Virax: The Next Frontier For Energy Storage
Authored by Aditya Mittal and Karthik Mittal.
The current COVID-19 pandemic has led to aversion of viruses as many deem them to be a health risk and a detrimental factor in global society. Many individuals have felt the consequences that these viruses bring on populations and have seen the devastating impact that they have.
Because of this, many scientists have been quick to reject the possibility of implementing viruses in current global problems, often resorting to less efficient techniques. However, at Virax, we safely utilize genetically engineered viruses to make a strong impact in the battery industry.
These overlooked viruses, if implemented safely, can form an environmentally friendly and energy efficient battery that revolutionizes current industries like the EV space. Current lithium ion batteries use the conversion of chemical energy to electrical energy for their processes, which lead to environmental degradation and smaller lifecycles after the chemicals run out.
However, through the substitution of viral components, replacing the section of the battery hosting these chemical components, we can form a healthier battery that has slower cycle power degradation, higher energy density, and better battery lifetime.
The M13 virus has been extensively tested in these viral batteries by MIT and the Korea Advanced Institute of Technology; however, these have only made slight improvements to the current lithium ion batteries that are there. At Virax, we use machine learning clustering algorithms and neural networks to find the optimal virus and coating material to fix this issue. Since there are so many combinations of viruses and chemical coating materials, machines are the optimal way to compute this optimization problem and find the best combination.
So, you probably have lots of questions. How exactly do these virus-based batteries work? How does machine learning find these optimal combinations? What are the applications of this technology?
This will all be addressed below in this article. Let’s get started!
How do Batteries Work?
First, a brief introduction into how conventional batteries work is necessary before discussing about their underlying problems.
Batteries are commonly defined as a “collection of one or more cells whose chemical reactions create a flow of electrons in a circuit.” There are three vital components to any battery: the cathode (the positive side of the battery), the anode (the negative side of the battery), and the electrolyte (the substance that chemically interacts with the anode and cathode itself). A separator keeps the anode and cathode from touching.
Batteries have a chemical reaction between the anode and the electrolyte, which causes electrons to flow through the outer portion of the circuit before coming back to the cathode where another chemical reaction takes place. A sort of charge escalator occurs where chemical energy is necessary for the electrons to flow back into the anode again.
Note that the anode does not just flow to the cathode the short way because of the electrolyte and separator. The electrolyte is the glue that holds the battery together: it is capable of transporting these ions between the separate chemical reactions that happen inside of the anode and the cathode.
There is also usually a casing that surrounds the battery that contains these chemicals so that they do not leech out into the environment; because the main form of electrical energy comes from this chemical energy transfer, it is pertinent that these noxious chemicals are safely enclosed.
When the switch is closed (or the circuit is complete), the electrons are freely able to travel from the anode to the cathode. After the material degrades in the cathode and anode or the chemicals run out, the battery becomes unable to produce electricity again. Other batteries can serve as replacements after the battery degrades over time.
What is the Problem with Lithium Batteries?
After understanding the inner workings of lithium-based batteries, a few potential problems seem to arise, particularly on how to safely deposit these chemical-based batteries so that they do not harm the environment and how to increase the storage and throughput of these batteries without changing its underlying components.
These issues can only be addressed by replacing these vital components of lithium ion batteries that were discussed above. Lithium-ion batteries function by storing and releasing electrical energy through moving lithium ions and electrons through the electrode materials by chemical energy.
If this environmental degradation problem is to be solved, there needs to be a better alternative for this chemical energy transfer. This means getting rid of the underlying electrolyte and proposing a new alternative. There are currently four main forms of energy transfers: mechanical storage, thermal, electrical, and the aforementioned chemical energy transfer.
Either a new form of energy transfer needs to be formed or one of the existing ones need to be utilized in a way that is as efficient and intensive as current lithium-ion batteries. Each of these energy transfers come with their own problems, and they need to be addressed accordingly.
