1 Introduction

CNT for electrochemical biosensors are predominantly used in two different modes such as amperometric (oxidase or dehydrogenase) enzyme electrodes based on the accelerated oxidation of NADH or hydrogen peroxide and based on the use of CNT molecular wires for achieving efficient electron transfer to enzyme redox centers. Secondly as bioaffinity devices (particularly DNA biosensors) based on the enhanced detection of the product of the enzyme label or of the target guanine and the use of CNT support platforms (Wang 2005). Lin et al. (2004) developed amperometric biosensors based on CNT-nanoelectrode ensembles (NEEs). Besteman et al. (2003) reported on a SWCNT transistor as a conductivity glucose biosensor. The accelerated electron transfer reaction of hydrogen peroxide at the CNT-based paste electrode offered a rapid low-potential (−0.10 V) detection of the substrate (Wang 2005). Koehne et al. (2003) fabricated low-density CNT arrays on silicon chips using a bottom-up approach, involving lithographic patterning, metallization of the electrical contacts, deposition of a catalyst and CNT growth by plasma enhanced chemical vapor deposition (Wang 2005). Wang and Musameh designed a needle microsensor for amperometric monitoring of glucose based on packing of a binderless CNT-GOx composite within a 21-gauge needle (Wang and Musameh 2003). The resulting microsensor offered a low-potential highly selective and sensitive detection of glucose. DNA biosensors, based on nucleic acid recognition processes, are rapidly being developed towards rapid, simple and inexpensive testing of infectious diseases such as cancer. Electrochemical hybridization biosensors rely on the immobilization of a single-stranded (ss-) DNA probe onto the transducer surface, the electrical signal is a resultant of duplex formulation of DNA strands (Gooding et al. 2002). SWCNT and MWCNT are used as biosensors with DNA strands, as they enhance detection of target guanine with CNT carrier platforms. Surface-confined MWCNT facilitate the adsorptive accumulation of the guanine nucleobase and enhances its oxidation signal (Joseph Wang et al. 2003). The amplification of the guanine signal with label-free electrical detection of DNA hybridization constitutes CNT biosensors with DNA strands. Highly sensitive bioelectronic protocols for detecting of proteins and DNA based on the coupling of several CNT-derived amplification processes for recognition and transduction events (Wang 2005). The detection of DNA and proteins down to 1.3 and 160 zmol, respectively, in 25–50 mL samples were demonstrated (Wang et al. 2004). Cai et al. (2003) described an indicator-free AC impedance measurement of DNA hybridization based on DNA probe-doped polypyrrole film over a MWCNT layer.

