Small worlds come in two flavors. The complete dataset from the original mission of the planet-hunting Kepler space telescope reveals a split in the exoplanet family tree, setting super-Earths apart from mini-Neptunes.
Kepler’s final exoplanet catalog, released in a news conference June 19, now consists of 4,034 exoplanet candidates. Of those, 49 are rocky worlds in their stars’ habitable zones, including 10 newly discovered ones. So far, 2,335 candidates have been confirmed as planets and they include about 30 temperate, terrestrial worlds. Careful measurements of the candidates’ stars revealed a surprising gap between planets about 1.5 and two times the size of Earth, Benjamin Fulton of the University of Hawaii at Manoa and Caltech and his colleagues found. There are a few planets in the gap, but most straddle it.
That splits the population of small planets into those that are rocky like Earth — 1.5 Earth radii or less — and those that are gassy like Neptune, between 2 and 3.5 Earth radii.
“This is a major new division in the family tree of exoplanets, somewhat analogous to the discovery that mammals and lizards are separate branches on the tree of life,” Fulton said.
The Kepler space telescope launched in 2009 and stared at a single patch of sky in the constellation Cygnus for four years. (Its stabilizing reaction wheels later broke and it began a new mission called K2 (SN Online: 5/15/13).) Kepler watched sunlike stars for telltale dips in brightness that would reveal a passing planet. Its ultimate goal was to come up with a single number: The fraction of stars like the sun that host planets like Earth. The Kepler team has still not calculated that number, but astronomers are confident that they have enough data to do so, said Susan Thompson of the SETI Institute in Mountain View, Calif. She presented the results during the Kepler/K2 Science Conference IV being held at NASA’s Ames Research Center in Moffett Field, Calif.
Thompson and her colleagues ran the Kepler dataset through “Robovetter” software, which acted like a sieve to catch all the potential planets it contained. Running fake planet data through the software pinpointed how likely it was to confuse other signals for a planet or miss true planets.
“This is the first time we have a population that’s really well-characterized so we can do a statistical study and understand Earth analogs out there,” Thompson said.
Astronomers’ knowledge of these planets is only as good as their knowledge of their stars. So Fulton and his colleagues used the Keck telescope in Hawaii to precisely measure the sizes of 1,300 planet-hosting stars in the Kepler field of view. Those sizes in turn helped pin down the sizes of the planets with four times more precision than before.
The split in planet types they found could come from small differences in the planets’ sizes, compositions and distances from their stars. Young stars blow powerful winds of charged particles, which can blowtorch a growing planet’s atmosphere away. If a planet was too close to its star or too small to have a thick atmosphere — less than 75 percent larger than Earth — it would lose its atmosphere and end up in the smaller group. The planets that look more like Neptune today either had more gas to begin with or grew up in a gentler environment, Fulton said.
That divergence could have implications for the abundance of life in the galaxy. The surfaces of mini-Neptunes — if they exist — would suffer under the crushing pressure of such a thick atmosphere.
“These would not be nice places to live,” Fulton said. “Our result sharpens up the dividing line between potentially habitable planets and those that are inhospitable.”
Upcoming missions, like the Transiting Exoplanet Survey Satellite due to launch in 2018, will fill in the details of the exoplanet landscape with more observations of planets around bright stars. Later, telescopes like the James Webb Space Telescope, also scheduled to launch in 2018, will be able to check the atmospheres of those planets for signs of life.
“We can now really ask the question, ‘Is our planetary system unique in the galaxy?’” exoplanet astronomer Courtney Dressing of Caltech says. “My guess is the answer’s no. We’re not that special.”
Although the term “quantum computer” might suggest a miniature, sleek device, the latest incarnations are a far cry from anything available in the Apple Store. In a laboratory just 60 kilometers north of New York City, scientists are running a fledgling quantum computer through its paces — and the whole package looks like something that might be found in a dark corner of a basement. The cooling system that envelops the computer is about the size and shape of a household water heater.
Beneath that clunky exterior sits the heart of the computer, the quantum processor, a tiny, precisely engineered chip about a centimeter on each side. Chilled to temperatures just above absolute zero, the computer — made by IBM and housed at the company’s Thomas J. Watson Research Center in Yorktown Heights, N.Y. — comprises 16 quantum bits, or qubits, enough for only simple calculations.
If this computer can be scaled up, though, it could transcend current limits of computation. Computers based on the physics of the supersmall can solve puzzles no other computer can — at least in theory — because quantum entities behave unlike anything in a larger realm.
