- Nano Express
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Analytical modeling of glucose biosensors based on carbon nanotubes
Nanoscale Research Lettersvolume 9, Article number: 33 (2014)
In recent years, carbon nanotubes have received widespread attention as promising carbon-based nanoelectronic devices. Due to their exceptional physical, chemical, and electrical properties, namely a high surface-to-volume ratio, their enhanced electron transfer properties, and their high thermal conductivity, carbon nanotubes can be used effectively as electrochemical sensors. The integration of carbon nanotubes with a functional group provides a good and solid support for the immobilization of enzymes. The determination of glucose levels using biosensors, particularly in the medical diagnostics and food industries, is gaining mass appeal. Glucose biosensors detect the glucose molecule by catalyzing glucose to gluconic acid and hydrogen peroxide in the presence of oxygen. This action provides high accuracy and a quick detection rate. In this paper, a single-wall carbon nanotube field-effect transistor biosensor for glucose detection is analytically modeled. In the proposed model, the glucose concentration is presented as a function of gate voltage. Subsequently, the proposed model is compared with existing experimental data. A good consensus between the model and the experimental data is reported. The simulated data demonstrate that the analytical model can be employed with an electrochemical glucose sensor to predict the behavior of the sensing mechanism in biosensors.
The advent of nanotechnology provides a new perspective for the development of nanosensors and nanoprobes with nanometer dimensions and is appropriate for biological and biomolecular measurements . The use of tools capable of detecting and monitoring the biomolecular process can create enormous advances in the detection and treatment of diseases and thereby revolutionize cell biology and medical science . A biosensor is an electronic device which has a biological probe and a transducer that is connected to a monitor. The demand for a wide variety of applications for a biosensor in industrial, environmental and biomedical diagnostics is dramatically increasing [1–3]. Biomedical applications, such as blood glucose detection, demand a great deal of research activities. Glucose oxide (GOx)-based enzyme sensors have been immensely used for the diagnosis and monitoring of blood glucose level because of the ability of GOx to identify glucose target molecules quickly and accurately [4–6]. Because of the constraints of other approaches, such as ultralow detection, large detection range, high cost, and knowledge complexity, the implementation of effective approaches using carbon-based materials is vital. Carbon nanotubes (CNTs) with superior electrical performance are essential in designing modern biosensors [7–10]. CNT-based biosensors have an economical production process, rapid response, high sensitivity, and good selectivity and are easily available in the market. Hence, a great deal of research has been conducted to study the performance of CNT-based field-effect transistor (FET) biosensors [11–14]. Due to their excellent mechanical stability, high conductivity, and antifouling properties, CNTs have been widely employed for GOx immobilization in biosensors . Moreover, the CNT platform provides a more appropriate environment for immobilized GOx and therefore provides a quick shuttling of electrons with the surface of an electrode [15, 16]. In sensor technology, analytical modeling based on experimental finding is still ongoing. This study proposes an analytical glucose biosensor model of single-wall carbon nanotube field-effect transistor (SWCNT FET) to predict the drain current versus drain voltage (I-V) performance. For the first time, the effects of glucose adsorption on CNT electrical properties, namely gate voltage, are studied and formulated versus a wide range of glucose concentration.
In this section, the methods of immobilization will be described to explain the sensing mechanism of a biosensor. Immobilization is a process to integrate a biocatalyst with a matrix that it is not soluble in aqueous media. A wide variety of approaches can be applied for the immobilization of enzymes or cells on a variety of natural and synthetic supports. Both of the immobilization approach and support are dependent on the type of enzyme and substrate [17, 18]. Enzymes are very instable and sensitive to their environment . When no special precaution is required, some common approaches, such as deactivation on an adsorption and chemical or thermal inactivation, are adopted [19, 20]. The important techniques that maintain the enzyme activity of immobilization are encapsulation, covalent immobilization, and site-specific mutagenesis [15, 21]. Ultimately, the application of the new materials will generally affect the quality of the sensing mechanism. Because of the high surface area-to-volume ratio, CNTs demonstrate good device performance  when they are used as a semiconducting channel in biosensors . The CNT application on glucose detection has been experimentally reported in  where GOx is utilized as an enzyme. The fabrication process of the SWCNT-based (1 to 2 nm in diameter, 50 μm in length) [25, 26] electrochemical glucose biosensors using GOx  is depicted in Figure 1a,b. Polyelectrolytes, such as poly(diallyldimethylammonium chloride) (PDDA) and polystyrenesulfonate (PSS) are implemented . Figure 1a shows the assembly of PDDA/SWCNT on polyethylene terephthalate (PET) polyester flexible substrate, and GOx biomolecular assembly is depicted in Figure 1b.
