Hardware

Quantitative test for bacterial concentration

Principle of Operation

Our system uses a photo sensor to measure the intensity of a light beam transmitted through the chip. The measurement process is as follows: A yellow LED emits light at 585-595 [nm] on the chip, with the bacteria inside absorbing a portion of the light. The light transmitted through the chip reaches the photo sensor which outputs an analog signal. This signal is then translated to a digital signal and fed to the computer. The end result is a graph of the output voltage as a function of time.
The output voltage can be compared to the bacterial concentration as shown in section 3.3.

Fig. 10: Schematic diagram of the quantitative system

Fig. 11:3D Model of the quantitative system

The system requires two measurements. The first measurement is a blank meant to calibrate the system. This measurement is done on a chip containing only motility buffer (control). The second measurement is for the bacterial solution.

To avoid undesired light reflections, we have designed a dedicated black box as shown in Fig 11, to house the chip and the electrical circuits discussed below.

The electrical circuits

Computer data system

The relation between the resistor’s voltage and bacterial O.D.

According to the voltage divider rule, the voltage that falls on the 1[MΩ] resistor V_R is equal to

Equation 1

According to “Emant”, the relationship between the resistance RL of a typical LDR and the light intensity is:

Equation 2

Where Iis the light intensity that reaches the photoresistor

Combining equations 1 and 2 we receive:

Equation 3

By definition:

Equation 4

Where A is the optical density of the sample and I0 is the light intensity emitted from the LED


Rearranging Equation 4

Equation 5

Integrating Equation 5 in Equation 1:

Equation 6

Where I₀ is the light intensity emitted from the LED and A is the optical density of the bacterial concentration inside the chip.

From Equation 6 it can be derived that V_R is expected to decrease as A increases.

System improvements

Initially the photo sensor we intended to use was a photodiode. Since the photodiode is relatively big it was difficult to fix its position. Thus, we replaced it with a photo resistor which is smaller and relatively sensitive to 600 [nm] wavelength.

In addition, before building the final system we used a battery as a voltage source and a USB data acquisition from NI to convert the analog signal into a digital one. In order to improve the system we replaced those two components with an Arduino controller that can serve as a constant voltage source and as a converter simultaneously.

As mentioned before the chip and the two electrical circuits were placed in a dark box (As shown in Fig XX) to avoid undesired light scattering. All the sides of the chip were darkened as well so the light can be transmitted only through the transparent channel.

To improve the dynamic range of the photoresistor we connected a resistor in series with the photo resistor. When the photoresistor is exposed to high light intensity, its resistance decreases dramatically. Under these conditions, most of the voltage falls on the resistor. Since

V_R increases with R. As we wanted the maximum voltage falling on the resistor to be 5v (the total voltage), we chose a resistor of 1[MΩ].

Finally, if the light intensity that originates from the LED is too high, it can lead to the saturation of the photo resistor. To be able to tune the light intensity of the LED, a potentiometer was added to the LED circuit, to adjust the desired resistance which produces the optimal light intensity.

Testing the system

As can be deduced from the mathematical equations the voltage is expected to decrease as the optical density increases. For that purpose we prepared bacterial solutions in motility buffer at different concentrations and loaded them to the system. the results are displayed in Fig XX.

Fig 14 The output voltage for different values of O.D as a function of time.

As can be deduced from the graph the output voltage converges after 88 [sec] which is much less than the time required for cluster formation (about 15 minutes). Thus, the system indeed can be used with FlashLab for real time detection. Moreover, the dynamic range of the system is relatively wide (0-3v), giving us the ability to detect a variety of bacterial O.D levels. In addition, the difference between the outputs obtained for O.D 0.757 and O.D 0.653 is much bigger than the system error’s measurement. Hence, it can be concluded that the system is relatively precise.

Overview

FlashLab, although a successful detection tool, has several drawbacks. By redesigning the fluidic channels and engineering a more sensitive measurement system, we will be able to get a more reliable, accurate and user friendly device.

Our preliminary testing supports those claims. Showing we can detect small differences in bacterial concentration, that is not noticeable otherwise. Our prototype was completed by designing an easy to use user interface, and making a more reliable and cost effective system. We believe this device can have a real world, commercial potential.

In the future,we plan first to expand our testing and improve the chip even more. Ideally, to design a chip that is compatible with different tests (for fast/slow moving, high concentrations of repellent, different temperatures etc.). Second, we plant to improve the quantitative device, by replacing to a more accurate sensor or adding by implementing a signal processing algorithm for better results. Third, according to the model we developed, there is a clear correlation between the repellent gradient and the bacterial concentration. The system allows to get a quantitative estimation of the bacterial concentration, so theoretically the results can be correlated to the repellent/attractant concentration.