Quantum computers aren’t putting standard computers to shame just yet. The most advanced computers are working with fewer than two dozen qubits. But teams from industry and academia are working on expanding their own versions of quantum computers to 50 or 100 qubits, enough to perform certain calculations that the most powerful supercomputers can’t pull off. The race is on to reach that milestone, known as “quantum supremacy.” Scientists should meet this goal within a couple of years, says quantum physicist David Schuster of the University of Chicago. “There’s no reason that I see that it won’t work.” But supremacy is only an initial step, a symbolic marker akin to sticking a flagpole into the ground of an unexplored landscape. The first tasks where quantum computers prevail will be contrived problems set up to be difficult for a standard computer but easy for a quantum one. Eventually, the hope is, the computers will become prized tools of scientists and businesses.
Attention-getting ideas Some of the first useful problems quantum computers will probably tackle will be to simulate small molecules or chemical reactions. From there, the computers could go on to speed the search for new drugs or kick-start the development of energy-saving catalysts to accelerate chemical reactions. To find the best material for a particular job, quantum computers could search through millions of possibilities to pinpoint the ideal choice, for example, ultrastrong polymers for use in airplane wings. Advertisers could use a quantum algorithm to improve their product recommendations — dishing out an ad for that new cell phone just when you’re on the verge of purchasing one.
Quantum computers could provide a boost to machine learning, too, allowing for nearly flawless handwriting recognition or helping self-driving cars assess the flood of data pouring in from their sensors to swerve away from a child running into the street. And scientists might use quantum computers to explore exotic realms of physics, simulating what might happen deep inside a black hole, for example.
But quantum computers won’t reach their real potential — which will require harnessing the power of millions of qubits — for more than a decade. Exactly what possibilities exist for the long-term future of quantum computers is still up in the air.
The outlook is similar to the patchy vision that surrounded the development of standard computers — which quantum scientists refer to as “classical” computers — in the middle of the 20th century. When they began to tinker with electronic computers, scientists couldn’t fathom all of the eventual applications; they just knew the machines possessed great power. From that initial promise, classical computers have become indispensable in science and business, dominating daily life, with handheld smartphones becoming constant companions (SN: 4/1/17, p. 18). Since the 1980s, when the idea of a quantum computer first attracted interest, progress has come in fits and starts. Without the ability to create real quantum computers, the work remained theoretical, and it wasn’t clear when — or if — quantum computations would be achievable. Now, with the small quantum computers at hand, and new developments coming swiftly, scientists and corporations are preparing for a new technology that finally seems within reach.
“Companies are really paying attention,” Microsoft’s Krysta Svore said March 13 in New Orleans during a packed session at a meeting of the American Physical Society. Enthusiastic physicists filled the room and huddled at the doorways, straining to hear as she spoke. Svore and her team are exploring what these nascent quantum computers might eventually be capable of. “We’re very excited about the potential to really revolutionize … what we can compute.”
Anatomy of a qubit Quantum computing’s promise is rooted in quantum mechanics, the counterintuitive physics that governs tiny entities such as atoms, electrons and molecules. The basic element of a quantum computer is the qubit (pronounced “CUE-bit”). Unlike a standard computer bit, which can take on a value of 0 or 1, a qubit can be 0, 1 or a combination of the two — a sort of purgatory between 0 and 1 known as a quantum superposition. When a qubit is measured, there’s some chance of getting 0 and some chance of getting 1. But before it’s measured, it’s both 0 and 1.
Because qubits can represent 0 and 1 simultaneously, they can encode a wealth of information. In computations, both possibilities — 0 and 1 — are operated on at the same time, allowing for a sort of parallel computation that speeds up solutions.
Another qubit quirk: Their properties can be intertwined through the quantum phenomenon of entanglement (SN: 4/29/17, p. 8). A measurement of one qubit in an entangled pair instantly reveals the value of its partner, even if they are far apart — what Albert Einstein called “spooky action at a distance.” Such weird quantum properties can make for superefficient calculations. But the approach won’t speed up solutions for every problem thrown at it. Quantum calculators are particularly suited to certain types of puzzles, the kind for which correct answers can be selected by a process called quantum interference. Through quantum interference, the correct answer is amplified while others are canceled out, like sets of ripples meeting one another in a lake, causing some peaks to become larger and others to disappear.
One of the most famous potential uses for quantum computers is breaking up large integers into their prime factors. For classical computers, this task is so difficult that credit card data and other sensitive information are secured via encryption based on factoring numbers. Eventually, a large enough quantum computer could break this type of encryption, factoring numbers that would take millions of years for a classical computer to crack.
Quantum computers also promise to speed up searches, using qubits to more efficiently pick out an information needle in a data haystack.