To produce stable negative charges, GOx is dissolved into a phosphate-buffered saline (PBS) with a concentration of 1 mg/mL. Phosphate monobasic (NaH2PO4) and dibasic (Na2HPO4) are employed as a standard pH buffer solution. The standard d-glucose solutions have been used in the glucose concentration test, and the results are shown in terms of drain current versus drain voltage (I-V) characteristics .
Figure 1b shows the structure of the SWCNT FET with PET polyester as a back gate and chromium (Cr) or aurum (Au) as the source and drain, respectively. A SWCNT is employed as a channel to connect the source and drain. According to the proposed structure, two main modeling approaches in the carbon nanotube field-effect transistor (CNTFET) analytical modeling can be utilized. The first approach is derived from the charge-based framework, and the second modeling approach is a noncharge-based analytical model using the surface-potential-based analysis method. The charge-based carrier velocity model is implemented in this work. The drift velocity of carrier in the presence of an applied electric field  is given as
where μ is the mobility of the carriers, E is the electric field, and E c is the critical electric field under high applied bias. From Equation 1, the drain current as a function of gate voltage (VG) and drain voltage (VD) is obtained as
where β = μCG/(2L), VGT = VG - VT, and critical applied voltage as Vc = (vsat/μ)L, where vsat is the saturation velocity, VG is the gate to source voltage, VT is the threshold voltage , CG is the gate capacitance per unit length, and L is the effective channel length . The unknown nature of the quantum emission is not considered in this calculation. Based on the geometry of CNTFET that is proposed in Figure 1b, the gate capacitance (CG) can be defined as
where CE and CQ are the electrostatic gate coupling capacitance of the gate oxide and the quantum capacitance of the gated SWCNT, respectively [30–33]. Figure 2 shows the I-V characteristics of a bare SWCNT FET for different gate voltages without any PBS and glucose concentration that is based on Equation 2.
The electrostatic gate coupling capacitance C E for Figure 1b is given as
where HPET is the PET polyester thickness, d is the diameter of CNT and ϵ = 3.3ϵ 0 is the dielectric permittivity of PET. The existence of the quantum capacitance is due to the displacement of the electron wave function at the CNT insulator interface. CQ relates to the electron Fermi velocity (vF) in the form of CQ = 2e/vF where vF ≈ 106 m/s . Numerically, the quantum capacitance is 76.5 aF/μm and shows that both the electrostatic and quantum capacitances have a high impact on CNT characteristics [35, 36]. At saturation velocity, the electric field is very severe at the early stage of current saturation at the drain end of the channel.
In this research, the effect of glucose concentration (Fg) on the I-V characteristics of the CNTFET is studied. First, PBS should be added to the solution so that glucose is detectable and the change in I-V can be observed. The I-V change is due to the carrier concentration gradient of the injected carriers from the PBS to the channel and vice versa. The channel carrier concentration can be modeled in the function of gate voltage variations as
where VGS1(with PBS) is the gate voltage in the presence of PBS, VPBS is the voltage due to the interaction of PBS with CNT in the solution, and VGS(without PBS) indicates the gate voltage in a bare channel. The effect of PBS in the I-V characteristics is modeled as
Before glucose and PBS is added, VGS(without PBS) is set to be 1.5 V. The VPBS is found to 0.6 V when the PBS concentration, FPBS = 1 mg/mL, is added into the solution. Using Equations 5 and 6, the presented model provides a good consensus between the model and the experimental data as shown in Figure 3.