For the latter issue, increasing transport of these materials can enhance energy storage and throughput. As the minimum material thickness has been reached with conventional materials, nano structures have been used in developing materials to boost transport. This has seen increased discharge rates but has failed to address the fundamental kinetic limitation that is reached with these kind of batteries.
Lithium batteries have been optimized to address this limitation by tailoring particle size, but this is difficult as fabricating these particles is challenging. There has been some preliminary research into the field of carbon nanotubes; however, a lack of definitive research has led many scientific researchers to shy away from the problem. The research in nanoparticles, nonetheless, is still ongoing, although being extremely slow-moving.
Healthy batteries should have low levels of resistance and relatively high levels of voltages; however, as this degradation continues, the inverse, in fact, occurs where there are instead high levels of resistance and low levels of voltages. Carbon nanotubes and nanostructure implements can alleviate some of these degradation issues, but they can not stop the inherent chemical processes running the battery.
These difficulties are again difficult to tackle without changing the underlying components of the battery, mainly the electrolyte, anode, and cathode, since these chemical processes are the ones that often degrade the battery in the first place.
To summarize, there are three major problems with lithium ion batteries:
- They are constrained by kinetic limitations, and research in nanoparticles has been slow and costly.
- They have low ionic and electric conductivity, which can be improved with carbon nanotubes.
- The materials inside of the batteries are toxic and have harmful organic solvents.
Quantifying the Problem
In 2007, scientists were able to reach an energy density of 200 Wh/kg, which is comparable to that of a lithium-ion battery. However, from that time, energy densities have nearly tripled in the last 10 years, which indicates that current energy density levels for virus-based batteries are ~600 Wh/kg assuming constant growth, which is a lower bound for such an exponential technology. Even with this lower bound, it still performs better than a lithium-ion battery, which shows virus-based battery’s potential for growth.
Additionally, typical EV batteries last 3–5 years (a maximum of 8 years for regular use of electric vehicles), while viruses can live for an indefinite period of time given enough resources. This means that these batteries can sustain much longer than the typical half-decade that traditional batteries survive for, and they don’t degrade and harm the environment.
Due to the extremely high energy density and nearly infinite energy lifetime of a virus-based battery, virus-based batteries should be able to outperform lithium-based batteries by an exponential factor within the next couple of years of research.
What is the Problem with Energy Storage?
Simply put, energy storage is environmentally unsustainable, ineffective, and inefficient. Current estimates put energy storage devices at over 17% of all greenhouse gases, which shows that the use of more bio-friendly storage devices can help reduce greenhouse emissions into the atmosphere. Regardless of this, lithium batteries after usage can leech into soil, water, or into the air, which can cause great problems down the road and will not be sustainable going forward.
Additionally, current energy storage is still not as effective as we want it to be as of now. Current energy storage is still ~110% away from the kW/h price goal, which means that current energy storage methods are not cost-effective enough to provide lasting energy. Thus, new forms of energy must be harvested with reduced price in the field. Millions of dollars has already been put into chemical battery research, but changes in prices have been minimal in the last couple of years, due to chemical battery capabilities already reaching their limits on several fronts.
Lastly, current energy storage mechanisms are also inefficient for change. To move a new storage device to the market, it takes around 1 year, preexisting device usage, and millions of dollars. By introducing a new form of battery into the battery industry, the device can bypass these regulations and move directly into making an impact in the sector.
More information about the current problems with lithium batteries can be found here.
Enter Virus-Based Batteries
Virus based batteries can be used to fix most, if not all, of the problems mentioned above:
- Nanoparticles are costly: This is largely due to the expensive mechanisms that are needed to assemble these nanoparticles together at such a small scale. By utilizing virus manpower, we can assemble these nanoparticles into fully-functioning carbon nanotubes for electron transport, thus removing much of the cost associated with nanoparticle production.
- Low ionic and electronic conductivity: This can be fixed with carbon nanotube structures that will lead to faster electron transport from the anode to the cathode. As mentioned earlier, these carbon nanotube structures will be created through viral genetic engineering. More on that below!