2 Prostate cancer

India is a country and is termed to be one of the developed countries in the near future with population of over 1 billion. The middle class population in India is increasing with diverse food habits and adoption to western culture. With the transition in food habits and life style there is an increase in non-communicable diseases (NCD) such as cancer and coronary heart disease (CHD) and other epidemic diseases affecting the Indian population. Cancer rates in India are increasing with development progress. According to WHO, cancer rates in India are considerably increasing with the cancer such as lung, oesophagus, stomach and larynx affecting males and cervix, breast, ovary and oesophagus affecting females. Oral cancer in males and females is 12.8 and 7.5 %, respectively, oesophagus cancer is 5.7 and 2.8 % in male and female, respectively, breast cancer in females is 19.2 %. Cancer that affects only males are prostate cancer and only females is ovary, breast and cervix. Prostate cancer in India is growing at a rate of 4.6 %, breast, ovary, cervix and endometrial cancer in females is at rate of 19.1, 4.9, 30.7 and 1.7 %, respectively (http://www.indiastat.com/health/16/diseases/77/cancer/ 1781). Cancer of the female reproductive tract has a high incidence amongst Indian women. Cervical cancer is the most common cancer among women with approximately 100,000 new cases occurring each year. Prostate cancer is another major cancer affecting men and is also growing at a very significant rate. Prostate cancer is a form of cancer that develops in the prostate (Lilja et al. 2008), a gland in the male reproductive system. Prostate cancer is curable when detected in the initial stages. There are several drugs that can be used to cure prostate cancer. This also involves routine doctor check-ups, including serial Prostate Specific Antigen (PSA) blood tests (usually every 3 months to 1 year). One of the major challenges in treatment of prostate cancer is prostate cancer detection in their early stages. Symptoms of prostate cancer are mild pain in the male reproductive organ and blood samples during urination. Due to negligence, lack of awareness and shyness patients do not report or consult doctor during initial stages. Hence prostate cancer grow and reach critical stages thus causing deaths, as it becomes very difficult to cure cancer when it reaches its critical stage. PSA is an enzyme that is contained in the blood stream; PSA is produced in the ducts of the prostate. If the PSA concentration level in the serum is in the range 0–4 ng/ml, the patient is considered as normal. PSA is used as a biomarker that can detect prostate cancer, however, non cancer diseases such as benign prostatic hyperplasia (BPH), can also result in increased release of PSA into the circulation in blood stream (Semin 1999). The development of ultrasensitive FET (field-effect transistor) biosensor that is affordable to distinguish between different isoforms of PSA, such as the ratio of free PSA to bound PSA, is inevitable for future disease diagnosis(Chi-Chang 2012). The use of PSA testing has aided the prediction of prostate cancer risk and treatment outcome. It is important to develop an ultrasensitive and selective biological sensor for PSA detection. An ultrasensitive biological detection system that allows the early detection of prostate cancer is expected to improve preventative healthcare. The most widespread techniques for detecting prostate cancer are based on the enzyme-linked immunosorbent assay (ELISA) (Moore et al. 1999; Tang et al. 2006). Recently, biomolecule sensors based on quasi one-dimensional semiconductor nanostructures, such as nanotubes, nanowires and nanobelts, have attracted considerable attention because of their distinct electrical, optical and magnetic properties(Patolsky et al. 2006; Cheng et al. 2008; Zhang et al. 2010; Shen and Chen 2009). The large surface-to-volume ratios and high selective binding of charged biomolecule onto these nanostructure surfaces can result in significant changes of electronic conductance in the channel of the nanostructure (Lin et al. 2008; Hsiao et al. 2009).

2.1 Biosensors for prostate cancer

Biosensors are analytical tools for the analysis of bio-material samples to gain an understanding of their bio-composition, structure and function by converting a biological response into an electrical signal. The analytical devices composed of a biological recognition element directly interfaced to a signal transducer which together relates the concentration of an analyte (or group of related analytes) to a measurable response (Wang 2005). Figure 1 shows the basic biosensor model. The sensor consists of a bio-receptor that reacts to the presence of cell/molecule/virus present in an analyte. Bio-receptor triggers the transducer that converts the biological changes to electrical signal that can be detected.

Fig. 1
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Transducers used in biosensor development (Wang 2005)

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Biosensor model need to distinguish between the analyte under investigation and similar molecule, with quick response time. The sensor model should have good selectivity and specificity for real time measurements. The sensor should be designed to be cost effective, reliable and interoperable over single platform. The data provided by the sensor should be continuous with any changes in analyte concentration and should be reusable. Several nano devices have been used as biosensors. Some of the basic Biosensing techniques are Fluorescence, DNA Microarray, SPR Surface plasmon resonance, Impedance spectroscopy, SPM (scanning probe microscopy, AFM, STM), QCM (quartz crystal microbalance), and SERS (surface enhanced raman spectroscopy), Electrochemical.

3 Carbon nano tubes

Resources such as the nanoHUB on the internet which is funded by the National Science Foundation (NSF) act as a gateway for all information related to nanotechnology. It consists of over 235 simulation tools for nanoelectronics, nanophotonics, etc. Some of the prominent ones include SCHRED, Quantum dot lab, Bulk Monte Carlo tool, Crystal viewer, Band structure lab and Ninithi to name a few. Others such as Ascalaph designer, CoNTub, Nanorex, NEMO 3-D, TubeASP, Tubegen are some of the tools used for modeling of nano structures. Tools such as Hspice from Synopsys allow modeling of CNTFET by using model files, for example, by using CNT model files available from the Stanford University Nanoelectronics Group. Ninithi can be used to visualize the 3D molecular geometries of graphene/nano-ribbons, carbon nanotubes (both single wall and multi-wall) and fullerenes. Ninithi can also be used to simulate the electronic band structures of graphene and carbon nanotubes. Ninithi is open source software for research in the field of nanotechnology. This tool is developed by Lanka Software Foundation with assistance from Sri Lanka Institute of Nanotechnology. The tool is helpful to visualize and analyze carbon allotropes in nanotechnology namely Graphene, Carbon nanoribbons, Carbon nanotubes (including multiwall carbon nanotubes) and Fullerenes in 3D view of the molecular structures. It is also possible to visualize the electrical properties of graphene and carbon nanotubes using Ninithi. Entering the properties of inputs M, N and length in angstroms in the settings tab, CNT can be modeled. An example with length of 5 Å with M = 3 and N = 3 provides the structure as shown in Fig. 2, length of 6 Å with M = 5 and N = 5 provides the structure as shown in Fig. 3. In a CNT the distance between two nearest neighboring carbon atoms is 1.42 Å.

