BIOLOGICAL ENGINEERING CYCLE
The engineering cycle is the foundation of our strategy in our iGEM project. From choosing our topic of interest to lab work, we employed the engineering design thinking process. We used an organized, iterative approach that promoted creativity and improvement for our bioengineered system. Here we describe the failures, successes and iterations made during our project.
The first steps involved topic selection followed by choosing a suitable strain of bacteria for Lithium and Arsenic detection and carefully creating the genetic circuit that powers our biosensor. This was followed by the hard calibration phase, where we made sure that the different components of our biosensor worked appropriately. By putting the various components of our biosensor through several testing, we were able to confirm its operation and therefore prepare it for practical applications. Every component of our sensor was chosen with safety and ethics in mind, highlighting ethical and responsible genetic engineering techniques. Our project's engineering flow drives continuous improvement and adaptation, ensuring our project remains on the cutting edge of synthetic biology and offers solutions to pressing challenges.
TOPIC SELECTION
To begin with, we identified various problems within our community and ways in which we could help mitigate those problems. At this stage, intensive research was done on possible problems we could solve. After several literature review, we chose Lithium detection and mining using synthetic biology as Lithium is one of the promising industries. Looking at the product cycle of the lithium batteries, we realized these batteries end up in dumpsites after use creating another problem, environmental pollution. In addition, Lithium has just been discovered in some parts of Ghana and we intend to jump ahead to provide cheaper and sustainable means of detecting, mining and recycling lithium to prevent further damages to our environment. This influenced our decision to not only tackle Lithium detection and mining but also Lithium recycling.
GENETIC CIRCUIT DESIGNS
Chromoproteins and fluorescent proteins as Reporters
In our project, we have strategically employed chromoproteins (CPs) as reporter genes to convert a transcriptional module into a quantifiable signal. Unlike fluorescent proteins (FPs), CPs offer several distinct advantages that align with our project's objectives. One notable advantage is the innate dark colour of CPs, making them easily discernible with the naked eye in ambient light conditions. This characteristic not only simplifies our detection process but also eliminates the need for costly and complex instrumentation, facilitating cost-effective examination. By opting for CPs as our reporter genes, we have leveraged their unique features to enhance the accessibility and practicality of our project.
Lithium sensing module
In a paper titled "Lithium-sensing riboswitch classes regulate expression of bacterial cation transporter genes" [1], the authors explored riboswitches, a genetic element, in sensing and regulating the expression of bacterial cation transporter genes related to lithium. By utilizing riboswitches, the researchers could detect the presence of lithium and subsequently control the expression of transporter genes responsible for moving cations, including lithium ions, across bacterial cell membranes. When the nhaA riboswitch detects the presence of Li+ ions it triggers the eforRed chromoprotein to be expressed thereby, indicating the presence of lithium.
Arsenic sensing module
By embracing the concept of employing an inducible promoter, we adopted, modified and improved the design from Ashesighana iGEM 2022 team, which hinged our design on a fascinating principle. This unique promoter remains in a dormant state until it encounters specific triggers, essentially waiting for an external stimulus to activate it. When the activator protein binds to the promoter in the presence of arsenite (As3+), it activates serving as an ignition for transcription. As a result, the reporter gene, amilGFP, emits a signal in the form of fluorescence (glow under blue light). This elegant mechanism offers a dynamic and controllable means of monitoring and reporting gene expression in response to arsenite exposure, a pivotal feature of our project.
Schematic of the As sending module based on the inducible promoter
Why Arsenic?
In our pursuit of a reliable and efficient method for detecting lithium, we turn to arsenic as an ideal pathfinder element. Arsenic, a well-established pathfinder for various geochemical indicators, plays a crucial role in hinting at the presence of valuable elements, including lithium. An international team of researchers found that certain geological settings with elevated arsenic concentrations often coincide with lithium deposits. This relationship is akin to the link between arsenic and gold in mineralization, where arsenic enrichment in soil minerals facilitates the binding of gold. In a similar manner, arsenic-rich geological environments appear to host elevated lithium levels, making arsenic an informative indicator for lithium prospecting. Furthermore, soil samples taken from regions with known lithium deposits frequently reveal significant arsenic levels, solidifying arsenic's status as a valuable pathfinder element for lithium. feature of our project.