- Environmental problems: Because viruses are made of biological materials and can easily degrade over time, these viruses add little to no negative effect on the atmosphere over time. Granted, current solutions with virus-based batteries still have one lithium terminal, but research has already shown that this lithium terminal can be replaced with a viral-created terminal in the near future.
- Energy lifetime: Because of the reproductive rate of these viruses, as long as the positive feedback loop between the battery component and the viral component continues, the virus should be able to reproduce and maintain its colony indefinitely within the battery host. Of course, this hasn’t been proved scientifically, but research by MIT shows these viruses can live far longer than conventional batteries.
- Inefficient energy storage: Because viral batteries are a completely new type of energy storage, they will take much less time to come to market, as they will not be wrapped up in as many governmental regulations. This allows for faster time to market and less time spent in validation research.
Quantitative research also backs up the use of virus-based batteries: from research done by MIT and Korea Advanced Institute of Technology, future trends in virus-based batteries can be extrapolated to show what current virus-based batteries look like. Note these numbers are based on lower bound estimates to ensure no overestimating in our research.
Viruses have 10x as much battery lifetime as current batteries (as they can survive up to 80 years), have 42% slower cycle power degradation (which means that they can sustain threshold power for more cycles), and have 90% higher energy density (which means higher specific power).
What is the Problem with Virus-Based Batteries?
Although virus-based batteries provide a viable alternative to lithium ion batteries, they lack effectiveness due to the viral types chosen. Current viral types chosen by researchers are ones that are commonly chosen in academia, and don’t utilize the best type of virus.
How is the best type of virus defined? We define the optimal virus by three benchmarks:
- Specific Power — how much power a battery can deliver in a single cycle
- Lifetime — how long the battery can sustain until its specific power drops below some threshold value
- Cycle Deficiency — how quickly does the specific power of the virus decrease
From these statistics, it can be shown that although the M13 bacteriophage was a great virus to start with, it lacks high numbers in two of the above categories. The MIT researchers noted that the M13 bacteriophage had a high specific power (~4.5 kW/kg, which is higher than lithium ion batteries), it degraded with a couple hundred cycles to a specific power < 1 kW/kg, and its lifetime only lasted for three thousand cycles.
However, these issues can be resolved in the future, understanding in that this was just preliminary experimentation. Further modifications can be made in the virus and material that it attracts to make these faster wires. Current research is being done towards the tobacco mosaic virus (TMV), a common pathogen of tobacco plants to see whether it can be a sound alternative for increasing energy density.
Also, note that another problem lies in the coating material used. The MIT researchers used cobalt oxide/gold for the anode and iron phosphate for the cathode; however, these have not been proven to be more effective than the hundreds of other combinations of materials out there. Thus, optimal coating material must also be determined to match the above three benchmarks.
There’s still lots of research to find the most optimal metal and genetically engineering the best type of virus for the most ordered structure. Before, the virally-assembled electrodes had a random structure, but if these arrangements become more ordered, energy density levels can increase and degradation can diminish.
Virax hopes to make an impact in this space by using machine learning to find the best coating and virus type to maximize the aforementioned three benchmarks.
Battery Vocabulary
Make sure to refer to the beginning of this article to understand the underlying processes of batteries. Before going on, here are some key terms you need to know:
Specific Capacity — the discharge current that a battery can deliver over time (measured in Ah)
Electroconductivity — the ability of a material to attract electrons. Note that it is highly useful to have electroconductive materials on the anode for increased electron transport.
Voltage — the difference in electric potential between two points, defined as the work needed to move a charge between two distinct points (often governed by the equation $V = IR)$
ESR (equivalent series resistance) — a measurement discussing the overall ability of the battery to resist current (higher resistance leads to more diminished current flows)
Nanowires — structures with a diameter in the order of a nanometer. These will be used to reduce resistance for electron transport and thus increase the energy output of the battery.
Carbon Nanotubes — nanowires primarily made of carbon that have high strength, durability, thermal/electrical conductivity. These are used in many places due to their lightweight properties and durable structure.
