Nanobiosensors are devices which incorporate nanomaterials to detect miniscule quantities of biological and chemical agents. The authors have already developed a novel bionanosensor (BNS) for quick, efficient, and precise detection of bacterial pathogens using the principles of CNT-DNA interaction and DNA hybridization. The detection ability of the (BNS) was observed to be independent of the device resistance. Two new methods (low-pass filter (LPF) and curve fitting (CF)) were developed for better analysis of the BNS. These methods successfully model the BNS. Evidence is provided to elucidate the success of the model, which can explain the DNA hybridization on the sensor surface. These models successfully demonstrated the detection of DNA hybridization versus nonhybridization. Thus, the models can not only help in better and efficient design and operation of the BNS, but can also be used to analyze other similar nanoscale devices.
Since the advent of nanotechnology, scientists and engineers have looked forward to the challenges of designing and fabrication of efficient and reliable methods to detect very minute quantities of biological and chemical agents. A nanobiosensor is defined as a device which incorporates nanomaterials for detecting microscopic quantities of biological/chemical agents by measuring changes in the properties of the device [
The BNS (also sometimes referred as the “sensor”) were fabricated with multiwalled carbon nanotubes (MWCNT) mats and had a range of resistances. The resistance is dependent on the number of carbon nanotubes. However, the number of carbon nanotubes on each sensor is unknown, as exhibited in SEM micrograph (Figure
(a) Photographic image of the BNS depicting 3 sensors which are the black spots in the image. (b) SEM-micrograph of the BNS. This micrograph is of the black regions of (a).
These BNS were driven by the frequency generator ( Phillips-PM 5138) in the range of 10 Hz to 10 MHz with input voltage 1 Volt peak-to-peak and the corresponding output was measured on the oscilloscope (Phillips-PM 3377). The voltage gain was measured as a function of frequency by a three-step process. All the measurements were calibrated with reference to the substrate, contacts, and curing conditions. Initially, the gain-frequency response was measured for the bare sensors. Then, the same measurement was made with the sensor primed with the single stranded DNA primer. This is referenced in this paper as the base data. The gain was again measured once the complimentary DNA strand was added. This is the detection stage of the BNS and is referenced in this paper as hybridization stage.
In Figure
Gain-frequency response for sensors having resistance from 70 Ω to 11.52 KΩ.
Figure
This characteristic graph also leads us to classify the BNS into two categories based on their resistance. The low-resistance sensors have been classified as devices having resistances of 510 Ω or lower. The high-resistance sensors are devices having resistance value of 800 Ω or higher.
Modeling is a simplified representation of the sensor. It helps to understand the factors responsible for sensor functionality. It allows the prediction of the sensor response, if a certain parameter is changed, and also aids in the improvement of the overall performance. In order to model a sensor, the electronic circuit simulation technique has been used. Circuit simulation is reasonably inexpensive, easily available and not difficult to use, and it also provides fairly accurate and very fast simulation compared to nonelectrical simulation methods. Multidisciplinary processes are complex as it involves transformation from one domain to another (e.g., bond graph simulation, which is a graphical representation of a dynamical system). Thus, it is always beneficial to have the systems simulated under one domain (i.e., circuit simulation).
The sensors have been molded based on two different resistance ranges (low and high resistances) by two different methods, both of which have yielded very similar results. In the first method, the sensor based on a low-pass filter (LPF) using MULTISIM (LPF model) was modeled, while in the second method, the data was mathematically fitted, and then, equations were solved for the circuit parameters using MATLAB (curve fitting model-CF model).
As observed, the characteristic curves for all the sensors relate to the characteristics of a LPF [
From the observed characteristics, curve frequency “
(a) Comparison of the gain-frequency response of a sensor modeled low resistance (219 Ω resistance) with the actual experimental data. (b) Comparison of the gain-frequency response of a sensor modeled high resistance (11.52 KΩ resistances) with the actual experimental data. The “sensor” implies the experimental data, and the “model” implies modeled/simulated data using MULTISIM.
The RC circuit model of the 219 Ω sensor is demonstrated in Figure
MULTISIM model of sensor having resistance 219 Ω.