In the glucose sensing mechanism reported in , β-d-glucose oxidizes to d-glucono-δ-lactone and hydrogen peroxide (H2O2) as a result of the catalyst reaction of GOx. The hydrolyzation of d-glucose-δ-lactone and the electrooxidation of H2O2 under an applied gate voltage produce two hydrogen ions and two electrons which contribute to the additional carrier concentration in the SWCNT channel. On the whole, the glucose sensing mechanism can be summarized as follows:
The variation of the proximal ionic deposition and the direct electron transfer to the electrode surface modify the electrical conductance of the SWCNT. The direct electron transfer leads to a variation of the drain current in the SWCNT FET. Therefore, Equation 10 that incorporates the gate voltage change due to the additional electrons from the glucose interaction with PBS is given as
VGlucose is the glucose-based controlling parameters that highlight the effects of glucose concentration against gate voltages. In the proposed model, Equation 12 is obtained by analyzing the rise ID with gate voltages versus glucose concentration. Based on the iteration method demonstrated in , the concentration control parameter as a function of glucose concentration in a piecewise exponential model is expressed as
In other words, the I-V characteristics of the biosensor can also be controlled by changing the glucose concentration. To evaluate the proposed model, the drain voltage is varied from 0 to 0.7 V, which is similar to the measurement work, and Fg is changed in the range of 2 to 50 mM .
Results and discussion
Glucose sensing and accuracy of sensor model
By increasing the glucose concentration in multiple steps from 2 to 50 mM, a fairly good consensus between our simulation model and experimental data particularly in the linear region is illustrated in Figure 4. The results show the accuracy of our predictive model against the measurement data of the glucose biosensor for various glucose concentrations up to 50 mM. It is observed that the current in the CNTFET increases exponentially with glucose concentration.
From Figure 4, the glucose sensor model shows a sensitivity of 18.75 A/mM on a linear range of 2 to 10 mM at VD = 0.7 V. The high sensitivity is due to the additional electron per glucose molecule from the oxidation of H2O2, and the high quality of polymer substrate that are able to sustain immobilized GOx . It is shown that by increasing the concentration of glucose, the current in CNTFET increases. It is also evident that gate voltage increases with higher glucose concentrations. Table 1 shows the relative difference in drain current values in terms of the average root mean square (RMS) errors (absolute and normalized) between the simulated and measured data when the glucose is varied from 2 to 50 mM. The normalized RMS errors are given by the absolute RMS divided by the mean of actual data. It also revealed that the corresponding average RMS errors do not exceed 13%. The discrepancy between simulation and experimental data is due to the onset of saturation effects of the drain current at higher gate voltages and glucose concentration where enzyme reactions are limited.
The CNTs as carbon allotropes illustrate the amazing mechanical, chemical, and electrical properties that are preferable for use in biosensors. In this paper, the analytical modeling of SWCNT FET-based biosensors for glucose detection is performed to predict sensor performance. To validate the proposed model, a comparative study between the model and the experimental data is prepared, and good consensus is observed. The current of the biosensor is a function of glucose concentration and therefore can be utilized for a wide process variation such as length and diameter of nanotube, capacitance of PET polymer, and PBS voltage. The glucose sensing parameters with gate voltages are also defined in exponential piecewise function. Based on a good consensus between the analytical model and the measured data, the predictive model can provide a fairly accurate simulation based on the change in glucose concentration.
AHP received his B.S. degree in Electronic Engineering from the Islamic Azad University of Bonab, Iran in 2011. At the moment, he is pursuing his Master’s degree in Eng. (Electronic and Telecommunication) from Universiti Teknologi (UTM), Malaysia. He is currently a member of the Computational Nanoelectronics (CoNE) Research Group in UTM. His current research interests are in biosensors based on nanomaterials and nanodevices.
MTA is a tenured assistant professor of nanoelectronics at the Nanotechnology Research Center at Urmia University. He received his Ph.D. degree in Electrical Engineering from Universiti Teknologi Malaysia in 2010. His research interests are in the simulation, modeling, and characterization of nonclassical nanostructure devices which include sensors and transistors.