Bioleaching Module
The bioleaching module utilizes acid thiobacillus ferroxidants in oxidizing iron and sulphur containing compounds. It contains tetrathionate hydrolase enzyme which catalyses tetrathionate hydrolysis. This involves sulphur oxidation to generate sulphate, elemental sulphur and thiosulfate. This module also contains high potential iron sulphur (HiPIP) and a pH resistant gene. The HiPIP has a high redox rate effective in catalysing the oxidation reaction taking place. It oxidises ferrous iron to ferric iron which further oxidises the metal sulphides in the compound thereby speeding the mineral dissolution rate to obtain lithium. Through these processes, the Lithium ions are liberated out from the compound and leached out. During the bioleaching process, hydrogen ion concentration increases thereby lowering pH hence the need for pH resistant gene.
DESIGN ITERATIONS
After modelling our modules in Benchling, we sent them to IDT for synthesis. They were able to make the arsenic module, but the HiPIP and Lithium had problems. The Li and HiPIP modules failed because the sequences contained a feature that created a hairpin in the terminator, which prompted us to modify the terminator using different types of terminators from the iGEM registry. Another reason contributing to the failure was a higher GC content.
To solve these issues, we did codon optimization to reduce the high GC content in the sequence and changed the double terminator to a single terminator. We resubmitted them for sequencing again. This time, they were able to make the lithium part, but the HiPIP could still not be made. Although they were able to synthesise the lithium part, there was a secondary product present in the synthesis as an impurity and as such required thorough screening for downstream application.
HYDROGELS
Sodium Alginate Concentrations
For our hydrogels, we used 2.5%, 5% and 10% of sodium alginate with 5% Calcium chloride to experiment and establish an ideal sodium alginate concentration. The 2.5% hydrogels did not form the balls as desired but rather looked like a gel. The 10% sodium alginate hydrogels formed well defined spheres. Thus, through trying out different sodium alginate concentrations we were able to determine the ideal sodium alginate concentration as 10%.
Optical Densities
Initially we started making hydrogels with bacteria at OD600 of 0.01, 0.1 and 1.00. The colour change for hydrogel balls with 0.01 OD was not clearly visible whereas that of 1.0 was more visible. In an attempt to increase visibility, we performed increased the OD and experimented with 2.00 OD, 3.00 OD and 4.00 OD. An increase in OD showed an increased intensity in the colour. However, in as much as the colour was well pronounced, it did not change over time which meant that higher OD was not ideal for our case. Having realized this, we reduced the OD to maximum of 1.5 and minimum of 0.75. This was the OD range we proceeded to use for all our subsequent experiments.
REFERENCES
[1] White, N. H., Sadeeshkumar, H., Sun, A., Sudarsan, N., & Breaker, R. R. (2022). Lithium-sensing riboswitch classes regulate expression of bacterial cation transporter genes. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-20695-6
BIOLITH ENTREPRENEURSHIP ENGINEERING CYCLE
The Entrepreneurship Subteam is an integral part of our project, and its primary goal is to ensure the project's alignment with market demands and to foster innovation in sustainable lithium mining practices. Our journey began with feedback from key stakeholders, including Engineers and Planners, and local communities. This feedback was pivotal in reshaping our project objectives and technical approach. As a result, we shifted our focus towards pre-exploratory technology for lithium mining and exploring the recycling of lithium from battery waste using genetically engineered bacteria.
PROBLEM IDENTIFICATION & STAKEHOLDER FEEDBACK
We recognized the importance of incorporating stakeholder feedback into our project. Engineers and Planners advised us that our project is best suited for pre-exploratory technology rather than direct extraction, given the technical requirements involved. This insight guided our project's adjustment towards more exploratory objectives while maintaining our sustainability commitment.
