Analytical modeling of glucose biosensors based on carbon nanotubes
© Pourasl et al.; licensee Springer. 2014
Received: 3 December 2013
Accepted: 7 January 2014
Published: 15 January 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.
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 .
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 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
Average RMS errors (absolute and normalized) in drain current comparison to the simulated and measured data for various glucose concentration
Absolute RMS errors
Normalized RMS errors (%)
0 (with PBS)
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.
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.
- Wolfbeis OS: Fiber-optic chemical sensors and biosensors. Anal Chem 2008, 80: 4269–4283. 10.1021/ac800473bView ArticleGoogle Scholar
- Diamond D: Principles of Chemical and Biological Sensors. New York: Wiley; 1998.Google Scholar
- Sandhu A: Glucose sensing: silicon’s sweet spot. Nat Nanotechnol 2007. 10.1038/nnano.2007.2 10.1038/nnano.2007.2Google Scholar
- 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.View ArticleGoogle Scholar
- 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/jp902830zView ArticleGoogle Scholar
- 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. 10.1088/0957-4484/21/16/165501View ArticleGoogle Scholar
- 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/jp211201bView ArticleGoogle Scholar
- 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/ac100036pView ArticleGoogle Scholar
- 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.2010.08.070View ArticleGoogle Scholar
- 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.Google Scholar
- 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/ma6031011View ArticleGoogle Scholar
- 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.201200383View ArticleGoogle Scholar
- 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-8-316View ArticleGoogle Scholar
- 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.2012.04.044View ArticleGoogle Scholar
- 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)00183-5View ArticleGoogle Scholar
- Sotiropoulou S, Chaniotakis NA: Carbon nanotube array-based biosensor. Anal Bioanal Chem 2003, 375: 103–105.Google Scholar
- 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.2003.10.019View ArticleGoogle Scholar
- Guisan JM: Immobilization of Enzymes and Cells. 2nd edition. Totowa: Humana Press; 2006.View ArticleGoogle Scholar
- Chaniotakis NA: Enzyme stabilization strategies based on electrolytes and polyelectrolytes for biosensor applications. Anal Bioanal Chem 2004, 378: 89–95. 10.1007/s00216-003-2188-3View ArticleGoogle Scholar
- Skoog DA, West DM, Holler FJ: Fundamentals of Analytical Chemistry. 5th edition. Philadelphia: Saunders College Publishing; 1988.Google Scholar
- Grieshaber D, MacKenzie R, Vörös J, Reimhult E: Electrochemical biosensors-sensor principles and architectures. Sensors 2008, 8: 1400–1458. 10.3390/s8031400View ArticleGoogle Scholar
- 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.2012.257View ArticleGoogle Scholar
- 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.2012.189View ArticleGoogle Scholar
- 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.2010.03.003View ArticleGoogle Scholar
- 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.View ArticleGoogle Scholar
- 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. 10.1116/1.3002386View ArticleGoogle Scholar
- 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.2009.025299View ArticleGoogle Scholar
- 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.2010.05.008View ArticleGoogle Scholar
- 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.Google Scholar
- Anantram M, Leonard F: Physics of carbon nanotube electronic devices. Rep Prog Phys 2006, 69: 507. 10.1088/0034-4885/69/3/R01View ArticleGoogle Scholar
- Tan MLP: Device and circuit-level models for carbon nanotube and graphene nanoribbon transistors. Thesis. Cambridge: University of Cambridge, Department of Engineering; 2011.Google Scholar
- 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-7-467View ArticleGoogle Scholar
- Tan MLP: Long channel carbon nanotube as an alternative to nanoscale silicon channels in scaled MOSFETs. J Nanomater 2013, 2013: 831252.View ArticleGoogle Scholar
- 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.View ArticleGoogle Scholar
- 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/nphys412View ArticleGoogle Scholar
- 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/ja061212kView ArticleGoogle Scholar
- 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.2013.2742View ArticleGoogle Scholar
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