MR received his Ph.D. degree in Electrical Engineering from UTM in 2013. He joined the Computational Nanoelectronics (CoNE) Research Group in 2009. He has published over 20 peer-reviewed papers in reputed international journals and conferences. His main research interests are in carbon-based nanoelectronics.
HCC was born in Bukit Mertajam, Penang, Malaysia, in 1989. She received her B. Eng. (electrical-electronics) from Universiti Teknologi Malaysia (UTM) in 2013. During her practical training, she underwent an internship at Intel Penang Design Centre, Penang, Malaysia. She is currently pursuing her Master’s degree at the same university.
CSL received his B. Eng. degree in Electrical Engineering (first class honors), M. Eng degree (Electrical), and Ph.D. degree from Universiti Teknologi Malaysia (UTM), in 1999, 2004, and 2011, respectively. He is a senior lecturer at UTM, a faculty member of the Department of Control and Mechatronic Engineering, and a research member of Process Tomography Research Group & Instrumentation (PROTOM-i), Faculty of Electrical Engineering. His research interests are in embedded system, emergency medical services, telerobotics and multi-agent system.
RI received his B.Sc. and M.Sc. degrees in Electrical and Electronic Engineering from the University of Nottingham, Nottingham, UK in 1980 and 1983, respectively, and his Ph.D. degree from Cambridge University, Cambridge, UK in 1989. In 1984, he joined the Faculty of Electrical Engineering, Universiti Teknologi Malaysia as a lecturer in Electrical and Electronic Engineering. He has held various faculty positions including head of the department and chief editor of the university journal. RI has worked for more than 20 years in this research area and has published various articles on the subject. His current research interest is in the emerging area of nanoelectronic devices focusing on the use of carbon-based materials and novel device structure. He is presently with the Universiti Teknologi Malaysia as a professor and head of the Computational Nanoelectronics (CoNE) Research Group. RI is a member of the IEEE Electron Devices Society (EDS).
MLPT was born in Bukit Mertajam, Penang, Malaysia, in 1981. He received his B. Eng. (Electrical-Telecommunications) and M. Eng. (Electrical) degrees from Universiti Teknologi Malaysia (UTM), Skudai, Malaysia, in 2003 and 2006, respectively. He conducted his postgraduate research in nanoscale MOSFET modeling at the Intel Penang Design Center, Penang, Malaysia. He recently obtained his Ph.D. degree in 2011 at the University of Cambridge, Cambridge, UK. He is a senior lecturer at UTM, a faculty member of the Department of Electronic and Computer Engineering and a research member of the CoNE Research Group, Faculty of Electrical Engineering. His present research interests are in device modeling and circuit simulation of carbon nanotube, graphene nanoribbon, and MOSFET. MLPT is a registered graduate engineer of BEM, IEEE member, MIET member, graduate member of IEM (GRAD IEM), MySET, Johor Bahru Toastmasters International Club, and alumnus of Queens’ College Cambridge.
Wolfbeis OS: Fiber-optic chemical sensors and biosensors. Anal Chem 2008, 80: 4269–4283. 10.1021/ac800473b
Diamond D: Principles of Chemical and Biological Sensors. New York: Wiley; 1998.
Sandhu A: Glucose sensing: silicon’s sweet spot. Nat Nanotechnol 2007. 10.1038/nnano.2007.2 10.1038/nnano.2007.2
Zhu ZG, Garcia-Gancedo L, Chen C, Zhu XR, Xie HQ, Flewitt AJ, Milne WI: Enzyme-free glucose biosensor based on low density CNT forest grown directly on a Si/SiO2 substrate. Sens Act B-Chem 2013, 178: 586–592.