Qubits can be made using a variety of materials, including ions, silicon or superconductors, which conduct electricity without resistance. Unfortunately, none of these technologies allow for a computer that will fit easily on a desktop. Though the computer chips themselves are tiny, they depend on large cooling systems, vacuum chambers or other bulky equipment to maintain the delicate quantum properties of the qubits. Quantum computers will probably be confined to specialized laboratories for the foreseeable future, to be accessed remotely via the internet.
Going supreme That vision of Web-connected quantum computers has already begun to Quantum computing is exciting. It’s coming, and we want a lot more people to be well-versed in itmaterialize. In 2016, IBM unveiled the Quantum Experience, a quantum computer that anyone around the world can access online for free. With only five qubits, the Quantum Experience is “limited in what you can do,” says Jerry Chow, who manages IBM’s experimental quantum computing group. (IBM’s 16-qubit computer is in beta testing, so Quantum Experience users are just beginning to get their hands on it.) Despite its limitations, the Quantum Experience has allowed scientists, computer programmers and the public to become familiar with programming quantum computers — which follow different rules than standard computers and therefore require new ways of thinking about problems. “Quantum computing is exciting. It’s coming, and we want a lot more people to be well-versed in it,” Chow says. “That’ll make the development and the advancement even faster.”
But to fully jump-start quantum computing, scientists will need to prove that their machines can outperform the best standard computers. “This step is important to convince the community that you’re building an actual quantum computer,” says quantum physicist Simon Devitt of Macquarie University in Sydney. A demonstration of such quantum supremacy could come by the end of the year or in 2018, Devitt predicts.
Researchers from Google set out a strategy to demonstrate quantum supremacy, posted online at arXiv.org in 2016. They proposed an algorithm that, if run on a large enough quantum computer, would produce results that couldn’t be replicated by the world’s most powerful supercomputers.
The method involves performing random operations on the qubits, and measuring the distribution of answers that are spit out. Getting the same distribution on a classical supercomputer would require simulating the complex inner workings of a quantum computer. Simulating a quantum computer with more than about 45 qubits becomes unmanageable. Supercomputers haven’t been able to reach these quantum wilds.
To enter this hinterland, Google, which has a nine-qubit computer, has aggressive plans to scale up to 49 qubits. “We’re pretty optimistic,” says Google’s John Martinis, also a physicist at the University of California, Santa Barbara.
Martinis and colleagues plan to proceed in stages, working out the kinks along the way. “You build something, and then if it’s not working exquisitely well, then you don’t do the next one — you fix what’s going on,” he says. The researchers are currently developing quantum computers of 15 and 22 qubits.
IBM, like Google, also plans to go big. In March, the company announced it would build a 50-qubit computer in the next few years and make it available to businesses eager to be among the first adopters of the burgeoning technology. Just two months later, in May, IBM announced that its scientists had created the 16-qubit quantum computer, as well as a 17-qubit prototype that will be a technological jumping-off point for the company’s future line of commercial computers. But a quantum computer is much more than the sum of its qubits. “One of the real key aspects about scaling up is not simply … qubit number, but really improving the device performance,” Chow says. So IBM researchers are focusing on a standard they call “quantum volume,” which takes into account several factors. These include the number of qubits, how each qubit is connected to its neighbors, how quickly errors slip into calculations and how many operations can be performed at once. “These are all factors that really give your quantum processor its power,” Chow says.
Errors are a major obstacle to boosting quantum volume. With their delicate quantum properties, qubits can accumulate glitches with each operation. Qubits must resist these errors or calculations quickly become unreliable. Eventually, quantum computers with many qubits will be able to fix errors that crop up, through a procedure known as error correction. Still, to boost the complexity of calculations quantum computers can take on, qubit reliability will need to keep improving.
Different technologies for forming qubits have various strengths and weaknesses, which affect quantum volume. IBM and Google build their qubits out of superconducting materials, as do many academic scientists. In superconductors cooled to extremely low temperatures, electrons flow unimpeded. To fashion superconducting qubits, scientists form circuits in which current flows inside a loop of wire made of aluminum or another superconducting material.
Several teams of academic researchers create qubits from single ions, trapped in place and probed with lasers. Intel and others are working with qubits fabricated from tiny bits of silicon known as quantum dots (SN: 7/11/15, p. 22). Microsoft is studying what are known as topological qubits, which would be extra-resistant to errors creeping into calculations. Qubits can even be forged from diamond, using defects in the crystal that isolate a single electron. Photonic quantum computers, meanwhile, make calculations using particles of light. A Chinese-led team demonstrated in a paper published May 1 in Nature Photonics that a light-based quantum computer could outperform the earliest electronic computers on a particular problem.
