The silver lines that crisscross the face of solar panels are essentially metal wires. They’re necessary to channel the electric current flowing out of the cells, but they reflect about 5% of the sunlight that reaches them, creating the single biggest drain on their efficiency.
Rebecca Saive, an assistant professor in applied physics at the University of Twente in the Netherlands, has invented a novel type of “front contact” that addresses this problem, reducing the wasted sunlight and improving the performance of solar photovoltaics.
Her transparent contacts are made from silver nanoparticles 3D-printed onto the silicon layer of a solar cell, using a technique she developed that produces an extremely thin and precise triangular shape. The steeply angled sidewalls reflect arriving light toward the absorbing body of the cell like a mirror, boosting electricity output by at least 5% and lowering costs roughly the same amount.
ETC Solar—a startup Saive cofounded with headquarters in Pasadena, California, and Rotterdam—produces a printing tool that enables manufacturers to integrate the technology into otherwise standard photovoltaics. It’s already selling the product, though the company hasn’t announced customers yet.
Meanwhile, ETC and Saive’s academic team at the University of Twente are using the front contacts and other advances to develop even more efficient solar cells that she says could eventually lead to solar plants that produce lower-cost electricity, and even to solar-powered cars.
CRISPR has been called the discovery of the century for its potential to change biomedical research and treatment of genetic diseases. But it was Omar Abudayyeh who helped turn the gene-editing tool into a diagnostic test, one that might help slow down the covid-19 pandemic.
Seizing on the precise gene-finding mechanism, in 2016 Abudayyeh, along with Jonathan Gootenberg and other colleagues at MIT, forged CRISPR into a tool to spot cancer mutations, bacteria, and mosquito-borne viruses like Zika. Soon, there was a spinout startup company called Sherlock Biosciences, $49 million in funding, and newspaper stories about CRISPR’s “new capabilities.”
Then came covid-19. Genetic tests to spot the pathogen were in desperately short supply in the US, with the workhorse technology, PCR, floundering. By early May, three months into the outbreak, around 2% of Americans had been tested for covid-19. Some economists say the country needs to test that many people every day to reopen with confidence.
That’s why, since January, Abudayyeh and his colleagues have been trying to forge CRISPR into an at-home test for the virus. The basic chemistry is simple enough, they think, to create an easy-to-use test that you could give yourself before heading to work, or maybe take at an airport gate before catching a flight.
If they succeed, virus testing could happen anywhere, anytime, and the gene-editing revolution would reach directly into people’s homes and lives for the first time.
Here’s how it works
The CRISPR revolution began with discoveries, in the early 2000s, that bacteria had evolved a way to chop up marauding phage viruses. CRISPR, whose name is an acronym for this natural biological invention, can spot unique sequences of DNA letters and cleave them with a cutting enzyme, Cas9. The tool, it turned out, was easy to use and worked in many species. Biotech startups began racing to treat genetic disease in humans. Gene-edited human twins were even born in China.
During what he calls the “Cas9 craze,” Abudayyeh was drafted into a less visible avenue of research: the effort to discover and characterize novel CRISPR enzymes.
Soon the list was growing, and Abudayyeh and colleagues were demonstrating what the new editors could do. There would be Cpf1, also known as Cas12a, and then Cas12b. But one called Cas13, discovered literally under our noses (it’s part of a human oral bacterium called Leptotrichia shahii), was special. Instead of cutting DNA, the enzyme could instead target RNA, the genetic messenger molecule inside of cells, which is also the primary genetic material of many viruses, including the coronavirus.
It was a totally new way to edit. What hadn’t changed was Abudayyeh’s close and ongoing collaboration with fellow gene editor Jonathan Gootenberg. The pair first met as MIT undergrads and then worked together in the busy lab of CRISPR pioneer Feng Zhang (who made our list of 35 innovators in 2013) at the Broad Institute. They’ve written 28 papers together, and in 2017 they were hired to establish a joint lab at the MIT McGovern Institute, which they christened the “AbuGoot Lab.”
“We joke that it’s a scientific bromance that just keeps on going,” says Abudayyeh, who reckons he’s the more practical of the two, while Gootenberg is more mathematical. “Our brains haven’t quite merged, but it’s close.”
And they needed two heads to understand the new RNA editor, Cas13. The enzyme turned out to have a bizarre “collateral effect.” Not only did it cut specific RNA strands, but once it got going, it would furiously chop up and degrade any RNA in its path. “The mechanism was insane and very confusing at first,” says Abudayyeh. “We think it’s part of a cell-suicide mechanism”—a natural self-destruct device in bacteria attacked by a virus. “When it activates, it shuts down everything in the cell.”
