Background and Motivation:

As a team, Aquamatic Technologies noticed that current water testing methods and systems are inefficient. For a scientist or researcher to test an aquatic environment for a toxin, pathogen, or pollutant, they must first acquire a sample of that liquid from its source, which may not always be easily available. Then, there is no standard or universal method of testing water for contaminants, labs across the globe all use different, however equally time consuming, expensive, and labor intensive methods. It can take many hours, days, or even weeks for results to be ready, during which the individual conducting said experiment must be on site ready to intervene throughout the experiment should anything go wrong.

Figure 1: Traditional Water Testing/Treatment Method

The motivation for our project arose from these problems. We began searching for a way to improve the process of toxin detection within liquids, with three main goals in mind: automation, lowered cost, and customizability.

The AM1:

Our product, the AM1, is a novel, modular piece of technology with electronic integration throughout. It will allow users to customize an aquatic environment of their choice via a user-friendly touch screen interface, and combine that liquid input with any biosensor.

Figure 2: Graphical Overview of the AM1's functionality

The use of a biosensor in our product allows us to generate quantifiable output related to the rates of completion of biological reaction. This data is used to quantify the presence of user-chosen toxins or pathogens.

Figure 3: A 360° view of the 3D model of the AM1

As a replacement to traditional water testing, after the water simulation and biosensor combination stage, the AM1 will he AM1 will facilitate a reaction between the two via the creation of nanoliter-sized droplets in a microfluidic chip. We chose this technology for its small size, fast prototyping, and low cost; the two microfluidic chips used in the AM1 can be easily replaced or modified within a day if need be. After the initial creation stage, these droplets are sent into an incubator, to be housed while the reaction takes place. The droplets are then sent into a second microfluidic chip, where they are further spaced out from each other before entering the sensing portion of the chip. There, data is taken and processed regarding the success rate of the reaction, informing users if any toxins had been detected, and the extent of their presence in the sample.

Further Impact:

While exploring past teams' websites, we found that many iGEM teams have worked on projects related to biosensors, aquatic environments, water testing and treatment, or a combination of these topics. Teams have even developed new biosensors for researchers to use in the lab. However, biosensor effectiveness can be limited depending on a number of factors. Aspects like pH, conductivity, salinity, temperature, turbulence, and more can all have an impact on the intensity of the biosensor output. This issue is one that the AM1 can alleviate through its modularity. By allowing users to adjust their base aquatic environment, researchers are able to test the effectiveness of their biosensor under a variety of conditions, meaning when the product is sent to use among labs around the world, its results can be compared to the AM1’s data output regarding effectiveness. You can read more about the impact of the AM1 on our Proposed Implementation Page.

Parts Breakdown:

Aquatic Environment:

The Aquatic Environment will filter water and simulate a chosen aquatic environment with a multi tank, reservoir, pump, and sensor system setup.

Figure 4: The AM1's multifluid input, the feature behind the device's ability to simulate aquatic environment

This began as a two tank system with a single filtration device. Knowing that we would be using microfluidic devices for the reaction and sensing stages of our product, a proper filtration system was necessary to remove any debri that could potentially clog the small microfluidic channels; something as small as fibers from clothing can compromise the microfluidic device performance. Our team went through many iterations of filtration devices on the journey to find the most efficient and effective one, while also considering how easy it would be to remove for deeper cleaning between uses of the AM1. You can navigate to our Engineering Success page for a more indepth and detailed explanation of the different iterations of filtration devices we went through and their 3D models.

As we continued brainstorming how to make our product more versatile, and after meeting with our integrated human practices collaborators, we decided to expand our initial aquatic environment design to allow users to customize liquid inputs directly in the device. Both biosensors and waterborne pathogens may be expressed differently depending on the environment they are being studied in. Microorganisms can multiply, or diminish, as a response to even small environmental changes. The addition of a simulation tank became quickly evident as a way to expand the usability of the AM1. With this, users can now either input their chosen liquid environment, if they have access to it, or recreate it using a set of small reservoirs that sit upon a larger tank. These reservoirs are connected to the tank below them via peristaltic pumps so the amount of liquid leaving them is highly controlled by the user. Currently, the simulation tank holds a pH sensor, and has the potential for additional salinity, ORP, and temperature sensors, each of which would give live data to the user via our touch screen interface about the value of each of these in the user's water. A PC fan and magnet system underneath the simulation tank creates turbulence in it if needed by the user, with 6 levels of intensity. All these additions are used to authenticate the water a user wants to have tested and sensed, and can be used to systematically adjust the water for more detailed results on how slight changes in pH, salinity, ORP, or other factors affect the survival rates of a chosen pathogen. To again ensure there is no debri in the water that would cause issues within the microfluidic chips, the water is sent through a second filtration tank before the sampling system takes it to the droplet generation stage.