REASSESSMENT OF PROJECT GOALS & OBJECTIVES
We revisited our project goals and made necessary adjustments in response to stakeholder feedback. We realigned our focus from extraction to pre-exploratory technology, ensuring we met the industry's needs and environmental standards.
RESEARCH & DEVELOPMENT
After restructuring the entrepreneurship team to accommodate the changes, in parallel with the wet-lab subteam, we conducted comprehensive research focusing on pre-exploratory technology and the technical prerequisites for successful implementation. Additionally, we began exploring the use of genetically engineered bacteria for efficient lithium recycling from battery waste, and we discovered papers that had reports of how bioleaching can be used to achieve that.
Fig 1. Electrical Hardware timeline showing progress of our journey.
TECHNOLOGY DEVELOPMENT & PROTOTYPING
The Entrepreneurship Subteam worked closely with other subteams to develop prototypes for pre-exploratory technology. These prototypes aimed to facilitate better lithium detection and sustainable mining practices. We also initiated testing and refinement of bacterial processes for lithium recycling.
MARKET ANLYSIS & VALIDATION
We conducted thorough market research to ensure market alignment identifying potential customers and partners. Validation of the demand for pre-exploratory technology in the lithium mining industry shaped our project direction.
BUSINESS MODEL DEVELOPMENT
Our team formulated a robust business model tailored to the pre-exploratory technology. We defined revenue streams, outlined potential collaborations with recycling partners, and developed a sustainable financial plan.
MONITORING & EVALUATION
We are committed to continuously monitoring project performance, customer feedback, and market dynamics. This data-driven approach ensures we adapt and improve as we progress, staying true to our sustainability goals.
STAKEHOLDER ENGAGEMENT
We established ongoing communication with our stakeholders, including Engineers, Planners, and local communities. Transparency and dialogue were vital in ensuring all parties were informed and aligned with our project adjustments and progress.
FEEDBACK LOOP
Maintaining an open and dynamic feedback loop with all stakeholders is a cornerstone of our approach. We consider ongoing feedback invaluable in refining our technology and project evolution. The project remains adaptable to changing market dynamics and emerging opportunities.
SCALING & MARKET ENTRY
Upon successful piloting, we are now focused on scaling the deployment of our pre-exploratory technology in the lithium mining sector. Collaborations with mining companies and recycling facilities are being explored as we gear up for market entry.
CONCLUSION
The Entrepreneurship section of the BIOLITH team remains dedicated to the success of the project, continuously evolving and improving the approaches that align with market needs and industry standards. Our journey showcases our commitment to responsible and sustainable lithium mining practices, driven by feedback, innovation, and collaboration.
BIOLITH ELECTRICAL HARDWARE ENGINEERING CYCLE
It is impossible to make mention of the biosensor without the electrical hardware as it forms a key part of our entire project. This hardware bridges the gap between the mechanical hardware and the software and modelling. It is composed of a camera we call the LithoCam, a device built by the team which is able to take images of the bar code on the lid of each biosensor and associate that bar code with its respective GPS location. This aids in uniquely identifying each biosensor when performing further analysis after retrieval from the ground. On our engineering path, several changes had to be made based on research and the desired functionalities of the device, but it allowed us to explore more components and broaden our knowledge scope regarding electronics. Each challenge was a major learning opportunity for us and a stepping stone for greater and better improvements on our design. Thus, we never admitted defeat when the going got tough and out of that struggle came forth our functioning and innovative LithoCam.
DESIGN CYCLE FOR ELECTRICAL HARDWARE
Fig 1. Electrical Hardware timeline showing progress of our journey.
