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Revision as of 16:52, 19 October 2016
Introduction
We developed our first FlashLab prototypes around on the commercial fluidic chip -IBIDI sticky–Slide I Luer 0.8. This chip is designed mostly for performing cell culture experiments with a custom specific bottom.
Fig. 1: The geometry of the commercial fluidic chip.
Table 1: Bill of materials for commercial chip
The chip is made of plastic and the bottom is closed by attaching a glass slide to it.
Fig. 2: Setup of commercial fluidic chip.
The commercial chip worked for preliminary testing but was not idle for our uses: The entry slots are
relatively wide, making it difficult to load the sample in a uniform and even fashion.
This affects the diffusion of the chemo-repellent in the channel and reduces the overall accuracy of
the device. Also, the channel is relatively shallow, forcing the use of a high concentration of bacteria
to get a visible signal, this proves to be a problem as storing a large amount of bacteria in a confined
space might cause oxygen shortage that will harm bacterial motility.
We devised new solutions to confronts those problems:
- Design a novel chip, based on the commercial chip but with unique changes to its geometry for improved performance.
- Design a quantitative test, a device built especially for detecting a change in bacterial concentration in the chip. This device is much more sensitive than the naked eye.
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 place 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
| Item Number | Part Name | Description | Quantity |
|---|---|---|---|
| 1 | Mold | PDMS Fluidic Chip Mold | 1 |
| 2 | Polymer Base and Curing Agent | word PDMS Mix | 1 |
| 3 | Laboratory Cover Glass | 25.5x75.5x0.3[mm] | 1 |
| 4 | Silicone Glue | Silicone Glue | 1 |
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.
The base
- The cone on the base of the floor is make the funnel shape of the chip ((a) in fig. 1).
- Small slits were made in the walls of the base to position the cover accurately.
- The overall size was determent so the chip will fit on standard microscope cover
slide. this will enable us to run experiments under a microscope easily.
Fig. 3: The geometry of the mold.
The cover
- Four rods coming out of the sides of the cover for easy extraction of the cover
when taking out the PDMS.
- The ramp is to insure that the channel will be inserted inside the PDMS and
getting the wanted channel height.
- The cover is made smaller than the base for a good fit and for letting out
any gas that might have been caught when inserting it. Those gases, if
left in will expend in the oven and cause deformation in the chip.
Fig. 4: The geometry of the cover.
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 that the fact that the polymer it uses (PLA) can be heated to relatively high temperatures without changing form (TG=60-65 C) and does not reacts to the PDMS. Another 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
Fluidic Factory enables fast prototyping of microfluidic chips, manifolds and connectors using COC (FDA approved, biocompatible, translucent and robust polymer). Printing the chip toke about 3 hours and was made straight from computer model. This technology just came out this year and we are the first iGEM group to ever use it.
Fig. 5: Factory chip fabrication process.
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, duo 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. 2:3D 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 2, to house the chip and the electrical circuits discussed below.
Fig. 2:3D Model of the quantitative system
The electrical circuits
The system consists of two independent electrical circuits as shown in Fig 3. The blue circuit contains a resistor of 10 [kΩ], a potentiometer, a LED and Arduino as a constant voltage source. The red circuit contains a photoresistor LDR that is sensitive to 600 [nm] wavelength, a resistor of 1[MΩ] and an Arduino controller. The Arduino supplies constant voltage to both circuits and measures the voltage that falls on the 1[MΩ] resistor.
Fig. 3: Schematic diagram of the two electrical circuits.
Computer data system
The Arduino controller collects samples of the voltage that falls on the 1[MΩ] resistor. The voltage
is converted to a digital signal and fed to the computer. The computer data system is based on a
"Matlab GUI".
Note that the Arduino I/O toolbox needs to be installed.
When running the Matlab code, the window
shown in Fig 4 pops up.
Fig. 4: The system's user interface
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 VR 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 at 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 VR 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 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 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 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 resistor.
Since:
VR 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
| Item Number | Part Name | Description | Quantity |
|---|---|---|---|
| 1 | Breadboard | conductor | 1 |
| 2 | Wire | conductor | 9 |
| 3 | Yellow led | Light source | 1 |
| 4 | Resistor 10[kΩ] | Isolator | 1 |
| 5 | Resistor 1[MΩ] | Isolator | 1 |
| 6 | Photoresistor LDR | Optical sensor | 1 |
| 7 | Commercial chip | The project device | 1 |
| 8 | Arduino uno | Power source and sample collector | 1 |
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.
1. Calloway, D. (1997). Beer-Lambert Law. Journal of Chemical Education, 74(7), 744. http://doi.org/10.1021/ed074p744.3