Modeling other sensor resistances with the LPF model has also shown a similar response with the experimented data.
After successfully developing the modeling technique, the sensors were examined for their detection capacity. They were examined using deoxyribonucleic acid (DNA) primers to determine if the model can assist in the determination of DNA hybridization. DNA is a double-stranded biological molecule that has instruction about genes for the biological development of all cellular forms of life [
Single stranded DNA is known to comfortably wrap around CNT (carbon nanotube) [
The final single strand of DNA (R-DNA) as well as the F-DNA (which was added in the priming process) needs to be delivered in an ionic medium, known as buffer, to keep the DNA intact. For every sensor, the base data was gathered with the sensor being primed by the buffer but without any DNA strands. This is shown in Figure
(a) The gain versus frequency plot for a low-resistance sensor detecting the DNA hybridization process. It can be observed that the gain of the hybridized sensor is lower than the original (buffer or base). (b) The gain versus frequency plot for a high-resistance sensor in the sensor detecting the DNA hybridization process. It can be observed that the gain of the hybridized sensor is higher than the original (buffer or base).
In Figure
In Figure
Similarly, the plots showing DNA nonhybridization is presented in Figure
(a and b) The gain versus frequency plot for the sensor detecting the DNA nonhybridization process. It can be observed that the gain of the nonhybridized sensor is similar to the original (buffer or base). Each graph represents a sensor with different resistance.
Based on the gain-frequency characteristics, the sensors were classified as the following: Group-(1) low resistance sensors: ranging from 10 Ω to 510 Ω. Group-(2) high resistance sensors: ranging from 800 Ω to 12 KΩ.
Each of these groups was analyzed and modeled by LPF. According to LPF, the capacitance of the equivalent model of the circuit was calculated using a cutoff frequency. As representative example of Group-1, a sensor with 70 Ω bare resistance was chosen. With this cutoff frequency, it was observed that the designed capacitance of the sensor exhibited almost a fivefold increase from 239.3 pF to 1034 pF. This increase is from the base characteristics to the hybridized characteristics of that sensor. Since the Group-2 sensors had a different gain versus frequency characteristics, a 2.52 KΩ bare resistance sensor was chosen as a representative example. In this case the cutoff frequency was iteratively determined from the average of the start and the plateau of the gain behavior. It was also observed that in this case the designed capacitance of the sensor circuit decreases marginally from 18.3 pF to 14.19 pF. To demonstrate the sensing ability of the sensors, nonhybridization DNA data was presented demonstrating that the capacitances of the sensor circuits remained same after adding the R-DNA (nonhybridized).
To authenticate the LPF model, the groups of resistances was alternatively modeled, by a more rigorous and quantitative, curve-fitting model (CF). According to this model, the gain-frequency data was fitted by a second degree polynomial (quadratic) for the Group-1 and Group-2 sensors. This polynomial equation was solved to determine the cutoff frequency which was then subsequently used to design the capacitance equivalent of the sensor circuit. The algorithm for curve fit technique is explained below.
The equation of the gain-frequency relation for the quadratic curve fit is given as follows:
And
Figures
The capacitance calculated from the circuit models (LPF and CF) for a low resistance sensor.
Capacitor (designed) | Capacitor (calculated) | |
---|---|---|
BASE | 239.3 pF | 229.75 pF |
Hybridized DNA | 1034 pF | 1038 pF |
(a) and (b) represent the curve fitting technique applied on the 70 Ω Base and Hybridized sensor, respectively. Equations underneath respective figures represent the gain-frequency relationship. Using the above equations, the capacitance for the sensor with known resistance was calculated and it resulted in a value comparable to the designed capacitance. Table
Figures
The capacitance calculated from the circuit models (LPF and CF) for high resistance sensors.