Machine Learning for Optimal Virus and Coating Type
A machine learning model can be implemented to tackle the problem listed above to find those the optimal combination of the three statistics highlighted above. Machine learning is often defined as the study of computational algorithms that improve continuously through experience and through extensive data points. With the use of machine learning and continuous learning, a network can be formed that closely models the inherent relationship between the virus and coating material to these values.
Multiple ML models will fit this approach; however, a neural network would be the best fit for our model as it would provide the necessary weights and bias that would classify the model correctly. Some benefits of neural networks include being able to store all fo the information on the neural network, being able to work with limited knowledge, and the allowance of parallel processing to sift through the data.
Inputs
Several inputs will be taken in for the neural network, including the virus’s genetic sequence and electric capabilities and the coating’s chemical properties. Note that there hasn’t been a wide-scale genomic library containing information about these viruses; however, organizations like NCBI are building comprehensive platforms to store this data (viruSITE and VIPR). Approximately 5600 complete reference viral genomes have been sequenced with common bacteriophages MS2 and phiX174 being the first to get sequenced.
Virus-Based Inputs
viruSITE is slowly increasing the amount of sequenced viruses; this data was extracted from numerous resources, like the NCBI RefSeq and PubMed datasets. This is growing to be a more comprehensive information resource for individuals in the field of viral genomics; however, it isn’t close to the level of granularity that is necessary for the inputs for our machine learning model.
VIPR, although less distinguished in the field, is used commonly in the field of computational genomics as well. These datasets are relatively incomplete; however, current advances are being done in the field to allow for faster genomic sequencing to get viral information at a much faster rate than ever before (through next generation sequencing).
Note that these datasets have many partially defined genomic sequences; however, for our purposes, fully defined sequences will provide more specificity to the ML model and increase the accuracy.
Assuming that this information is accessible within the next five to ten years (knowing that there are global organizations working on tackling this problem), the data provided by these databases can be used to train the model itself. The underlying genomic sequence (formed probably through next-generation sequencing) will allow for heightened conclusions and allow the neural network to make conclusions through its nodes.
Electric properties are difficult to distinguish without lab testing; however, relatively similar viruses to the M13 virus can be deduced to have electrical components because of the lab testing that has already been done on the M13 virus itself. Furthermore, materials inside of the viruses can subsequently be deduced accordingly based on whether they have electric properties.
Some of the electric properties that we will be looking at include the resistivity of the virus, the electric conductivity, and the temperature coefficient of resistance. The resistivity and the temperature coefficient of the virus are to ensure that there is no energy lost in the electric energy transfer while the electric conductivity is to determine whether faster strides can be made towards transporting the electrons from the supposed cathode to the anode. These electric properties are all crucial to the intents of the model itself.
Another area of concern lies in whether these viruses can sufficiently latch on to the coating material necessary. Again, this would require a bit more experimentation, but strides can be made through elaboration based on known facts about the M13 virus and its constituents.
Coating-Based Inputs
Different materials will be tested accordingly to see if any can provide better results than the original amorphous phosphates that were tested before. Current testing in carbon nanotubes is occurring, which could lead to higher energy outputs inside of the neural network model itself.
As discussed before, there has been little progress made in the nanotech-battery field due to lack of experimental testing and difficulty in developing evidence-based claims. However, there has been lots of research that has been done in the field to determine the optimal coating for increased electron transfer.
Although there are some coatings that are much more efficient and allow for faster electron transfer than others, we also need to take into account the interaction with the virus and how these biological processes will affect the inherent chemical composition of the material itself.
These chemical composition of the coating material as well as the electron transfer rate will be added to the inputs of our model to account for this.
These inputs for the model are definitely attainable within the next two to five years considering the current spread of next generation sequencing and the rapid spread of genomic datasets.
High-Throughput DNA Sequencing (NGS)
High-throughput DNA sequencing can be used to determine viral DNA and pass that as input into neural network models. This is largely because DNA for many viruses is not readily available and thus sequencing is needed to determine whether the virus has optimal energy density and ESR.