Wen Z, Ci S, Li J: Pt nanoparticles inserting in carbon nanotube arrays: nanocomposites for glucose biosensors. J Phys Chem C 2009, 113: 13482–13487. 10.1021/jp902830z
Zhu Z, Song W, Burugapalli K, Moussy F, Li Y-L, Zhong X-H: Nano-yarn carbon nanotube fiber based enzymatic glucose biosensor. Nanotechnology 2010, 21: 165501. 10.1088/0957-4484/21/16/165501
Alwarappan S, Boyapalle S, Kumar A, Li C-Z, Mohapatra S: Comparative study of single-, few-, and multilayered graphene toward enzyme conjugation and electrochemical response. J Phys Chem C 2012, 116: 6556–6559. 10.1021/jp211201b
Du D, Zou Z, Shin Y, Wang J, Wu H, Engelhard MH, Liu J, Aksay IA, Lin Y: Sensitive immunosensor for cancer biomarker based on dual signal amplification strategy of graphene sheets and multienzyme functionalized carbon nanospheres. Anal Chem 2010, 82: 2989–2995. 10.1021/ac100036p
Abdelwahab AA, Koh WCA, Noh H-B, Shim Y-B: A selective nitric oxide nanocomposite biosensor based on direct electron transfer of microperoxidase: removal of interferences by co-immobilized enzymes. Biosens Bioelectron 2010, 26: 1080–1086. 10.1016/j.bios.2010.08.070
Kiani M, Ahmadi M, Akbari E, Rahmani M, Karimi H, Che Harun FK: Analytical modeling of bilayer graphene based biosensor. J Biosens Bioelect 2013, 4: 131.
Zheng D, Vashist SK, Dykas MM, Saha S, Al-Rubeaan K, Lam E, Luong JH, Sheu F-S: Graphene versus multi-walled carbon nanotubes for electrochemical glucose biosensing. Materials 2013, 6: 1011–1027. 10.3390/ma6031011
Razumiene J, Gureviciene V, Sakinyte I, Barkauskas J, Petrauskas K, Baronas R: Modified SWCNTs for reagentless glucose biosensor: electrochemical and mathematical characterization. Electroanalysis 2013, 25: 166–173. 10.1002/elan.201200383
Raicopol M, Prun A, Damian C, Pilan L: Functionalized single-walled carbon nanotubes/polypyrrole composites for amperometric glucose biosensors. Nanoscale Res Lett 2013, 8: 316. 10.1186/1556-276X-8-316
Jose MV, Marx S, Murata H, Koepsel RR, Russell AJ: Direct electron transfer in a mediator-free glucose oxidase-based carbon nanotube-coated biosensor. Carbon 2012, 50: 4010–4020. 10.1016/j.carbon.2012.04.044
Sotiropoulou S, Gavalas V, Vamvakaki V, Chaniotakis N: Novel carbon materials in biosensor systems. Biosens Bioelectron 2003, 18: 211–215. 10.1016/S0956-5663(02)00183-5
Sotiropoulou S, Chaniotakis NA: Carbon nanotube array-based biosensor. Anal Bioanal Chem 2003, 375: 103–105.
Zhang Y-Q, Tao M-L, Shen W-D, Zhou Y-Z, Ding Y, Ma Y, Zhou W-L: Immobilization of L -asparaginase on the microparticles of the natural silk sericin protein and its characters. Biomaterials 2004, 25: 3751–3759. 10.1016/j.biomaterials.2003.10.019
Guisan JM: Immobilization of Enzymes and Cells. 2nd edition. Totowa: Humana Press; 2006.
Chaniotakis NA: Enzyme stabilization strategies based on electrolytes and polyelectrolytes for biosensor applications. Anal Bioanal Chem 2004, 378: 89–95. 10.1007/s00216-003-2188-3
Skoog DA, West DM, Holler FJ: Fundamentals of Analytical Chemistry. 5th edition. Philadelphia: Saunders College Publishing; 1988.
Grieshaber D, MacKenzie R, Vörös J, Reimhult E: Electrochemical biosensors-sensor principles and architectures. Sensors 2008, 8: 1400–1458. 10.3390/s8031400
Cao Q, Han SJ, Tulevski GS, Zhu Y, Lu DD, Haensch W: Arrays of single-walled carbon nanotubes with full surface coverage for high-performance electronics. Nat Nanotechnol 2013, 8: 180–186. 10.1038/nnano.2012.257
Park H, Afzali A, Han S-J, Tulevski GS, Franklin AD, Tersoff J, Hannon JB, Haensch W: High-density integration of carbon nanotubes via chemical self-assembly. Nature Nanotech 2012, 7: 787–791. 10.1038/nnano.2012.189
Lee D, Cui T: Low-cost, transparent, and flexible single-walled carbon nanotube nanocomposite based ion-sensitive field-effect transistors for pH/glucose sensing. Biosens Bioelectron 2010, 25: 2259–2264. 10.1016/j.bios.2010.03.003
Lee D, Cui T: Layer-by-layer self-assembled single-walled carbon nanotubes based ion-sensitive conductometric glucose biosensors. Sens J, IEEE 2009, 9: 449–456.