Microfluidic Device I:

Figure 5: The system's droplet generation microfluidic chip

Water from the liquid environment combines with a stimuli in the sampling system and is then sent into one of the two ports in the two-input droplet generator microfluidic chip; the second port is where the biosensor substance is inputted. At this point the liquids are traveling through channels that are just 125 microns wide. After being mixed together within the chip, oil is used to pinch off nanoliter sized droplets of the biosensor/liquid combination, each of which are now essentially mini-reactions themselves. Within each tiny droplet the biosensor is now free to react with the stimuli should any of the sought-after pathogens be present in that droplet. Depending on the biosensor and liquid being used, this reaction will need some amount of time to incubate.

Custom Incubator:

Figure 6: The design of the AM1's custom built incubator

Incubation is a necessary step for reactions of this nature to reach completion, so our team built a custom droplet incubator. A thermoelectric cooling chip is programmed and controlled via arduino and raspberry pi with accuracy control of about +/- 0.5℃. Housing for this we constructed by milling polycarbonate sheets into a rectangular container that would fit the necessary chip, heat bed, and 6-bed test tube container. This entire structure is enclosed within the AM1, but is still easily accessible and removable if necessary. Everything remains in droplet form throughout, and the length of incubation is determined by user input and fully automated.

Microfluidic Device II:

Figure 7: The AM1's fluorescence sensing microfluidic chip

After incubation is complete, the droplets are ready to be processed and sensed with our second microfluidic chip. This is a single input chip that uses oil to further space out the droplets and make sensing easier and more accurate. Next, individual droplets pass over the sensing portion of the chip, where the fluorescence detection system samples the fluorescence levels in the channel. This data is measured and saved for analysis to detect differences between the fluorescence levels in the droplets versus baseline. In droplets where the biosensor reaction took place, the data would reflect large differences between the droplet and baseline levels.

Touchscreen User Interface:

Figure 8: The AM1's touch screen, the interface between users and electronics

An integral part of our project was creating an automated system that users can easily understand and adjust to their needs, so we decided to integrate a raspberry pi powered touchscreen. This screen is mounted on the exterior housing of the AM1, and takes in user input for real time modifications. Users begin by selecting to either create a custom protocol or skip directly to sensing an already saved protocol. If they choose the custom protocol, they are taken to a screen with a slider for each available parameter they can adjust. Once the values they’d like are chosen the user can either save this protocol to use in the future again, or press next to use it just once. If the user decides to use an existing protocol instead of customizing their own, they are taken to a screen with all saved protocols. Then, after choosing one of the given options, users can either select it to use in the system, modify it, or delete it if it's no longer needed. Once the system is running a selected protocol, the screen will give updates on the AM1’s progression via progress bars.

Housing Frame:

Figure 9: The AM1's custom housing

The AM1 is fully housed in polypropylene and ABS plastic sheets held together by 20 mm aluminum rails. To piece the various components together, we used t-slots, hinges, and custom acrylic shelving. The goal was to provide easy access to the different parts while also providing proper support and protection to the whole system. Because the system is composed of both liquid and electronic components, we divided the product into two halves - one solely for liquid inputs and the other for electronics and output analysis. The custom shelving was a big factor in the success of our assembly because it allowed for the seamless integration of various parts. We started off with one acrylic sheet for shelving but later moved to two to provide more room and tolerance. The acrylic sheets were first cut to dimension with a laser cutter; the smaller incisions for part placement and screws were later done using a manual mill.


Figure 10: The device's Raspberry Pi, the central part of its automation

The automation of the AM1 was achievable through the integration of all the electronics needed for the different features of the device with the application displayed on its touchscreen. The central part of this whole integration is the Raspberry Pi, which runs all the code needed for the AM1 to function, therefore increasing the portability of the device by eliminating the need for a bigger, stationary computer to be plugged into it.

Aside from the incubator which independently runs on Arduino code (corresponding to a closed loop between a peltier cooling chip and a DS18B20 temperature sensor) the entirety of the automation code is written in Python Script and is run from the hardware system’s Raspberry Pi. PyFirmata Python library was used to seamlessly connect and control our Arduinos using a singular coding language, Python. The code is modular and split into multiple functions, where each function corresponds to running a specific part of the device.