STAGE 1
In our project development process, we initially identified the ESP32 CAM microcontroller as the most suitable choice based on a Pugh chart evaluation. However, due to its unavailability, we had to procure one, and as a preliminary step, we sought to demonstrate the feasibility of associating photographs taken with a camera to GPS coordinates using a microcontroller. This led us to utilize the Raspberry Pi, equipped with the Pixi 2 camera, as a proof of concept. The core project concept involved employing electrical hardware to capture images of hydrogel samples before and after their placement in the ground, followed by an analysis to map out the likelihood of finding lithium in specific areas. This concept included incorporating barcodes, linking them to GPS coordinates, saving data in a database, and visualizing sample collection locations on a map. To achieve this, barcode labels would be affixed to the device housing, which took the form of a cuboid with hydrogel samples flanking the biosensor. Given the remote nature of potential mining sites without internet access, we planned to store captured images on an SD card for subsequent processing at a location with internet connectivity.
Fig 2. Pugh Chart comparing the different microcontrollers.
Fig 3. GPS locations from neo6mv2 GPS module showing on the terminal of the Raspberry pi
Fig 4. A picture taken by the pixi 2 cam showing image saved GPS location
STAGE 2
In implementing the esp32 cam we considered implementing the following:
- Push button – To manually take the pictures.
- OLED screen – To display the GPS coordinates where the picture was taken, and
indicate when the camera has captured the image.
- Wireless power source – To power the device remotely so as to not require a plug-in-wall power supply.
- Code the GPS module – This had to be done to retrieve latitude and longitude readings from the module.
CHANGES AND MODIFICATION
- The reset button on the esp32 cam was used as the button for taking pictures.
- Using an OLED screen required extra pins which the esp32 cam did not have. As a result, we considered two alternatives.
-Choice 1:
This circuit captures the picture after pressing the reset button. There will be a flash of light that indicates a picture being taken. The file name will have the GPS location of the place the picture was taken, as well as a number indicating the order of sequence of pictures. All these components are powered wirelessly using the Li-ion batteries, also the camera is coded to ‘shutdown’, if it is not in use. It wakes up only when the reset is pressed hence saving battery power as well.
-Choice 2: :
Since the ESP32 cam is a board that sacrifices so much of its pins for internal connections. when using both the camera and the microSD card there are so many internal connections. Unfortunately, the few pins left are suitable for use when including an OLED screen. A second microcontroller (ESP32 wroom) will therefore be used. This would mean we have slightly increased our hardware space, as we will have: ESP32 cam, ESP32, OLED, and a GPS module. This option perfectly displays GPS locations on the OLED and captures pictures and saves it on the SD card and it also tells when the picture is captured. Both microcontrollers communicate with each other using an interrupt pin.
Fig 5. Image of electrical setup with OLED screen and SD card file directory showing images taken and saved.
SCHEMATIC PCB DESIGN
We considered the following:
- Reset button on ESP32 would have to serve as camera-capture button.
- It will have header pins.
- The SD-card will be able to be removed and inserted at will.
- GPS module has its antenna attached so we need a space where that will rest.
- The camera would have to be protruding outward.
Fig 6. Schematic diagram of camera
Fig 7. PCB design of camera
IDEA 2 CONCLUSION
The first idea is building an electrical device that can take a picture and save the picture on an SD Card using the GPS location as the name of the picture file. The plan was to use an ESP32 CAM to take pictures and a NEO-6MV2 GPS Module to take notes of the GPS locations. An initial electrical design was done using the above-mentioned electrical components as well as two 3.7v lithium-ion batteries and an LM2596 to step down the combined voltage from the batteries to 5V in order to supply power to the whole system.
After the pictures with their GPS location have been saved on the SD Card, the SD card will be placed in another device for processing and mapping out the information.
STAGE 3
Following certain loopholes, we identified in our final design in stage 2, we had to make some changes. The loopholes and possible suggestions:
1. A screen to preview the object before a picture is taken was not considered. Following this, research was done, and it was discovered that a TFT screen can be used as the preview screen (the 3.5” inch TFT Touch Shield LCD Module was what was easily available and hence was used).