Next generation sequencing works in the following three steps:
- Library Prep: This step helps prepare certain DNA and RNA samples to be compatible with the DNA sequencer that is being used. These libraries are usually created by fragmenting DNA and adding adaptors to each end which allows for easier amplification and purification.
- Sequencing: Libraries are added to a flow cell and placed into the sequencer. These DNA fragments are amplified through cluster generation, resulting in millions of copies. Then, using SBS or sequencing by synthesis, tagged nucleotides bind to the DNA template strand. Since these tagged nucleotides are engineered to have fluorescent capabilities, it can note which nucleotide has been added. After reading the DNA strand with this, the reads are washed away, and the same is done for the reverse strand.
- Data Informatics: Then, the software identifies nucleotides and pairs nucleotide sequences together to reconstruct a full picture of the genome. Then, data analysis can be ran to analyze this data. In our case, the data will be passed into our input pipeline.
Note that this is a possibility for usage if the viral datasets mentioned above never come into fruition. If the viral datasets already provide enough information, this may not need to be used.
Outputs
The outputs, as discussed above, are the three benchmarks: specific power, lifetime, and cycle deficiency. The reason why these were tackled is because the major problems that have currently been seen with virus-based batteries lies how fast they degrade over time and their specific power.
Since specific power is used to determine how much power the battery can give out per unit time and thus how much current it can provide, this is one of the best statistics to determine a battery’s effectiveness. Although this is a good statistic, having a high specific power is useless if it reduces dramatically over a short period of time. Thus, the cycle deficiency rate was defined to determine how fast the specific power drops over a hundred cycles of usage.
Lastly, lifetime was used, since this metric is one that we wish to greatly improve with the use of virus-based batteries. Virus batteries provide a unique solution to conventional chemical batteries as they can survive for longer periods of time; thus, lifetime should be checked to maximize this value.
These outputs are weighted accordingly based on level of importance: the specific power was weighted mostly heavily, as a high-throughput battery is ultimately what is trying to be found with virus batteries. Then, cycle deficiency rate is weighted second-most, while lifetime comes in last. After evaluating these three characteristics for each virus and coating material combination, optimal virus and coating material configurations can be created.
Issues — Underfitting, Dimensionality, and Testing
A problem could arise with underfitting as the lack of accessible data could lead to false predictions by the neural network. However, as more data gets compiled and more analyses get performed, this underfitting problem can be fixed. This lack of data could also lead to supervised approaches failing as there are no case studies available that actually have information on the ESR and energy density capabilities, barring the M13 virus, meaning that there is no data to feed to the neural network.
However, this problem can be overcome through extrapolation of the M13 virus data, knowing that there are many similar counterparts with just small modifications to the M13 virus. This can act as the inputted data to feed into the neural network to allow for better predictions.
Another issue arises in the dimensionality of the model. Modeling how viruses interact in a 3D setting with batteries requires enormous computation; thus, for the purposes of the initial model, the virus and the battery will be considered separately and will be split into the inputs mentioned above. However, we plan to represent this in a 3D model space to simulate possible interactions and produce the best possible accuracy.
Lastly, an issue arises in the testing of the viruses and coating materials that are given as the optimal outputs of this model. These viruses and coating materials must be tested in the lab, but as shown by the MIT experiment, this can cost millions of dollars to complete. However, note that this problem arises for technology from 2007 (which was when the MIT experiment was conducted) and that current technology will significantly offset the time and cost to make the virus-based battery. Nevertheless, it continues to be an issue, and will likely need to be solved by separate observation of the virus and coating material.
Other Approaches
A clustering approach is also being taken in order to further the validity of our model even further. Particularly, a k-means clustering approach (which aims to find a user-defined k clusters through unsupervised learning techniques) or a hierarchical clustering approach (which clusters different viruses together according to their proximity of their numerical and categorical inputs) can be implemented.
For this technique, the coating material will be left alone as a distinct mutual relationship can already been seen between the M13 virus and the current coating material. Therefore, the only output that needs to be modified is the viral type.