Fig. 2
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Graphene structure

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Fig. 3
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Carbon nanoribbon structure

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A carbon nanotube is carbon nanoribbon rolled around an axis perpendicular to the resultant chiral vector. Figures 4 and 5 shows the single walled and multiwall CNT rolled using carbon nanoribbon. The electrical properties are obtained by checking the ‘Show electrical properties’ checkbox.

Fig. 4
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Carbon nanotube structure

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Fig. 5
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Multi wall carbon nanotube

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Electrical properties of nanotubes are obtained using the Ninithi tool. The bonding and anti-bonding energy levels can be obtained as shown in Fig. 6. The electrons within the nuclei of two atoms are placed into the bonding orbitals and electrons which are mostly outside the nuclei of two atoms are placed into anti-bonding orbitals.

Fig. 6
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Electrical properties (E-k diagram) of carbon nanotube

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The E-k diagram describes the energy-wave momentum relationship for carriers and the “bandstructure”. Subbands closest to the equilibrium Fermi level (denoted E = 0 here) are of particular interest, since they are usually the levels giving rise to current. In CNT bands, these subbands are extracted and is output as “lowest subbands”. Figure 7 shows the lowest sub band in the E-k diagram.

Fig. 7
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Lowest subband in E-k diagram for CNT

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CNTs can be metallic or semiconducting depending on its chirality (m,n). If the CNT’s chirality difference (m–n) is a multiple of 3 (includes zero), the CNT is metallic; otherwise, it is semiconducting. Figure 8 shows the overlap of energy bands in the E-k diagram and is the property exhibited by metallic CNT.

Fig. 8
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Metallic CNT

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Figure 9 shows the single walled CNT with chiral vector of 5.2 of length 30 Å. The C–C bond length in Angstroms is 1.42, and C–C transfer energy in eV is 3.013.

Fig. 9
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Semiconducting CNT

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Figure 10 shows the E-k energy band diagram of semiconducting CNT. Magnetic field introduces a phase factor to the electron wave function in the circumferential direction. As a result, the electronic properties of a nanotube can be modulated by a magnetic field. The energy spectrum is a plot of energy (E) versus wave vector (k). As the magnetic flux increases the energy-band structure of the nanotube oscillates from that of a metal to that of a semiconductor.

Fig. 10
figure 10

Energy band diagram of SNCWT

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Three SCNT each of chiral vectors (5.2, 15.3 and 27,6) and length of 30 Å are combined together to design multi walled CNT as shown in Fig. 11. Young’s modulus is 1 TPa for CNT as compared with Aluminum which is of 70 GPa, the strength to weight ratio is 500 times greater than Aluminum, the maximum strain is approximately 10 % higher than any other material, thermal conductivity is approximately 3,000 W/mK in the axial direction. The electrical conductivity of CNT is almost six times higher than that of copper, the high carrying capacities have made CNT more attractive for biosensor.