IMPLEMENTING A TFT SCREEN
Including the TFT screen required the addition of another microcontroller because the esp32 cam did not have enough I/O pins to communicate with the 3.5” inch TFT Touch Shield LCD Module (communicates using 8-bit parallel pins) so an ESP32 DOIT DEVKIT was chosen. The idea was to allow the esp32 cam communicate the images it views serially or via local Wi-Fi network (using the Esp Now Protocol) to the ESP32 DOIT DEVKIT which give the TFT screen the necessary data to display the images.
Schematic of circuit with TFT screen
Fig 8. Schematic diagram with TFT screen
STAGE 4
While exploring the implementation of the TFT screen, we had the opportunity to pitch our project to the fabrication laboratory expert on our campus. He brought to our attention that mobile phones now attribute pictures to locations once the user has their location turned on. He suggested we explore the idea of using an application to do all that we want our electrical hardware. Following this, we decided to analyse both ideas based on their pros and cons.
ELECTRICAL HARDWARE SOLUTION:
Cost Analysis of Electrical Hardware Implementation
Fig 9. Table of cost analysis of camera
PROs OF ELECTRICAL HARDWARE:
- 1. Easy to develop the final product.
CONs OF ELECTRICAL HARDWARE:
- 1. A lot of processes and gadgets so information may be lost along the way.
- 2. Lower picture quality of two mega pixels.
- 3. Less seamless. More steps to undertake.
- 4. Costly
- 5. May look like a classroom project considering the tools and components being used.
WEB APPLICATION SOLUTION:
This solution considers that our users may already have a smart phone and even if they do not have a smart phone, the cheapest smart phone is not as expensive as the total cost of making Solution 1. The web application solution considers that smart phones already can take pictures and take notes of GPS locations. Hence, an application could be created to:
- 1. Take pictures and GPS location.
- 2. Perform the processing of the pictures.
- 3. Mapping out the information.
PROs:
- 1. Seamless for user
- 2. A cheap smart phone can cost as low as ₵200.
- 3. May look more professional.
- 4. Few processes
- 5. Better picture quality with cheap smart phones having a picture quality of 5-8 mega pixels.
CONs OF ELECTRICAL HARDWARE:
- 1. More difficult to build (may take more time to build).
STAGE 5
After conversations about our business model with our instructor and some research done by talking to other experts, we divided the market of our product to two groups. Huge progressive mining industries and conservative local miners. Progressive mining industries already use software that enhance their mining experience and may see the value in our software as a product we are selling to them. However, conservative local miners will want to see something physical if we want to sell our project as a product to them. Due to this, we decided to continue with the electrical hardware idea and the application idea to serve different customer bases. However, we added another application that would be coupled with the electrical hardware. If we take an area for prospecting, we will want the biosensors placed in the ground to be certain distances apart in that area. This second application directs the user to places where they can create holes in that specific area to insert the biosensor into the ground while following the distance apart rule.
ELECTRICAL HARDWARE IN STAGE 5:
In stage 3, it was mentioned that we added the 3.5” inch TFT Touch Shield LCD Module to preview the images our camera views. However, we faced challenges sharing the images taken by the esp32 cam to the esp32 DOIT DEVKIT. This is because the images taken by the esp32 cam are saved in .jpg which can have sizes between hundred kilobytes or megabytes. However, if we are transmitting data from the esp32 cam to the esp32 DOIT DEVKIT serially or via ESP-NOW, the amount of data that can be sent at a time is 250 bytes. Hence, to transmit, the image data must be broken down and sent at intervals. Due to this:
1. The ESP32 kept crashing since we wanted to view the images in real-time and it was sending a lot of data within very short intervals.