We can identify which viruses are closely related to the M13 virus and implement them through lab testing to predict whether they hold better lifetime values, specific power, and cycle deficiency compared to the traditional M13 virus.
Note that this approach would not provide that much of a significant difference in the three statistics as the neural network techniques since we are basing most of our ML inputs off of the M13 virus; however, it still can provide relatively strong results.
The same inputs would be used for this clustering approach, which means that both the neural network and the clustering model can be implemented side by side, where we can determine which model is the best.
The Process
Virax breaks down virus-based batteries into two steps:
- Using clustering and neural network models to determine the optimal virus and coating for maximal specific power, lifetime, and viral cycle deficiency over time.
- Genetically engineering environmentally-friendly viruses to substitute chemically harmful lithium battery electrodes for slower cycle power degradation, higher energy density, and battery lifetime.
For more information behind how these viral components can be substituted to act as specific sections of a battery, look below for a detailed rundown on current virus testing with the M13 virus and lithium ion batteries.
Introduction to Viruses
Before moving on, here are some key terms you need to know:
Virus — an infectious agent that is able to multiple only within the living cells of a host (consisting of a nucleic acid molecule in a protein coat often)
Bacteriophage — a virus that infects a bacterium and reproduces inside of it, acting as a sort of parasite
M13 bacteriophage — a bacteriophage composed of single-stranded DNA (6407 nucleotides long, encapsulated in 2700 copies of the major coat protein p8)
pVIII — major coat protein of the M13 virus with a hydrophobic core, an acidic N-terminal, and a basic C-terminal (unlikely to interfere in the regulation of genes being last in operon)
pIII — minor coat protein of the M13 virus, determining the infectivity of the viron itself
Viruses, specifically the M13 viruses, have five major functionalities:
- Rapid reproduction — This is useful to us, as it allows for longer lifetime of the battery and faster assemblance of carbon nanotubes inside of the battery.
- Acellular — Since these viruses have no cytoplasm or cellular organelles, it makes it much easier to genetically engineer these viruses by inserting plasmids.
- Response to stimulation — This is important for our purposes because it allows us to add certain affinities to materials, which will be gone into more depth below.
- Hijacking of DNA/RNA — Since the virus is acting as the host in this scenario, this does not apply to a virus-based battery.
- Viral component assembly — Viral components are made inside of the cell for viral reproduction, which means that viruses that are prone to mutation must be filtered out of the ML algorithm process.
However, it’s not their classification that matters anyway. It’s their viability as a source of energy storage in a new form of batteries that could be used to power anything from electronic devices, to high grade energy output laser and fiber optic systems. Now that we know what a virus does, let’s take a look into its operating system.
This is the inside of a virus. Maybe in real life, it’s slightly less colorful, but this model is based off of thousands of imaging instances. The virus can be broken into main parts:
- The purple-pink protrusions are its envelope proteins, small membrane proteins that help with virion assembly and morphogenesis (the biological process that allows a cell or organism to develop its specific shape inside the nuclear envelope for structural formation of the organism).
- The pink ring on the virus is the fatty/viral envelope, which is a membrane typically made from the proteins and phospholipids of the virus’ various hosts, as well as some of its naturally produced glycoproteins.
- On the inside, there are yellow globules called capsids, which are the vehicles that pass the viral genes from the (green-colored) stringlike genome. To synthesize viral genes, the blue, cloudy labyrinth is are enzymes, called polymerases and transcriptase (et cetera) that lower the activation energy needed for the reaction creating nucleic acids to occur, speeding up the synthesis of DNA/RNA.
The M13 Virus
The M13 virus can be used to assist with the design of these structures and materials. They are specifically used to act in place of the anodes and cathodes within the battery and to also organize the carbon nanotubes within the battery for increased electron transport.
Two major modifications are made to the M13 virus: the VIII gene and the III gene, creating a new virus called E4. Additionally, for increased mobility, tetraglutamate was attached to the E4, which leads to increased ionic interactions and higher electroconductivity.