Fig. 11
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Multi Wall CNT structure

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4 Carbon nano tubes with DNA strands

DNA is a polymer. The monomer units of DNA are nucleotides and the polymer is known as a “polynucleotide.” Each nucleotide consists of a 5-carbon sugar (deoxyribose), a nitrogen containing base attached to the sugar and a phosphate group. There are four different types of nucleotides found in DNA, differing only in the nitrogenous base. The four nucleotides are given one letter abbreviations as shorthand for the four bases: A is for adenine, G is for guanine, C is for cytosine and T is for thymine. The deoxyribose sugar of the DNA backbone has 5 carbons and 3 oxygens. The carbon atoms are numbered 1′, 2′, 3′, 4′, and 5′ to distinguish from the numbering of the atoms of the purine and pyrmidine rings. The hydroxyl groups on the 5′- and 3′-carbons link to the phosphate groups to form the DNA backbone. Deoxyribose lacks an hydroxyl group at the 2′-position when compared to ribose, the sugar component of RNA. A nucleoside is one of the four DNA bases covalently attached to the C1′ position of a sugar. The sugar in deoxynucleosides is 2′-deoxyribose. The sugar in ribonucleosides is ribose. Nucleosides differ from nucleotides in that they lack phosphate groups. The four different nucleosides of DNA are deoxyadenosine (dA), deoxyguanosine (dG), deoxycytosine (dC), and (deoxy) thymidine (dT, or T). A nucleotide is a nucleoside with one or more phosphate groups covalently attached to the 3′- and/or 5′-hydroxyl group(s). The DNA backbone is a polymer with an alternating sugar-phosphate sequence. The deoxyribose sugars are joined at both the 3′-hydroxyl and 5′-hydroxyl groups to phosphate groups in ester links, also known as “phosphodiester” bonds. DNA is a normally double stranded macromolecule. Two polynucleotide chains, held together by weak thermodynamic forces, form a DNA molecule (Hallick et al. 2013).

The discovery of carbon nano tubes (CNTs) plays an important role in development of electrochemical DNA sensors (Rubianes and Rivas 2005). Various CNT based electrochemical are developed because the combination of unique electrical, thermal, chemical, mechanical and 3D spatial properties of CNTs with DNA hybridization offers the possibility of creating DNA bio sensors with specificity, simplicity, high sensitivity and multiplexing. CNT enables immobilization of DNA molecules and also used as powerful amplifier to amplify signal transduction of hybridization (Pingang et al. 2006). Two types are generally used to immobilize the CNT on electrodes—aligned and non-aligned. Two approaches are generally used for the immobilization of bio molecules onto CNTs that are non covalent attachment (physical absorption) and covalent binding (some cross linker agents (1-ethyl–3-3 dimethylaminopropyl) carbodilimide hydrochloride (EDC)/Nhydroxysuccinimide (NHS)] or affinity binding (avidin–biotin interaction). The application of arrayed CNT into DNA chip requires small amount of sample and development of CNT base biosensor has an important role in DNA based diagnostics in hospitals or at home (Pingang et al. 2006). The MCDNA tool developed by Bulyha and Heitzinger (2009) calculates ionic concentration profiles in electrolytes between two charged surfaces. A voltage difference between the two electrodes can be applied and one of the electrodes can be functionalized with different types of biomolecules. The leading applications are the surfaces of BioFETs (biologically sensitive field-effect transistors). The simulations are 3D Metropolis Monte-Carlo calculations in the constant-voltage ensemble. One of the surfaces can be functionalized with PNA (peptide nucleic acid), ssDNA (single-stranded DNA) or dsDNA (double-stranded DNA) oligomers. In addition to the concentration profiles, other quantities of interest such as the chemical potential, the surface charge density and the dipole moment density of the boundary layer at the functionalized surface are calculated. The whole system is electrically neutral and periodic in the two coordinate directions of the parallel surfaces. The density of the biomolecules at the surface can be adjusted via their distance. The PNA and DNA oligomers and their linkers are modeled as impenetrable cylinders with two hemispheres of the same radius at the top and at the bottom. PNA oligomers are modeled by uncharged cylinders and ssDNA and dsDNA oligomers carry the charges of the phosphate groups of the backbone on their outside just as in B-DNA oligomers. The linkers are orthogonal to the surface so that they touch the surface. The upper hemisphere of the linker overlaps with the lower hemisphere of the oligomer and acts as a flexible joint. Hence the oligomers can be rotated with respect to the surface. The length of the linkers and the number of nucleotides in the oligomers, the number of oligomers in the simulation box, the distance between them and their angle with respect to the surface plane are parameters of the simulation. The ionic concentration and the distance between the two electrodes must be specified. The distance between the two electrodes, i.e., the height of simulation box and the applied voltage yield the electric field in the simulation box. The accuracy of the Monte-Carlo simulations is determined by the algorithmic parameters. The default algorithmic parameters yield fast results with relatively small noise.