2. Some of the image data got lost. Hence, image data at the TFT screen’s end was distorted.
This required us to find a preview screen that could be directly connected directly to the esp32 cam without all these issues. Hence, we need a TFT screen that required fewer pins to be used. We were able to finally acquire the Adafruit 2.8-inch TFT Touch shield which has the option to communicate via SPI. This meant we needed only five pins from the esp32 cam to directly connect the TFT screen to it with an additional two pins to supply power to the screen. We still needed the ESP32 DOIT DEVKIT because after the TFT screen was connected to the esp32 CAM, we needed UART pins and I/O pins for the connection of the GPS module and a button to take pictures. Below is the schematic of the final electrical design and PCB layout.
Fig 10. Schematic of final electrical design and PCB layout
Fig 11. 2D of front and back layer of PCB
Fig 12. 3D of front and back layer of PCB
Due to the orientation of the final frame of our electrical hardware and the fact that we are using through hole components, we had to create another board such that our esp32 cam and battery are on one side of the board and the rest of the components on the other side.
Fig 13. 2D and 3D layout of PCB containing the esp32 cam
Now we have been introduced to our biosensor and the LithoCam into detail, but what then? How do we make sense of the colour change observed and how do we put to use the images taken by the LithoCam. This is where the software steps into the picture and makes its long-awaited entrance in its perfection. The software is vital to the success of this project because it puts all the pieces together and makes sense of all the data collected, completing the detection process. Similar to all the other cycles, the software engineering cycle also experienced a series of twists and turns. Our path to success was a struggle because of limited data, paid softwares, APIs (Application Programming Interfaces) which were difficult to program, just to name a few. Nonetheless, we continued to follow the engineering design cycle, never gave defeat the upper hand and from the fruit of our labour came a functioning mobile app and a successful mapping application.
BIOLITH HARDWARE ENGINEERING CYCLE
Our entire solution starts with the mechanical hardware(biosensor) which performs the actual detection and acts as the lithium explorer.It contains the sodium alginate hydrogels which change colour depending on the prescence of lithium and arsenic ions in the dug ground. SolidWorks (a Computer Aided Design software) was used for modelling the structure of the biosensor which would be placed into the ground. The design of the sensor was subjected to different iterations based on tests and experiments performed in the lab, as well as the feedback received from some of our stakeholders and other users. We were sure to integrate majority of the feedback received or find some innovative way to address any concerns raised from our stakeholders. Observe our progress from the start to the very end, each design an improvement of the next and each setback a learning opportunity to grow and be open-minded in designing.
DESIGN 1
Our first design consisted of a hexagonally shaped containment with bacteria embedded in flat hydrogels with the following features:
- Slides – the strains of bacteria synthesized for the detection of lithium and arsenic would be contained in sodium alginate hydogels and placed in slides which would fit into the containment.
- Hexagonal containment – the hexagonal shape allows for maximum exposure by increasing the contact surfaces of the bacteria for the diffusion of metal ions.
Fig 1 First CAD model
PROBLEM ENCOUNTERED
The wet lab team experienced some difficulty in making the hydrogels in rectangular flat shapes during their experiments and they also tended to deform easily, hence the need to brainstorm another way to create the hydogels and called for re-evaluation.
DESIGN 2
After finding out that it would not be possible for the gels to be rectangular such that they fit into the slides, further testing was performed, and the small spherical shapes of the hydrogels were proven to be better and firmer. Thus, the design had to be modified to suit this new discovery.
Fig 2. Second SolidWorks CAD Model
NEW DESIGN
The new design included the following features:
- Long wells (holes) – several of these would be created in the agarose mold using plastic rods and the hydrogels would be placed in them.
- Agarose gel containment – agarose would be cast into a cylindrical shape and would serve as a means to hold the bacteria.
- Magnetic covering – similar to the initial idea however it would be attached to a screw-like object which would be joined to the bottom of the overall structue to hold it all in place.
PROBLEM ENCOUNTERED
Few hydrogels could fit into the structure, meaning a lower sensitivity for detection of lithium and arsenic ions. It also lacked a casing to serve as a covering over the agarose containment.