The VIII gene was altered since it is the main coat protein of the M13 virus; however, due to the structure of the virus, altering this gene does not affect the regulation of other genes. The structure of the gene is a hydrophobic core with an acidic N-terminal and a basic C-terminal part. The III gene acts in a similar way but is considered the minor coat protein of the M13 virus.
In terms of energy lifetime, M13 has been proven to be highly resistance exhibiting a half-life of up to 120 days. Additionally, it was completely inactivated by strongly acidic and alkaline conditions and by temperatures above 95°C. Since these viruses have a high reproductive rate, they will be able to survive for long periods of time inside of harsh environments.
Replicating the M13 Virus With Growth Receptors
For faster replication of these viruses inside of the battery, growth receptors can be used to speed up the process. Specific growth receptors, like the granulocyte-macrophage colony-stimulating factor (otherwise known as colony-stimulating factor 2 or G-CSF), have already been shown to increase viral replication within a host’s body. Although this has not been explicitly stated in the MIT paper, this could be one way to reduce both cost and time needed to fully create these virus batteries.
Using the M13 Virus
As aforementioned, the M13 virus will serve two functions: construct carbon nanotubes for increased electron transport and attract phosphate ions for increased electroconductivity.
The following steps were taken for creation of the carbon nanotubes:
- The E4 virus was genetically engineered to contain peptide groups with an affinity for single-walled carbon nanotubes. This was altered through a modification fo the pIII protein, where a common carbon nanotube-binding sequence was attached to the N-terminus of pIII.
- Silver nanoparticles were produced on the virus to increase electronic conductivity. These nanoparticles were modified to contain certain chemicals that the viruses have been genetically engineered to express affinity to in the pVIII major coat protein.
- These were precisely confirmed by direct current plasma atomic emission spectroscopy, which is a type of spectroscopy that uses three electrodes to produce a plasma stream produced by contacting the cathode with the anodes. This can be used to determine the amount of nanoparticles being produced into the virus, since measurements must be precise due to the volatility of the virus.
- The silver nanoparticles were then chlorinated and reduced to silver to enhance local electronic conductivity through the nanowires.
The following steps were taken with regards to the phosphate group affinity:
- The E4 virus was genetically engineered to contain peptide groups with an affinity for nucleating amorphous phosphate fused to a viral coat protein, which allowed for increased electrochemical conductivity and higher voltage.
- Iron phosphate groups were added to the battery after being dehydrated by thermal annealing at a temperature of 400 degrees Celsius.
- Reproduce the virus in a contained environment to stabilize the specific capacity. Use growth receptors as mentioned above to speed up the replication.
- Attach the virus as the anodes of the battery by placing battery components inside of a precipitate and stimulate the virus.
The following steps were then taken to integrate the virus with the battery:
- To genetically engineer the M13 virus as a multibiological platform, the MC#1 sequence (N′-HGHPYQHLLRVL-C′) and the MC#2 sequence (N′-DMPRTTMSPPPR-C′) were fused into the N-terminus of the pIII of the E4 virus, which already had the desired gVIII change from above.
- Viruses were incubated with the single-walled carbon nanotubes to form nanostructures.
- This structure was added as part of the anode, while a lithium ion was used for the cathode.
- The battery was connected to a LED to show functionality of the battery.
Evaluating the M13 Virus
The M13 virus’s capability with regards to the battery were evaluated in the following way:
- Postive electrodes were created by mixing iron phosphate with polytetrafluoroethylene to monitor the discharge capacity of the M13 viruses over a period of a hundred cycles.
- These numbers were presented as a Ragone plot, which helps for the comparison of various energy densities for different battery types.
- Tunneling electron microscope images can be taken to determine how the virus is growing over time.
Results
The electrochemical properties of the nanowires paired with the use of the M13 bacteriophage elicited a voltage of 2.0–4.3 V. Although this compares to a lithium ion battery’s voltage capacity, after several cycles of using the battery, the voltage and specific capacity drop heavily, showing that methods to maintain viral reproduction need to be considered in the creation of this battery.
