Difference between revisions of "Team:Technion Israel/Hardware"

Chip redesigned

We designed a new chip with the following improvements:

(a) Reducing the radius of the entry slot will enable a controlled insertion of the sample.
The smaller slot will slow down any flow (for example, flow caused by loading a sample from a syringe). Also, this will fix the diffusion source at a permanent location in the chip for all of our experiments.

(b) Shaping the channel as a funnel in order to concentrate the bacteria even further as they move away from chemo-repellents (from left to right).

(c) A deeper channel will result in a darker shade of color in the same bacterial concentration than in the commercial chip, while reducing the risk of oxygen shortage.

Fig. 1: The geometry of the designed fluidic chip.

The new chip was fabricated in two methods: as a PDMS chip and in a Dolomite 3D printer.

Fabricating the PDMS chip

PDMS is considered the standard for microfluidic fabrication in labs. It is optically clear, and in general, inert, non-toxic, and non-flammable.

The PDMS was then fabricated according to the following steps:

1. Design a two part mold using SolidWorks software- cover and base.
2. Print the mold using Ultimaker 2 Etentended+ 3D printer.
3. Mix the polymer base and curing agent at 10:1 weight ratio, respectively. Then, fill the mold with the mix.
4. Place the mold inside a desiccator to degas for 2 hours.
5. Bake the mold at 70 C for 3 hours.
6. Carefully take off the mold’s cover and then cut out the PDMS chip.
7. Attach the PDMS chip to a thin cover glass (0.3 mm) using silicon glue*.

The following scheme describes the mentioned process:

Fig. 2: PDMS chip fabrication process.

*Traditionally, bonding PDMS to glass is done by plasma treatment. Our 3D printed mold resulted in PDMS chips with relatively rough surface finish, forcing us to use other methods.

Table 1: Bill of materials for PDMS chip

Designing the mold

The mold is comprised of two parts which together create a unique geometry and allow for easier extraction of the PDMS out of the mold.

Printing the mold using Ultimaker 2 Etentended+ 3D printer . This 3d printer was chosen because of its high accuracy (X,Y,Z =12.5, 12.5, 5 micron) and due to the fact that the polymer it uses (PLA) can be heated to relatively high temperatures without changing form (TG=60-65 C). More benefits of 3D printing are the low price and fast manufacturing time: We printed our mold for about 25$, and it took about 6 hours.

Dolomite Fluidic Factory

Results

We were able to make a few prototypes of the PDMS chip. The extraction of the chip was relatively easy and without any visible cracks or deformations. The chip still needed to be punctured in the entry slots, due to spaces between the molds. Also, attaching a glass slide to the PDMS needed to be done carefully, as the glass is thin and brittle.

Fig. 6: PDMS chip prototype.

The fluidic factory 3D printer did not produce us a usable chip. The channels kept collapsing while printing the model. Despite not achieving a usable chip, we believe that this technology shows a lot of promise.

Fig. 7: Fluidic Factory chip.

Quantitative test for bacterial concentration

Principle of Operation

Our system uses a photosensor 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 photosensor 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 Equation 6.

Fig. 1: Schematic diagram 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 2, 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

For a typical low cost LDR, the relationship between the resistance R_LDR of a typical LDR and the light intensity is:

Where I is the light intensity that reaches the photoresistor.

Combining equations 1 and 2 we receive:

By definition:

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


Rearranging Equation 4:

Integrating Equation 5 at Equation 1:

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 photosensor 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 photoresistor 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 of 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 2) 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 photoresistor. When the photoresistor is exposed to high light intensity, its resistance decreases dramatically. Under these conditions, most of the voltage falls on the 1[MΩ] 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 photoresistor. 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 5.

Fig. 5: 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.

Table 1: Bill of materials

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 are 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 to first, 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 plan to improve the quantitative device, by replacing to a more accurate sensor or 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.