Development of solution-gated graphene transistor model for biosensors
© Karimi et al.; licensee Springer. 2014
Received: 13 December 2013
Accepted: 24 January 2014
Published: 11 February 2014
The distinctive properties of graphene, characterized by its high carrier mobility and biocompatibility, have stimulated extreme scientific interest as a promising nanomaterial for future nanoelectronic applications. In particular, graphene-based transistors have been developed rapidly and are considered as an option for DNA sensing applications. Recent findings in the field of DNA biosensors have led to a renewed interest in the identification of genetic risk factors associated with complex human diseases for diagnosis of cancers or hereditary diseases. In this paper, an analytical model of graphene-based solution gated field effect transistors (SGFET) is proposed to constitute an important step towards development of DNA biosensors with high sensitivity and selectivity. Inspired by this fact, a novel strategy for a DNA sensor model with capability of single-nucleotide polymorphism detection is proposed and extensively explained. First of all, graphene-based DNA sensor model is optimized using particle swarm optimization algorithm. Based on the sensing mechanism of DNA sensors, detective parameters (Ids and Vgmin) are suggested to facilitate the decision making process. Finally, the behaviour of graphene-based SGFET is predicted in the presence of single-nucleotide polymorphism with an accuracy of more than 98% which guarantees the reliability of the optimized model for any application of the graphene-based DNA sensor. It is expected to achieve the rapid, quick and economical detection of DNA hybridization which could speed up the realization of the next generation of the homecare sensor system.
With the discovery of graphene, a single atomic layer of graphite, material science has been experiencing a new path in biomedical applications, due to its fascinating properties . Graphene possess extraordinary physical properties, such as a unique electronic band structure, extremely high carrier mobility, biocompatibility and well-known two-dimensional (2D) structure exposing every atom of graphene to the environment [1–3]. It is demonstrated that the high sensitivity of graphene to the charged analytes (ions, DNA, cells, etc.) or an electric field around it renders graphene an ideal material for high-performance sensors. In the last 5 years, there has been an increasing amount of literature on solution-gated field effect transistors (SGFETs) as useful candidates for chemical and biological sensors [4, 5]. The interface between nanomaterials and biosystems is emerging as one of the most interesting areas of intense research . Recent advances and key issues for the development of DNA sensors to bridge the knowledge to clinical detection of DNA hybridization emerged as a promising means of diagnostic prediction in genetic research [7, 8]. The aim of this paper is to provide a possibility of having more sensitive and sequence-selective DNA biosensors by developing the SGFETs analytical model for electrical detection of DNA molecules [9, 10]. Graphene layer is selected as a sensing template because of its large surface-to-volume ratio which guarantees better physical adsorption of DNA due to more accessible contact, compared with other carbon materials .
It is noteworthy to explain the DNA adsorption effect on nanomaterials of graphene surface as well as the proposed model. In graphene, the electronic transport takes place by hopping along π orbitals which is due to the sp2 hybridized covalent bonds that held the carbon atoms together, while each of them can participate in some kind of bonding with adsorbates . Theoretical data suggest that the bonding between the DNA bases and the carbon atoms is a kind of van der Waals (vdW) bonding (π-π stacking) [27, 28]. Since the DNA molecules have the negative charges, therefore, it could be expected that the adsorption of DNA molecules on graphene surface would directly modulate the drain current of the SGFET device [29, 30]. Based on the detection mechanism, we recently proposed an analytical model for the detection of DNA molecules in which the DNA concentration was modelled by a gate voltage .
It is concluded that the sensor model with the suggested parameters represents the same trend as experimental data [2, 6]. Since the values of the parameters A, B and C in Equation 2 were calculated based on trial and error, there is necessity of a methodological approach for obtaining a viable and accurate model which is reliable for being used in different applications of the graphene-based DNA sensor. To this purpose, an evolutionary algorithm (EA) called particle swarm optimization (PSO) is used for optimizing the mathematical model shown in Equation 1. The PSO technique is widely used in optimizing different sorts of problems including fine materials, medical science, control theory, energy issues, etc. [33–36]. The important facts that make PSO popular among the researchers are its fastness, avoiding from being trapped in the local optima, and the capability of being employed in any type of optimization problems [37–40].
Particle swarm optimization overview
The PSO is a swarm-based optimization algorithm which is classified as a metaheuristic optimization algorithm. The idea of the PSO rises from the movement of a bird flock which was first introduced by Kennedy and Eberheart [41–45]. The aim of employing PSO algorithm in this study, is to find the best possible values for A, B and C parameters in Equation 2 which leads to have a more accurate DNA sensor model with better I-V characteristic. Each particle at each step is supposed to return a set of three values with respect to A, B and C parameters. Afterwards, these values must be evaluated using a proper fitness function. During the optimization process, the values of A, B and C parameters change, until we can get the best possible solutions.
i = 1, 2, …, nop (number of particles); k = 1, 2, …, kmax (maximum iteration number) where i is the particle number; k is the iteration number; W refers to the inertia weight coefficient which is decreased continuously from 1.2 to 0.5, r1 and r2 are random values between 0 and 1, c1 and c2 are acceleration coefficients and set to be equal to 2, denotes the position and is the velocity of particle i at iteration k.
where I(k) is the experimental waveform of the DNA sensor, represents the value of the modelled waveform for particle i and ψ i is the fitness value for the i th particle. Obviously, the lowest fitness value represents the most fitted curve which is desired for a reliable DNA sensor model.