5 Results and discussion

In this work, Monte Carlo simulator that can model and biosensor developed by Alena Bulyha, Clemens Heitzinger of University of Vienna and Purdue University for simulation of ionic concentration profiles at charged boundaries functionalized with DNA oligomers is used. Number of molecules in one direction is set to 3 that yield 3 × 3 molecules in the simulation cell, the tool supports a maximum of 5 × 5 molecules. Length of nucleotides or oligomers is set to 20, the tool supports a maximum length of 30 A. The distance between molecules is set to 8 nm, the maximum distance between molecules can be 15 nm. The angle with respect to x–y plane is set to 90 which is an integer number 0–90. The physical parameters for simulation such as ionic bulk concentration of Na+ and Cl− ions (in mol/l) are set to 0.01 molar. The applied voltage between the two electrodes at the top and the bottom electrodes is set to −50 mV, surface charge density of the bottom surface is set to −0.2/nm2 and the minimum half height H of the simulation cell is set to 50 nm. The simulation tool also requires to set algorithmic parameters, the number of iterations are set to 10, number of steps in Monte carol simulations is set to 2000 and number of movements is set to 6000. Figure 12 shows the experimental setup for validating the performance of CNT based DNA marker biosensor. The CNT which is cylindrical in shape consists of 100 nodes, with 100 nm length, 10 nm diameter is first verified for its functionality without biomarkers or DNA strands. Potential difference of less than 2 V to a maximum of 20 V is applied between the Gate and Ground terminal of CNT. With the increase potential difference from 0 to 20 V, the gap at the centre of CNT reduces from 5 to 0 nm.

Fig. 12
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CNT structure test setup

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Three different DNA biosensors are simulated using the experimental setup. Peptide nucleic acid, single-stranded DNA and double-stranded DNA are simulated to obtain the concentration profile and chemical potential with respect to height of simulation cell and iteration, respectively. Figure 13 shows the simulation box to validate the DNA strands, the box contains PSA molecules, at low ionic concentrations. A single molecule in the simulation box, is placed at the center of the lower electrode, if there are large number of molecules, they are arranged in a square grid and each molecule is centered in its grid cell. Each oligomer is bound to the surface by a linker.

Fig. 13
figure 13

Simulation box for DNA based biosensors

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The concentration profile in ssDNA is 0.075 M at simulation cell height of 45 nm. As the cell height reduces the concentration reaches saturation. The concentration profile varies by a factor of 0.05 in less than 5 nm. Graphs plotted in Figs. 14, 15 and 16 are obtained by using Monte Carlo tool.

Fig. 14
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ssDNA biosensor concentration profile and chemical potential

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Fig. 15
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dsDNA biosensor concentration profile and chemical potential

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Fig. 16
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PNA biosensor concentration profile and chemical potential

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Concentration profile in dsDNA is 0.15 M at 45 nm box size, and reaches saturation at 40 nm box height. Table 1 shows the dsDNA performance after MC simulation. The surface charge density is 0.080169 q/nm2,

Table 1 dsDNA performance parameters
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PNA biosensor concentration profile and chemical potential is shown in Fig. 15, the maximum concentration is at 0.05 at 45 nm box height. Surface charge density of PNA biosensor is −0.071615 q/nm2. Table 2 shows the performance parameters of PNA biosensor.

Table 2 PNA performance parameters
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The concentration profile is 23.4656 M for dsDNA and is found to be superior as compared with ssDNA and PNA for length of oligomers of 12 nm, distance between molecules of 8 nm and linker length of 1 nm.

6 Conclusion

In this paper, properties of SWSNT and MWCNT are analyzed using Ninithi software, electrical properties and energy band diagram are used to analyze the semiconducting and metallic properties of CNTs. ssDNA and dsDNA biosensor models are analysed for their performance using MC biosensor lab and compared with PNA biosensor. The results shows that the dsDNA is suitable as biosensor for detecting prostate cancer.