DESIGN 3
This design was an improvement on the 2nd design. To increase the sensitivity of our biosensor, we increased the number of holes and also included a casing for our biosensor to protect the agarose from easily breaking due to any vigourous movements before it has been deployed. This biosensor would be placed at a depth of 3 metres in the ground. Given the heightened sensitivity of the bacteria, we presumed that this depth would be optimal for detecting traces of the lithium element and its associated pathfinder element, arsenic.
Fig 3. Biosensor without its casing
Fig 4. Biosensor casing with plastic rods within
PROBLEM ENCOUNTERED
No way to uniquely identify each biosensor so that we can know can associate each biosensor to its GPS location.
DESIGN 4
To be able to uniquely identify each biosensor for further analysis by our software we placed a bar code on the lid of each biosensor for easy tracking and identification. Following a comprehensive discussion with a prominent prospecting company in Ghana, Engineers, and Planners, we received invaluable feedback indicating that a depth of 3 meters merely reached the topsoil layer, and consequently, no significant findings were likely in that range. This feedback prompted us to reevaluate our approach, leading us to reconsider the depth and modify it to 15 meters below the surface and incorporating this into our design.
Fig 3. Biosensor without its casing
Fig 4. Biosensor casing with plastic rods within
BIOLITH SOFTWARE ENGINEERING CYCLE
Now we have been introduced to our biosensor and the LithoCam into detail, but what then? How do we make sense of the colour change observed and how do we put to use the images taken by the LithoCam. This is where the software steps into the picture and makes its long-awaited entrance in its perfection. The software is vital to the success of this project because it puts all the pieces together and makes sense of all the data collected, completing the detection process. Similar to all the other cycles, the software engineering cycle also experienced a series of twists and turns. Our path to success was a struggle because of limited data, paid softwares, APIs (Application Programming Interfaces) which were difficult to program, just to name a few. Nonetheless, we continued to follow the engineering design cycle, never gave defeat the upper hand and from the fruit of our labour came a functioning mobile app and a successful mapping application.
DESIGN CYCLE FOR SOFTWARE
Fig 1. Design cycle of BIOLITH software
STAGE 1:CHARTING NEW TERRITORIES
We studied the breakthrough work carried out by previous iGEM teams before starting our ground-breaking endeavor. Our objective was to find any pertinent earlier research, as well as areas where we could improve current tools and use them to solve our particular situation. The 2017 Ashesi University team, the 2019 University of Edinburgh team, the 2020 University of Newcastle team, and the 2020 University of Texas team are just a few of the famous teams whose work we looked at. All of these outstanding teams had difficulty developing biosensors to deal with environmental problems.
Similar to our current mission, these teams employed either Green Fluorescent Protein (GFP) or chromoproteins to signal the presence of the elements they were investigating. However, their results offered only binary outcomes: the element was either present or absent.
A remarkable breakthrough emerged from the 2022 Ashesi University team, who adopted a novel approach. Instead of limiting themselves to simple binary detection, they used pathfinders to ascertain the likelihood of an element's presence based on quantifying color changes.
Our mission took inspiration from these prior projects, steering our focus towards the detection of lithium and its associated pathfinder elements for pre-exploration. Our unique twist? We aimed to undertake a more comprehensive analysis that involved generating a surface plot illustrating regions and their respective probabilities of containing lithium.
In essence, we built upon the foundations laid by these pioneering teams, harnessing their collective wisdom to refine our strategy. Our deviation from the norm was utilising a machine learning model to recognize color changes and our development of a probability model and a mapping model, culminating in a detailed surface plot depicting the likelihood of lithium presence in various areas.
STAGE 2:THE QUEST FOR BLUE
Our journey commenced with the exploration of the 'Shades of Blue' color palette. Initially, we set out to design a logistic regression algorithm tailored to discern various shades of blue. Our hypothesis was rooted in the belief that the intensity of biosensors correlated with the quantity of lithium in the soil. However, a pivotal realization swiftly altered our course: color intensity did not correspond to lithium quantity. Still, given our biosensors' high specificity, it merely served as a marker for lithium's presence.
