Is Carbon Dioxide the New Black? Yes, If These Fabric-Designing Scientists Have Their Way
Each year the world releases around 33 billion tons of carbon dioxide into the atmosphere. What if we could use this waste carbon dioxide to make shirts, dresses and hats? It sounds unbelievable. But two innovators are trying to tackle climate change in this truly unique way.
Chemist Tawfiq Nasr Allah set up Fairbrics with material scientist Benoît Illy in 2019. They're using waste carbon dioxide from industrial fumes as a raw material to create polyester, identical to the everyday polyester we use now. They want to take a new and very different approach to make the fashion industry more sustainable.
The Dark Side of Fast Fashion
The fashion industry is responsible for around 4% of global emissions. In a 2015 report, the MIT Materials Systems Laboratory predicted that the global impact of polyester fabric will grow from around 880 billion kg of CO2 in 2015 to 1.5 trillion kg of CO2 by 2030.
Professor Greg Peters, an expert in environmental science and sustainability, highlights the wide-ranging difficulties caused by the production of polyester. "Because it is made from petrochemical crude oil there is no real limit on how much polyester can be produced...You have to consider the ecological damage (oil spills, fracking etc.) caused by the oil and gas industry."
Many big-name brands have pledged to become carbon neutral by 2050. But nothing has really changed in the way polyester is produced.
Some companies are recycling plastic bottles into polyester. The plastic is melted into ultra-fine strands and then spun to create polyester. However, only a limited number of bottles are available. New materials must be added because of the amount of plastic degradation that takes place. Ultimately, recycling accounts for only a small percentage of the total amount of polyester produced.
Nasr Allah and Illy hope they can offer the solution the fashion industry is looking for. They are not just reducing the carbon emissions that are conventionally produced by making polyester. Their process actually goes much further. It's carbon negative and works by using up emissions from other industries.
"In a sense we imitate what nature does so well: plants capture CO2 and turn it into natural fibers using sunlight, we capture CO2 and turn it into synthetic fibers using electricity."
Experts in the field see a lot of promise. Dr Phil de Luna is an expert in carbon valorization -- the process of converting carbon dioxide into high-value chemicals. He leads a $57-million research program developing the technology to decarbonize Canada.
"I think the approach is great," he says. "Being able to take CO2 and then convert it into polymers or polyester is an excellent way to think about utilizing waste emissions and replacing fossil fuel-based materials. That is overall a net negative as compared to making polyester from fossil fuels."
From Harmful Waste to Useful Raw Material
It all started with Nasr Allah's academic research, primarily at the French Alternative Energies and Atomic Energy Commission (CEA). He spent almost 5 years investigating CO2 valorization. In essence, this involves breaking the bonds between the carbon and oxygen atoms in CO2 to create bonds with other elements.
Recycling carbon dioxide in this way requires extremely high temperatures and pressures. Catalysts are needed to break the strong bonds between the atoms. However, these are toxic, volatile and quickly lose their effectiveness over time. So, directly converting carbon dioxide into the raw material for making polyester fibers is very difficult.
Nasr Allah developed a process involving multiple simpler stages. His innovative approach involves converting carbon dioxide to intermediate chemicals. These chemicals can then be transformed into the raw material which is used in the production of polyester. After many experiments, Nasr Allah developed new processes and new catalysts that worked more effectively.
"We use a catalyst to transform CO2 into the chemicals that are used for polyester manufacturing," Illy says. "In a sense we imitate what nature does so well: plants capture CO2 and turn it into natural fibers using sunlight, we capture CO2 and turn it into synthetic fibers using electricity."
The Challenges Ahead
Nasr Allah met material scientist Illy through Entrepreneur First, a programme which pairs individuals looking to form technical start-ups. Together they set up Fairbrics and worked on converting Nasr Allah's lab findings into commercial applications and industrial success.
"The main challenge we faced was to scale up the process," Illy reveals. "[It had to be] consistent and safe to be carried out by a trained technician, not a specialist PhD as was the case in the beginning."
They recruited a team of scientists to help them develop a more effective and robust manufacturing process. Together, the team gained a more detailed theoretical understanding about what was happening at each stage of the chemical reactions. Eventually, they were able to fine tune the process and produce consistent batches of polyester.
They're making significant progress. They've produced their first samples and signed their first commercial contract to make polyester, which will then be both fabricated into clothes and sold by partner companies.