Lee D, Cui T: pH-dependent conductance behaviors of layer-by-layer self-assembled carboxylated carbon nanotube multilayer thin-film sensors. J Vacuum Sci Technol B: Microelect Nano Struct 2009, 27: 842. 10.1116/1.3002386
Ahmadi MT, Tan MLP, Ismail R, Arora VK: The high-field drift velocity in degenerately-doped silicon nanowires. Int J Nanotechnol 2009, 6: 601–617. 10.1504/IJNT.2009.025299
Chek DC, Tan MLP, Ahmadi MT, Ismail R, Arora VK: Analytical modeling of high performance single-walled carbon nanotube field-effect-transistor. Microelectron J 2010, 41: 579–584. 10.1016/j.mejo.2010.05.008
Ahmadi MT, Karamdel J, Ismail R, Dee C, Majlis BY: Modelling of the current–voltage characteristics of a carbon nanotube field effect transistor. In 2008 ICSE 2008 IEEE International Conference on Semiconductor Electronics. Johor Bahru: Piscataway: IEEE; 2008:576–580.
Anantram M, Leonard F: Physics of carbon nanotube electronic devices. Rep Prog Phys 2006, 69: 507. 10.1088/0034-4885/69/3/R01
Tan MLP: Device and circuit-level models for carbon nanotube and graphene nanoribbon transistors. Thesis. Cambridge: University of Cambridge, Department of Engineering; 2011.
Tan MLP, Lentaris G, Amaratunga GA: Device and circuit-level performance of carbon nanotube field-effect transistor with benchmarking against a nano-MOSFET. Nanoscale Res Lett 2012, 7: 467. 10.1186/1556-276X-7-467
Tan MLP: Long channel carbon nanotube as an alternative to nanoscale silicon channels in scaled MOSFETs. J Nanomater 2013, 2013: 831252.
Lin Y-M, Appenzeller A, Chen Z, Chen Z-G, Cheng H-M, Avouris P: Demonstration of a high performance 40-nm-gate carbon nanotube field-effect transistor. 63rd Device Res Conf Digest 2005 DRC’05 2005, 1: 113–114.
Ilani S, Donev LA, Kindermann M, McEuen PL: Measurement of the quantum capacitance of interacting electrons in carbon nanotubes. Nat Phys 2006, 2: 687–691. 10.1038/nphys412
Heller I, Kong J, Williams KA, Dekker C, Lemay SG: Electrochemistry at single-walled carbon nanotubes: the role of band structure and quantum capacitance. J Am Chem Soc 2006, 128: 7353–7359. 10.1021/ja061212k
Rahmani M, Ahmadi M, Karimi H, Kiani M, Akbari E, Ismail R: Analytical modeling of monolayer graphene-based NO2 sensor. Sens Lett 2013, 11: 270–275. 10.1166/sl.2013.2742
The authors would like to acknowledge the financial support from UTM GUP Research Grant (vote no Q.J130000.2623.09J21) and Fundamental Research Grant Scheme (vote no R.J130000.7823.4F247 and R.J130000.7823.4F314) of the Ministry of Higher Education (MOHE), Malaysia. The authors also acknowledge the Research Management Centre (RMC) of the Universiti Teknologi Malaysia (UTM) for providing excellent research environment to complete this work.
The authors declare that they have no competing interests.
AHP designed and performed the device modeling and simulation work, analyzed the data, and drafted the manuscript. MLPT, MTA, and RI supervised the research work, and MR assisted with the carbon nanotube device modeling. MLPT proofread the manuscript, and HCC improved the quality of the figures through MATLAB simulation. MLPT and CSL provided the funding for the research. All authors read and approved the final manuscript.