# Electrochemical Detection of Penicillin G Using Molecularly Imprinted Conductive Co-Polymer Sensor
## 3 Results and Discussion
### Sensor's Performances
#### 3.2.1 Sensitivity  
Classical EIS measurements are performed with them samples electrically linked to a potentiostat. This implies the use of a 3-electrodes system. The counter and working electrodes are linked to the two electrodes of the IDE samples (see Figure 3). As we do not need a referenced value for the potential, the reference electrode is short-circuited to the counter electrode. The measured frequencies are taken in 40 points going from 16 mHz to 1 mHz (voltage amplitude = 10 mV, 0 V bias). They are performed in 40 mL of a 0.5 PBS solution at 25 \({}^{\circ}\)C. Each PenG addition is performed after a measurement is done, in the PBS solution, which is stirred strongly for a minute, while the sensor is emerged. Later, the sensor is immersed again in the PBS solution and left in contact with that solution without agitation for 15 min, in order to allow the PenG to diffuse on and in the polymer matrix.  
The EIS curves show indeed a clear signal variation. An example of a sensor is presented in Figure 6. The graphs show the modulus (on the upper part) and the phase (on the lower part) of the impedance. The MIP results are shown on the left and the corresponding NIP results are shown on the right. On these graphs, each data curve corresponds to a PenG concentration increase of 12.5 ppb. At 63 Hz for the magnitude and 1000 Hz for the phase, significant signal variation can be observed, and it seems quite linear. These chosen frequencies are highlighted with the red line in Figure 6.  
To make the choice of the frequency more reproducible than a simple observation, an automatic analysis is performed thanks to a MatLab code designed for this analysis at all frequencies between 20 mHz and 100 kHz. Within this frequency range, the program will therefore define the frequency for which the relative variation of the phase and modulus values is the most important between the first and the last measurement. A frequency will  
Figure 5: SEM images of a polypyrrole MIP sample taken after the extraction step (**a**) magnification 35,000 times; (**b**) magnification 100,000 times.  
then be chosen in each case, and the values observed for these frequencies will then be compared as a function of the concentration.  
The response signal variation is presented in percent and is calculated as follows:  
Signal response variation (in percent) = (R - R\({}_{0}\))/R\({}_{0}\cdot 100\)  
With R\({}_{0}\), the first response signal before any addition of analyte, and R, the response signal at a given concentration.  
To ensure this, it is important to draw a graph of the signal response versus the PenG concentration, as it is presented in Figure 7. The curves are obtained thanks to the MATLAB code explained earlier. There, it is possible to observe the difference between the response signal of MIP and NIP. Here, it can clearly be seen that the MIP give significantly better results than the NIP, both in phase and in modulus. This demonstrates the MIP effect and its efficiency as well as the fact that the sensor is working properly.  
Figure 6: EIS modulus (**above**) and phase (**below**) graphs for MIP (**left**) and NIP (**right**) sensors while increasing the penicillin G concentration. Chosen frequencies are highlighted thanks to the red line.  
Figure 7: Sensor’s magnitude (**left**) and phase (**right**) responses versus penicillin G.