These new batteries exhibited strong characteristics compared to traditional lithium ion batteries in other fields, maintaining a high energy density of ~200 Wh/kg and a high specific power of ~4.5 kW/kg.
Future Steps — Machine Learning for Energy Lifetime
Bayesian NP Time Series can be used to predict energy lifetime of the virus battery. A variety of different inputs can be used to determine battery lifetime, including lifetime of the virus, current ESR levels, energy density, specific power, and specific capacity levels. Other electrical measurements can also be taken, but these will be the most pertinent towards energy lifetime models.
These can be integrated into applications that can inform users whether the viral component is falling behind in terms of reproductive rate or efficiency, and resources can be allocated asynchronously to the virus at this point. We hope to integrate this functionality once the optimal virus-coating configuration has been determined for easier convenience for the user.
Applications
The applications of these virus based batteries are extensive due to their versatility and durability. The low resistance and high energy potential of these batteries allow for applications in the EV industry as substitutes to current degradable batteries to the biomedical industry as alternatives for carrying carbon nanotubes to cancer cells (being well-known in the human body).
Currently, virus-based batteries are mainly used as alternatives for lithium-air batteries in the EV space as many believe that the virus-based industry is not big enough yet to compete with well-established lithium-ion producers. It does this by taking oxygen from the atmosphere and lithium from the battery to produce an energy-efficient and environmentally productive battery.
Current electric vehicles, like the Tesla Model S and the Model 3 batteries, run at voltages of approximately 375 volts and 350 volts respectively. With the M13 virus battery being able to power 2–4.3 volts, multiple of these batteries can allow for the running of standard electric vehicles.
Although electric vehicles talk about how they plan on cutting down pollution in the atmosphere, they still leave a debilitating effect as the chemicals excreted from these battteries are unusable and harmful to the environment after they are converted for their necessary functions.
However, the addition of viruses like the M13 virus can pave the way for a cleaner future with their zero chemical emissions. After talking with leading electronics and biology companies like Tesla, Apple, General Electric, and Gingko Bioworks, they are optimistic about this future, wanting to have a net-zero environmental impact on the world at the end.
The EV space offers much to be desired as these batteries would also have higher energy density and lower resistance levels than before, as evidenced by the M13 virus. Furthermore, the coating materials and the viruses that will be found by the machine learning algorithm will produce more optimal values than this.
We plan on integrating these virus based batteries into these electronic vehicle industry, currently valued at over one trillion dollars. Batteries are one of the most vital aspects of these electronic vehicles, and these virus based batteries hold much promise for the future.
After developing our machine learning algorithm and building the optimal combination of viral materials and coatings, we plan on beta-testing these batteries in small EV companies, like Canoo and EVgo, before expanding outwards to bigger companies. Several of these companies are receptive to the idea and are willing to endorse, based on whether these voltage and energy levels increase.
The future of batteries is exciting as viruses, previously thought to be only harbingers of disease, can directly lead to a much more efficient and cleaner environment.
For more information, please view our one-pager and website.
Contact Us
Aditya Mittal: If you want to talk more, schedule a meeting: Calendly! For information about projects that I am currently working on, consider subscribing to my newsletter! Here’s the link to subscribe. If you’re interested in connecting, follow me on Linkedin, Github, and Medium.
Karthik Mittal: Hi! I am a 16 year old currently interested in the fields of machine learning and biotechnology. If you are interested in seeing more of my content and what I publish, consider subscribing to my newsletter! Check out my March newsletter here! Also, check out my LinkedIn and Github pages. If you’re interested about personal mindsets or just stuff in general, sign up for a chat using my Calendly.
Okezue Bell: My name’s Okezue, a developer and researcher obsessed with learning and building things, especially when it involves any biology or computer science. Check out my socials here, or contact me: i@okezuebell.com. Twitter | LinkedIn | Website
Yuvraj Dhindsa: My name’s Yuvraj, a researcher that’s currently working to make an impact in the world in the social media and technology space. Contact me here: LinkedIn |Instagram