The indiscriminate cutting, though, meant Cas13 wasn’t a great editor on its own. “It was kind of disappointing, but we came from an engineering background, so we asked what it is good for,” says Abudayyeh. Maybe they could blow up RNA in a cancer cell, bringing it to a halt?
The idea that the collateral damage could turn CRISPR into a lab diagnostic was first floated by scientists from the rival laboratory of Jennifer Doudna at the University of California, Berkeley. There a team proposed that indiscriminate cutting could serve as a detection mechanism. In short, if the enzyme found a match in a test tube—a piece of RNA belonging to a virus, say—the collateral cutting could be used to sever special RNA that, when broken, would set off a visible fluorescent signal.
Great idea, but on its own, Cas13 wasn’t sensitive enough to create a test. So Abudayyeh and Gootenberg got help from MIT professor Jim Collins, who showed them how to add a preamplification step, or a way to copy and multiply the RNA before testing for a match. By 2017, the group was showing off a complete CRISPR diagnostic system called Sherlock that could locate unique mutations that cause cancer or flag the presence of bacteria, or even the Zika virus. And it was highly accurate. Imagine being able to pick out one person’s face from the population of 100 million Earths. That’s the equivalent of what Sherlock could do in sorting through RNA.
Sherlock soon had competition from the Berkeley team, which started its own CRISPR diagnostics company, Mammoth Biosciences. One result: a tangle of competing diagnostic patents that is reminiscent of the bruising, costly fight between the two institutions over the original CRISPR inventions. Abudayyeh shrugs: “It’s more exciting when you have more than one group working on it. And it’s better for CRISPR diagnostics that it’s not just one company trying to peddle a technology.”
He’s right: reaching the market is the hard part. That’s because diagnostic testing is a business of giant companies, big machines, and centralized labs. It can take a hundred million dollars to develop a test that sells for $45. “Not for the faint of heart” is how venture capitalist Bruce Booth once described the business. By late 2019, Sherlock, a company Abudayyeh cofounded, was still edging the CRISPR-based tests toward the market.
But then the pandemic exploded out of China and changed everything. When the shortage of tests in the US became clear, the Food and Drug Administration started giving emergency approvals to makers of dozens of tests, allowing them into the market immediately. In May, Sherlock Biosciences won US authorization to perform a version of the CRISPR test that had to be done in a lab, although at press time no one had yet used it on a patient.
A home test
Still, it wasn’t easy enough for someone without training to use. Back on MIT’s campus, Abudayyeh, Gootenberg, and Zhang set out to simplify the technology. They reasoned that if they could eliminate some of the fluid mixing steps, the test could be used in workplaces, in pharmacies, or even at home. It didn’t need repeated heating and cooling, as PCR does. And the readout was easy to understand: just colored bars on a paper strip, like a pregnancy test. “Our vision is really testing that can be done at home,” says Abudayyeh. “So how can we push this so it’s fewer steps, simple, and cheap?”
Right now, so-called point-of-care diagnostic tests do exist, but they need to be run on machines that cost thousands of dollars. One device, ID NOW, which is sold by Abbott, returns coronavirus results in 15 minutes and is used by the White House to screen visitors meeting with President Donald Trump. But the machine that processes the test costs thousands to buy. Abudayyeh says CRISPR home tests might cost $6 each and only use simple equipment.
By May, the researchers had created a simplified version and launched a website to share the new chemistry, which they showed could spot the coronavirus in swabs from patients. They are working with a design firm to create a prototype of a plastic cartridge to hold and mix the test ingredients. So has Abudayyeh tested himself? He hasn’t. “It’s tempting to spit in the tube,” he says. “But it’s also a scary thing to do.”
Pretty soon, though, people around the world may be having such “Do I or don’t I have it?” moments regularly, or at least that’s the hope.
The work is “not final,” Abudayyeh says. “Final is a simple device you can spit in. But this is the version of the chemistry that would work for the home. I think our goal right now is to have it ready for the fall. For when the second wave comes.”
Photo by David Vintiner
Christina Boville helped design a process that improves on biology’s way of controlling chemical reactions. She starts with natural enzymes—proteins that enable chemical reactions in living cells—and engineers them to produce useful chemicals that don’t exist in nature. The approach can reduce manufacturing times for compounds used in the pharmaceutical industry from months to days, shrink waste by up to 99%, and cut energy consumption in half.