Results and discussion
Results of optimization for DNA sensor model
The best values of the optimizing parameters over the 20 runs
The best fitness value obtained
Optimized value for A
Optimized value for B
Optimized value for C
The MAPE value for different concentrations of DNA sensor ( F )
MAPE value (%)
Accuracy based on MAPE (%)
F = 0.01
F = 0.1
F = 1
F = 10
F = 100
F = 500
In the next section, it is demonstrated that the optimized model of solution-gated graphene-based DNA sensors can be utilized for electrical detection of DNA hybridization application.
DNA hybridization detection using the optimized model
As shown in Figure 5, by applying the gate voltage to the DNA solution, it is obviously affirmed that the conductance of SGFET shows amipolar behaviour since the Fermi energy can be controlled by the gate voltage. Based on this outstanding characteristic, it is notable that the graphene can continuously be switched from the p-doped to the n-doped region by a controllable gate voltage. At the transition point where the density of electron and hole are the same, the minimum conductance (Vgmin) is detected. This conjunction point is called charge neutrality point (CNP). The doping states of graphene have been monitored by the Vg,min to measure the minimum conductance of the graphene layer which is identified from the transfer characteristic curve.
It can be seen in Figure 5 that by immobilization of the probe DNAs, either complementary or mismatch, on the graphene surface, the Vg,min is considerably left-shifted by 10 mV. This fact can demonstrate the dependency of Vg,min on the immobilization of the probe DNA and hybridization of the complementary target DNAs. In other words, DNA molecules as n-dopants, shift the gate voltage leftwards due to the fact that DNA molecules n-dopes the graphene layer . By introduction of DNAs as electron-rich molecules, the number of carriers would change in the graphene channel which has led in varying the conductance of source and drain [51–53]. SGFETs with high sensitivity is applied to detect the DNA hybridization based on the conductance variations. Finally, the hybridization event has been performed by introducing complementary sequences which include the target sequence of the probe DNA immobilized graphene device .
I ds , V gmin for different concentration of DNA molecules
It is apparently seen that the considerable decrease of conductance is a sign of probe-target matching combination in DNA hybridization. The experimental data indicates the strong dependency of the gate voltage on the concentration increment which can have a predictable influence on the current-voltage characteristics of SGFET device. In other words, the I d shifts downwards while the gate voltage shifts leftwards.
The complementary DNAs also successfully attach to the graphene surface through graphene-nucleotide interaction and impose n-doping effect which results as the left shift of Vg,min after DNA hybridization. It is stated that the stacking interaction between nucleotide and graphene surface upon DNA hybridization has a strong influence on Vg,min, which can shift it leftwards . This phenomena describes that the transfer of electrons from the target DNA happens because the probe DNA brings it to the proximity of the graphene surface . In addition to the Vg,min shift, the I d experiences a current decrease from 4.7 to 4.1 amp at Vg = -0.5v. Furthermore, when DNA molecule is present, the I d continues to decrease with concentration increment of complementary DNAs. This fact can be explained by the p-type behaviour of graphene in the FET structure as observed by [56–59], which can justify the current decrease upon DNA hybridization event. While graphene is known as a p-type semiconductor with the holes as a majority of carriers, the electrons from DNA will lower the carrier concentration of graphene and hence reduce the conductance. By increasing the amount of complementary DNA concentration, more DNAs will make the configurational change and cause more electrons being trapped on the surface. The current or conductance shows a steady drop off at V g = -0.5v.
Decision making table based upon different conditions happened to detective parameters
Hybridization is happened
Due to the outstanding properties of graphene nanomaterial such as high surface area, electrical conductivity and biocompatibility, it has remarkable potential for DNA and protein detection as a biosensing material. The detection of DNA hybridization is currently an area of intense interest whereas recent studies have proved that the mutations of genes are responsible for numerous inherited human disorders. In this research, graphene is chosen as both a sensing layer and a conducting channel in solution-gated field effect transistors for detection of DNA hybridization. In order to facilitate the rational design and the characterization of these devices, a DNA sensor model using particle swarm optimization theory developed and applied for detection of DNA hybridization. Furthermore, our proposed model is capable of detecting the single-nucleotide polymorphism by suggesting the detective parameters (Ids and Vgmin). Finally, the behaviour of solution-gated field effect transistor-based graphene is compared by the experiment results. An accuracy of more than 98% is reported in this paper which guarantees the reliability of an optimized model for any application of the graphene-based DNA sensor such as diagnosis of genetic and pathogenic deseases.
The authors would like to acknowledge the financial support from Research University grant of the Ministry of Higher Education of Malaysia (MOHE) under Project grant: GUP - 04H40. Also, thanks to the Research Management Center (RMC) of Universiti Teknologi Malaysia (UTM) for providing an excellent research environment to complete this work.
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