Capacitance (designed) | Capacitance (calculated) | |
---|---|---|
BASE | 18.3 pF | 18.31 pF |
Hybridized DNA | 14.19 pF | 16.21 pF |
(a) and (b) represent the curve fitting technique applied on the 2.52 KΩ bare and hybridized Sensor, respectively. Equations below the respective figures represent the gain-frequency relationship. Using the above equations, the capacitance for the sensor with known resistance (2.52 KΩ) was calculated, and it was closely comparable with the designed value of capacitance. Table
Similar comparison processes for calculating the capacitances between the LPF and CF models were applied, for the sensors where nonhybridization experiments were performed. Table
(a and b) The capacitance calculated from the circuit models (LPF and CF) for nonhybridizing DNA sensors.
90 Ω resistance | Capacitance (designed) | Capacitance (calculated) |
---|---|---|
BASE | 313 pF | 322 pF |
Nonhybridized DNA | 313 pF | 316 pF |
320 Ω resistance | Capacitance (designed) | Capacitance (calculated) |
---|---|---|
BASE | 58 pF | 58 pF |
Nonhybridized DNA | 58 pF | 58 pF |
On combining the experiments with the modeling parameters, the following were observed.
The low resistance sensors on being hybridized, that is, after receiving second primer (R-DNA), show an increment in the value of capacitance, whereas for very high resistance sensors, there is a decrement in the value of capacitance. Figures
(a) Designed capacitance change from BASE to hybridized DNA for sensor having 70 Ω bare resistance. (b) Designed capacitance change from BASE to hybridized DNA for sensor having 2.52 KΩ bare resistance.
(a) Designed capacitance remains the same from BASE to hybridized DNA for sensor having 90 Ω bare resistance. (b) Designed capacitance remains the same from BASE to hybridized DNA for sensor having 320 Ω bare resistance.
The reason for the different trends in the capacitor change is critical in understanding the operation and performance of a BNS. The change in the capacitance can be understood using the RC circuit model. Figure
RC model for sensor with
After adding buffer; the capacitance for both; very low- and very high-resistance sensor increases [
From the models (LPF and CF) discussed earlier, it was observed that on hybridization, the capacitance of the sensor increases by almost a fivefold for the low-resistance sensors. Hybridization occurs when the complementary primer (R-DNA) is added to the sensor. At this stage the F-DNA, which was already on the surface of the sensor, wrapped around CNTs leaves the host (CNT) and starts bonding with the F-DNA. This creates additional parallel channels of capacitance. Some of these new capacitance channels are quite large, since it is created from the void left over by the F-primer. The extra channel capacitance, is connected in parallel to the earlier capacitances of the base sensor, and hence increases the overall capacitance of the hybridized sensor.
The gain for the high-resistance sensors remains constant over the major frequency domain (after the initial drop), where the measurement was performed. This implies that the impedances of these sensors are not frequency dependent and, therefore, are primarily resistive. Hence in the hybridization stage the unwrapping of the DNA does not cause any significant increase of the capacitance. This is possible only when the additional small capacitance caused by the hybridization is connected in series to the original capacitance of the base sensor. This leads to a small decrease in capacitance of the hybridized sensor which matches with the experimental observation.
However, for the nonhybridization process, the capacitance of R-DNA was similar to that of the buffer. The F-DNA on the MWCNTs will not make any bonds with R-DNA, and as a result, the R-DNA will again wrap around the MWCNT. This will neutralize the capacitance caused by the F-DNA, and hence, the final capacitance is same as the buffer capacitance.
Successful frequency dependent models showing the operation of the BNS were developed. The models were able to analyze data for both low- and high-resistance sensors and hence were independent of sensor resistances. The results from both the electrical circuit models (LPF and CF) were consistent with each another. The CF model is more quantitative and can provide information on the concentration of the hybridized and nonhybridized DNA in a particular sample. Both of these models hypothesized the BNS to be working like an RC circuit with the capacitance playing a major role in the results. Even though the characteristic curves of the low- and high-resistance sensors are very different from each other the sensor performance, it is independent of their resistances. The modeling not only aids in understanding the working principle of the sensor but also enables its efficient design leading to the optimal operation frequency range. These models will be very useful in studying other types of sensors leading to their optimal performance.
The authors wish to acknowledge University of New Haven for providing support and funding for this project.