In 2019, Boville cofounded Aralez Bio with David Romney and Frances Arnold, who won a Nobel Prize in 2018 for a new way of creating enzymes called directed evolution. Boville’s process creates chemicals known as non-canonical amino acids (ncAAs), which are used in making 12% of the 200 best-selling medicines, including those for migraines and diabetes, and are also used in agriculture. “Nature was built using 20 amino acids, and now our enzymes can make hundreds more,” she says. Drug ingredients “normally take five to 10 steps to make,” she adds, “but we can do it in a single step.”
Aralez Bio was recently approached by a pharmaceutical company to produce ncAAs that had taken the company nine months to make with conventional methods. Boville’s enzymes now makes the same compound overnight.
Training a typical natural-language processor requires so much computing power that it emits as much carbon as the life span of five American cars. Training an image recognition model releases as much energy as a typical home puts out in two weeks—and it’s something that leading tech companies do multiple times a day.
Much of the energy use in modern computing comes from the fact that data needs to be constantly transferred back and forth between memory and the processor. Manuel Le Gallo is working with a research team at IBM that’s building technology to enable new kinds of computing architecture that aims to be faster and more energy efficient but still highly precise.
Le Gallo’s team developed a system that uses memory itself to process data, and his team’s early work has shown they can achieve both precision and huge energy savings. The team recently completed a process using just 1% as much energy as when the same process was performed with conventional methods.
As companies from the financial sector to life sciences constantly train their AI models to improve them, their energy needs will balloon. “What will change is we will be able to change models faster and more energy efficiently, which will definitely reduce the carbon footprint and energy spent training those models,” Le Gallo says.
Photo by Samuel Trümpy
Nadya Peek began tinkering with machines out of stubbornness.
As an undergraduate, when she collaborated with artists on their installations, she often ran into limitations with the tools and equipment they were using. Rather than accept her fate, she hacked the machines until they finally did what she wanted. It got her thinking: why couldn’t machines be more flexible? What if instead of changing your idea to fit the tools, you could change the tools to fit your idea? Thus began her quest to create application-specific machines that could help anyone do almost anything.
Peek is now an assistant professor at the University of Washington, where she dedicates herself to this vision. She designs modular components—motors, mechanical arms, and material cutters—that can be assembled every which way and programmed with a little bit of code to carry out tasks from the frivolous to the scientific. When she teaches people to use her components, she delights in their creativity: they’ve made T-shirt-designing machines and cocktail-mixing machines, 3D printers, and chemistry pipetting machines. The machines are often no larger than a desktop and can be broken down and reassembled for new tasks once they’ve outlived their original use.
Peek tries to make her tools as low-cost and accessible as possible: some use only cardboard for their frames, and the designs are available to download. Her machines have been used by students, hackers, and even architects.
Peek’s goal is to give anyone with an idea the means to translate it into physical reality. She notes that computers were originally designed to carry out specific tasks, but evolved to be more general-purpose. She thinks machines that automate physical tasks should be no different. “I ultimately really would like to see automation as ... just another thing that you can use for creative problem solving,” she says.
Photo by Dakota Lenox
Leila Pirhaji built an AI-based tool for measuring tiny molecules in the body called metabolites, and her work could help us better detect and treat diseases. “There are 100,000 metabolites in the body,” she says. “They are involved in our metabolism and are downstream from DNA, so they show the effects of both our genes and lifestyle.” Such metabolites include everything from blood sugars and cholesterol to obscure molecules that appear in significant numbers only when someone is sick.
The problem is that measuring and identifying metabolites is expensive and time consuming, and fewer than 5% of metabolites in a patient can be identified using common technologies.
So Pirhaji developed a platform that uses machine learning to do it much more quickly. First she built a huge database of all known information about existing metabolites and how they interact with various proteins and other molecules. Then her team collected tissue and blood samples from patients with known diseases, and measured the metabolites.
Her platform was able to analyze the data, understand the complex connections between diseases and metabolites, and use this information to discover new drugs. When she tested it in a mouse with Huntington’s disease during her PhD at MIT, her team learned new mechanisms for the disease and found new potential ways of treating it.
As CEO of ReviveMed, Pirhaji is focusing on liver, immune, inflammatory, and other diseases. Using her platform, the startup partners with major pharmaceutical companies to match existing medicines to new treatments and find new targets for future drugs
Randall Platt has created a way to record molecular events in a cell across time—a technology that has the potential to transform our understanding of a number of important biological processes.