Currently, one of the largest challenges is financial. "We need to raise a fair amount to buy the equipment we need to produce at a large scale," Illy explains.
How to Power the Process?
At the moment, their main scientific focus is getting the process working reliably so they can begin commercialization. In order to remain sustainable and economically viable once they start producing polyester on a large scale, they need to consider the amount of energy they use for carbon valorization and the emissions they produce.
The more they optimize the way their catalyst works, the easier it will be to transform the CO2. The whole process can then become more cost effective and energy efficient.
De Luna explains: "My concern is...whether their process will be economical at scale. The problem is the energy cost to take carbon dioxide and transform it into these other products and that's where the science and innovation has to happen. [Whether they can scale up economically] depends on the performance of their catalyst."
They don't just need to think about the amount of energy they use to produce polyester; they also have to consider where this energy comes from.
"They need access to cheap renewable energy," De Luna says, "...so they're not using or emitting CO2 to do the conversion." If the energy they use to transform CO2 into polyester actually ends up producing more CO2, this will end up cancelling out their positive environmental impact.
Based in France, they're well located to address this issue. France has a clean electricity system, with only about 10% of their electric power coming from fossil fuels due to their reliance on nuclear energy and renewables.
Where Do They Get the Carbon Dioxide?
As they scale up, they also need to be able to access a source of CO2. They intend to obtain this from the steel industry, the cement industry, and hydrogen production.
The technology to purify and capture waste carbon dioxide from these industries is available on a large scale. However, there are only around 20 commercial operations in the world. The high cost of carbon capture means that development continues to be slow. There are a growing number of startups capturing carbon dioxide straight from the air, but this is even more costly.
One major problem is that storing captured carbon dioxide is expensive. "There are somewhat limited options for permanently storing captured CO2, so innovations like this are important,'' says T. Reed Miller, a researcher at the Yale University Center for Industrial Ecology.
Illy says: "The challenge is now to decrease the cost [of carbon capture]. By using CO2 as a raw material, we can try to increase the number of industries that capture CO2. Our goal is to turn CO2 from a waste into a valuable product."
Beyond Fashion
For Nasr Allah and Illy, fashion is just the beginning. There are many markets they can potentially break into. Next, they hope to use the polyester they've created in the packaging industry. Today, a lot of polyester is consumed to make bottles and jars. Illy believes that eventually they can produce many different chemicals from CO2. These chemicals could then be used to make paints, adhesives, and even plastics.
The Fairbrics scientists are providing a vital alternative to fossil fuels and showcasing the real potential of carbon dioxide to become a worthy resource instead of a harmful polluter.
Illy believes they can make a real difference through innovation: "We can have a significant impact in reducing climate change."
Podcast: The Friday Five weekly roundup in health research
The Friday Five covers five stories in health research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Covered in this week's Friday Five:
- Sex differences in cancer
- Promising research on a vaccine for Lyme disease
- Using a super material for brain-like devices
- Measuring your immunity to Covid
- Reducing dementia risk with leisure activities
One day in recent past, scientists at Columbia University’s Creative Machines Lab set up a robotic arm inside a circle of five streaming video cameras and let the robot watch itself move, turn and twist. For about three hours the robot did exactly that—it looked at itself this way and that, like toddlers exploring themselves in a room full of mirrors. By the time the robot stopped, its internal neural network finished learning the relationship between the robot’s motor actions and the volume it occupied in its environment. In other words, the robot built a spatial self-awareness, just like humans do. “We trained its deep neural network to understand how it moved in space,” says Boyuan Chen, one of the scientists who worked on it.
For decades robots have been doing helpful tasks that are too hard, too dangerous, or physically impossible for humans to carry out themselves. Robots are ultimately superior to humans in complex calculations, following rules to a tee and repeating the same steps perfectly. But even the biggest successes for human-robot collaborations—those in manufacturing and automotive industries—still require separating the two for safety reasons. Hardwired for a limited set of tasks, industrial robots don't have the intelligence to know where their robo-parts are in space, how fast they’re moving and when they can endanger a human.
Over the past decade or so, humans have begun to expect more from robots. Engineers have been building smarter versions that can avoid obstacles, follow voice commands, respond to human speech and make simple decisions. Some of them proved invaluable in many natural and man-made disasters like earthquakes, forest fires, nuclear accidents and chemical spills. These disaster recovery robots helped clean up dangerous chemicals, looked for survivors in crumbled buildings, and ventured into radioactive areas to assess damage.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.