In order to ensure the compatibility of the hardware and software aspects of the AM1, all functions used to run the device’s hardware are run as background tasks using a Celery Job Queue. This helps parallelize software and hardware functions to avoid any lagging. Overall, our coding structure and style optimizes the testing process to produce timely results.

Our array of functions are then combined into three main Python scripts: The first corresponds to the AM1’s multi-fluid input system, and it relies on a closed loop between different sensors submerged in the input water and peristaltic pumps to dispense liquids in the user’s water until the target liquid parameter levels are reached.

The second script automates droplet generation: it samples 1 mL of water from the sampling tank and mixes it with stimuli in a test tube. It then pushes the resulting water + stimuli mix, the biosensors and the oil through the AM1’s tubing using high priming flow rates until all liquids reach the droplet generation chip. Once the liquids fill up the ports of the chip, their flow rates are significantly reduced in order to reach the flow rates needed for optimal droplet generating conditions. The resulting droplets are then pushed into our incubator for a period of time determined by the user. Resulting numbers and timings for our droplet generation can be found in the table in the results section of this wiki.

Lastly, the third Python script initiates the movement of droplets out of the incubator. Using primed flow rates, the droplets are then reinjected along with oil into our sensing chip. Just like for the droplet generation chip, the liquids are then slowed down to circulate through the channels of the chip. By this step, the user is expected to have the materials and equipment needed for sensing already set up.

To collect our fluorescence results, all the necessary setup for data collection was done by the Boston University CIDAR Lab in their dedicated microscope facility . To collect the output data and automatically visualize it, the PyFirmata library is used to control an Arduino from the Raspberry Pi to get the sensor’s data and store it. The data is then retrieved and used with the Python NumPy library to plot the water testing results.

Though we were initially planning on having all functions of the AM1 be called sequentially from one main Python script, splitting those functions into three scripts would allow a larger flexibility and modularity by allowing the user to run different steps of the testing process at any time they would like to. For all three stages of testing, the user is alerted via the touch screen interface about a step being over, and has the ability to simply click on a touch screen button to run the Python script corresponding to the next stage of testing.

This also allows the user to take advantage of our modular system to make changes to the setup between different steps of a run. Depending on their need, the user could easily replace a chip, a tube, or refill their oil without having to wait for testing to end.

Water displacement within our system can be done using two different methods: a pressure based method using pressure pumps (along with flow sensors), and a flow rate based method using syringe pumps. Pressure pumps are much more portable, while syringe pumps have a larger footprint and do not need a pressure source. For both cases, droplet generation or reinjection for sensing can be automated through a Python script run from the AM1’s touch screen. For example, we ran the second stage of our device (sampling, mixing and droplet generation) with pressure pumps, but used syringe pumps for the third stage of our device (reinjection and fluorescence sensing). This interchangeability allows the user to prioritize portability or consistency depending on their application, or it could simply allow the user to go with one of these options if they do not have access to the other.


Berketa, Kseniia. “Dependence of the Biosensor Responses on Ph of the Buffer Solution ...” Research Gate, May 2022.
Bhalla, Nikhil, Pawan Jolly, Nello Formisano, and Pedro Estrela. “Introduction to Biosensors.” Essays in biochemistry. Portland Press Limited, June 30, 2016.
“Contaminants - Water Quality Association.” Water Quality Association. Accessed October 11, 2022.
Dixon, Thomas A. “Sensing the Future of Bio-Informational Engineering.” Nature news. Nature Publishing Group, January 15, 2021.
“Ecology and Evolution of Waterborne Pathogens and Indicator Organisms.” Essay. In Indicators for Waterborne Pathogens. National Academy Press, 2004.
European Pharmaceutical Review. “New Selective and Sensitive Biosensors Made Using Synthetic Biology.” European Pharmaceutical Review, July 26, 2018.
Mehrotra, Parikha. “Biosensors and Their Applications - A Review.” Journal of oral biology and craniofacial research. Elsevier, 2016.
“Water Quality Topics: Pathogens.” EPA. Environmental Protection Agency, April 2022.,-The%20origin%20of&text=Other%20pathogenic%20microorganisms%2C%20such%20as,Pfiesteria.
“What Conditions Encourage Bacteria to Grow?” What conditions encourage bacteria to grow? / Food Safety FAQ / Health and Safety / Consumer Resources / Home - Florida Department of Agriculture & Consumer Services. Accessed October 11, 2022.,acidic%20or%20extremely%20salty%20conditions.