Currently, for instance, one of the best tools available to understand the molecular processes that occur during embryonic development or immune responses to cancer is RNA-seq, a technique that allows biologists to develop a snapshot of how genes are being expressed—which ones are being turned on or off—at a single moment in time. But while RNA-seq provides a snapshot, Platt’s tool could potentially be used to record the equivalent of a brief video, capturing gene expression over time and thus providing a much richer picture of, say, an embryo’s development.
“At the core of all of biology and biomedicine is looking at transitions in systems—whether it be a stem cell that develops into a neuron or a healthy neuron that develops into a degenerative neuron,” he says. “How people approach this problem today is they perform time-point experiments and then kind of guess what’s happening in between. I was going after a technology that would fill that gap—what was happening to the cells throughout this transition.”
Platt has big ambitions for his tool. He invented it to deal with a problem that repeatedly frustrated him when he was a graduate student at MIT. A group identified a gene that, when mutated and missing, appeared to play a role in autism—though precisely when the gene affected the brain’s development remained a mystery.
“If you want to identify a meaningful defect in a neuron you need to know exactly when, where, and how to look,” he says. “This was the biological problem that motivated me to create the recording tool.”
Venkat Viswanathan, an associate professor at Carnegie Mellon, has made major strides in developing anodes made out of pure lithium, promising a new class of batteries that pack more energy and deliver more power for a given amount of weight. That could enable cheaper electric vehicles and low-emissions aircraft.
Researchers have long recognized that lithium-metal anodes could boost the performance of batteries over ones made of graphite. But they’re prone to developing needle-like “dendrites” as lithium ions build up. This can shorten the battery’s life and even spark fires. Viswanathan’s solution was developing a hybrid polymer-ceramic separator between the electrodes. It applies enough pressure to prevent the dendrites from forming but still allows ions to flow through the battery, which produces the electric current.
Viswanathan and colleagues secured more than $4 million from the Energy Department’s moonshot ARPA-E program, and partnered with battery maker 24M Technologies to produce and test commercial-size lithium-metal cells.
Viswanathan has also worked with Aurora Flight Sciences and Airbus A3 on battery designs for vertical takeoff and landing aircraft, which can function as air taxis or ambulances that zip across metropolitan areas.
If there’s one thing that frustrates Anastasia Volkova, it’s inefficiency. So when she realized she could combine remote sensing data with scientific modeling to improve crop yields, reduce the use of agricultural chemicals, and make better use of water, she knew she’d found her life’s work. It didn’t matter that she was still pursuing her doctorate in aerospace at Sydney University or that she would need to single-handedly raise more than $5 million in startup money: Volkova, the daughter of a self-taught botanist and the goddaughter of a successful farmer, wanted to fix what she thought was wrong with large-scale farming.
Her resulting venture, Flurosat, uses imaging sensors on satellites, planes, and drones to detect when crops are in trouble long before their distress is discernible to the naked eye. Like humans, plants spike a fever when they’re sick. They also heat up in response to pests or because they’re not getting the nutrition or water they need. Flurosat uses multispectral and thermal cameras to record these changes and AI to calibrate crop models. Comparing a real crop with its digital twin then enables Volkova and her team to make real-time recommendations to agronomists and farm managers about what their yields need to thrive.
This kind of monitoring and support could reduce the overuse of nitrogen, pesticides, and herbicides and optimize irrigation.
Microchips are usually etched into a substrate of brittle silicon crystals. That means if you try to bend or stretch them, their molecular structures break and performance drops dramatically. Circuits that aren’t as fragile have been around for a while, but they’ve always had to trade off performance or ease of manufacturing to achieve flexibility. Sihong Wang, however, has developed new manufacturing techniques to build circuits that can stretch and bend while performing just as well as an inorganic semiconductor circuit.
Building on his previous work with Zhenan Bao at Stanford, one of the field’s pioneers, Wang has created a set of new processes that dramatically move things forward. Using a physical effect known as nanoconfinement to build layered polymer circuits at the smallest possible scale, he can now reliably build high-performance circuits that can be stretched to twice their original length without losing any performance.
These rubbery polymers, he says, open up whole new classes of devices—malleable enough to be molded to your shape, applied as a skin patch, or even inserted inside the body, while able to do everything just as well as a more traditional machine. But that means a set of new problems to solve. How do you power them? He’s already got ways to harness energy from the human body—using another invention called a “nanogenerator”—rather than requiring external batteries. Can these then be placed inside the body without triggering an immune response? That’s next.