This marked a turning point in our approach. Drawing inspiration from the AshesiGhana 2022 team, which had successfully used Pathfinder elements for prospecting gold, we decided to apply a similar principle to our project. Thus, our revised strategy aimed to enhance our ability to detect lithium's presence in various regions accurately.
Fig2: Image showing different shades of blue
STAGE 3:BRIDGING THE SPECTRUM
As our project evolved, we introduced two additional pathfinder minerals into our scope, necessitating the development of a probability model. Our objective was to translate the meaning behind each color change of the hydrogels into a numerical probability value, facilitating the creation of a comprehensive map depicting the likelihood of lithium deposits.
To test the viability of this approach, we engineered a Python script capable of generating images with three distinct colors in varying proportions. A secondary script was devised to systematically analyze each pixel in these images, recognizing and counting the colors. In our testing system, blue denoted lithium detection, red signaled the presence of pathfinder element 1 (Arsenic), and yellow represented pathfinder element 2 (Rubidium). The probability formula was thus conceived: The probability formula was then formulated as follows:
P(Lithium) = (0.5 * number of blue hydrogels + 0.25 * number of red hydrogels + 0.25 * number of yellow hydrogels) divided by the total number of hydrogels.
The assignment of probabilities was deliberate: lithium received a 0.5 probability coefficient, while both pathfinder elements were assigned a probability of 0.25. This emphasized that detecting lithium alongside its pathfinder elements increased the likelihood of lithium's presence.
This approach enabled us to come up with a numerical value that is of more importance in generating a surface plot.
Fig 3: Snippet of code showing the generate_random_picture function used to generate the random images.
STAGE 4:PLOTTING THE PROBABILITIES
In the fourth stage of our endeavour, we harnessed the probabilities derived from images to craft a comprehensive surface plot. This plot employed GPS coordinates on the X and Y axes to pinpoint specific locations, while the Z axis conveyed the calculated probabilities. The software we developed facilitated a visual representation of probability distribution across our area of interest, enhancing our capacity to identify regions with varying lithium concentrations.
STAGE 5:ADAPTING TO REALITY
In the final phase of our project, we encountered constraints that compelled us to adapt our approach. We streamlined our focus due to limitations in available genetic components, opting to work exclusively with arsenic as the pathfinder alongside lithium. This shift in focus presented an opportunity to fine-tune our probability model:
R is the number of red hydrogels
Y is the number of yellow hydrogels
N is the total number of hydrogels.
P is probability
At this juncture, we deeply understood the specific colors our biosensors displayed upon detecting these minerals. Yellow signified arsenic, while red indicated lithium's presence.In tandem with these adjustments, we optimized the physical structure of our hydrogels, transitioning from a rectangular shape to a spherical form. This alteration enhanced the biosensors' interaction with the environment, improving their efficacy.
In response to these adjustments, we developed two key software components to support our project. The first was a K-Nearest Neighbor color detection software, designed to identify the specific color exhibited by sodium alginate hydrogels containing the bioindicators. This software played a pivotal role in the accurate classification of hydrogel color changes, streamlining the detection process.
The second software component was a mapping application, allowing us to plot and visualize the probabilities of finding lithium across different GPS locations. This mapping software offered a practical and informative means of displaying the spatial distribution of lithium probabilities, thus facilitating a more comprehensive understanding of the field of interest. These software developments were instrumental in optimizing our lithium and arsenic detection system.
Lastly, we introduced a mobile app designed to provide a seamless experience for our users. This app served multiple purposes:
- Facilitating communication with our team.
- Assisting with biosensor orders.
- Enabling users to view their generated surface plots.
- Aiding in the mapping of areas even in our absence.
Users could calculate the number of biosensors required for their exploration needs and utilize the BIOLITH camera to capture the barcodes of biosensors, correlating them with specific locations where the biosensors was placed. Below are the key features of the mobile app:
