristics of the FET-based sensor are studied. A graphene-based gas sensor model is also developed. The results from graphene and CNT models are compared with the experimental data. A satisfactory agreement, within the uncertainties of the experiments, is obtained. Graphene-based gas sensor exhibits higher conductivity compared to that of CNT-based counterpart for similar ambient conditions.
Fulltext Analytical calculation of sensing parameters on carbon nanotube based gas sensors

Akbari E, Buntat Z, Ahmad MH, Enzevaee A, Yousof R, Iqbal SM, et al.

Sensors (Basel), 2014;14(3):5502-15.
PMID: 24658617 DOI: 10.3390/s140305502

Carbon Nanotubes (CNTs) are generally nano-scale tubes comprising a network of carbon atoms in a cylindrical setting that compared with silicon counterparts present outstanding characteristics such as high mechanical strength, high sensing capability and large surface-to-volume ratio. These characteristics, in addition to the fact that CNTs experience changes in their electrical conductance when exposed to different gases, make them appropriate candidates for use in sensing/measuring applications such as gas detection devices. In this research, a model for a Field Effect Transistor (FET)-based structure has been developed as a platform for a gas detection sensor in which the CNT conductance change resulting from the chemical reaction between NH3 and CNT has been employed to model the sensing mechanism with proposed sensing parameters. The research implements the same FET-based structure as in the work of Peng et al. on nanotube-based NH3 gas detection. With respect to this conductance change, the I-V characteristic of the CNT is investigated. Finally, a comparative study shows satisfactory agreement between the proposed model and the experimental data from the mentioned research.