One company, D-Wave, claims to have a quantum computer that can perform serious calculations, albeit using a more limited strategy than other quantum computers (SN: 7/26/14, p. 6). But many scientists are skeptical about the approach. “The general consensus at the moment is that something quantum is happening, but it’s still very unclear what it is,” says Devitt.
Identical ions While superconducting qubits have received the most attention from giants like IBM and Google, underdogs taking different approaches could eventually pass these companies by. One potential upstart is Chris Monroe, who crafts ion-based quantum computers. On a walkway near his office on the University of Maryland campus in College Park, a banner featuring a larger-than-life portrait of Monroe adorns a fence. The message: Monroe’s quantum computers are a “fearless idea.” The banner is part of an advertising campaign featuring several of the university’s researchers, but Monroe seems an apt choice, because his research bucks the trend of working with superconducting qubits.
Monroe and his small army of researchers arrange ions in neat lines, manipulating them with lasers. In a paper published in Nature in 2016, Monroe and colleagues debuted a five-qubit quantum computer, made of ytterbium ions, allowing scientists to carry out various quantum computations. A 32-ion computer is in the works, he says.
Monroe’s labs — he has half a dozen of them on campus — don’t resemble anything normally associated with computers. Tables hold an indecipherable mess of lenses and mirrors, surrounding a vacuum chamber that houses the ions. As with IBM’s computer, although the full package is bulky, the quantum part is minuscule: The chain of ions spans just hundredths of a millimeter.
Scientists in laser goggles tend to the whole setup. The foreign nature of the equipment explains why ion technology for quantum computing hasn’t taken off yet, Monroe says. So he and colleagues took matters into their own hands, creating a start-up called IonQ, which plans to refine ion computers to make them easier to work with.
Monroe points out a few advantages of his technology. In particular, ions of the same type are identical. In other systems, tiny differences between qubits can muck up a quantum computer’s operations. As quantum computers scale up, Monroe says, there will be a big price to pay for those small differences. “Having qubits that are identical, over millions of them, is going to be really important.”
In a paper published in March in Proceedings of the National Academy of Sciences, Monroe and colleagues compared their quantum computer with IBM’s Quantum Experience. The ion computer performed operations more slowly than IBM’s superconducting one, but it benefited from being more interconnected — each ion can be entangled with any other ion, whereas IBM’s qubits can be entangled only with adjacent qubits. That interconnectedness means that calculations can be performed in fewer steps, helping to make up for the slower operation speed, and minimizing the opportunity for errors. Early applications Computers like Monroe’s are still far from unlocking the full power of quantum computing. To perform increasingly complex tasks, scientists will have to correct the errors that slip into calculations, fixing problems on the fly by spreading information out among many qubits. Unfortunately, such error correction multiplies the number of qubits required by a factor of 10, 100 or even thousands, depending on the quality of the qubits. Fully error-corrected quantum computers will require millions of qubits. That’s still a long way off.
So scientists are sketching out some simple problems that quantum computers could dig into without error correction. One of the most important early applications will be to study the chemistry of small molecules or simple reactions, by using quantum computers to simulate the quantum mechanics of chemical systems. In 2016, scientists from Google, Harvard University and other institutions performed such a quantum simulation of a hydrogen molecule. Hydrogen has already been simulated with classical computers with similar results, but more complex molecules could follow as quantum computers scale up.
Once error-corrected quantum computers appear, many quantum physicists have their eye on one chemistry problem in particular: making fertilizer. Though it seems an unlikely mission for quantum physicists, the task illustrates the game-changing potential of quantum computers.
The Haber-Bosch process, which is used to create nitrogen-rich fertilizers, is hugely energy intensive, demanding high temperatures and pressures. The process, essential for modern farming, consumes around 1 percent of the world’s energy supply. There may be a better way. Nitrogen-fixing bacteria easily extract nitrogen from the air, thanks to the enzyme nitrogenase. Quantum computers could help simulate this enzyme and reveal its properties, perhaps allowing scientists “to design a catalyst to improve the nitrogen fixation reaction, make it more efficient, and save on the world’s energy,” says Microsoft’s Svore. “That’s the kind of thing we want to do on a quantum computer. And for that problem it looks like we’ll need error correction.”
Pinpointing applications that don’t require error correction is difficult, and the possibilities are not fully mapped out. “It’s not because they don’t exist; I think it’s because physicists are not the right people to be finding them,” says Devitt, of Macquarie. Once the hardware is available, the thinking goes, computer scientists will come up with new ideas.
That’s why companies like IBM are pushing their quantum computers to users via the Web. “A lot of these companies are realizing that they need people to start playing around with these things,” Devitt says.
Quantum scientists are trekking into a new, uncharted realm of computation, bringing computer programmers along for the ride. The capabilities of these fledgling systems could reshape the way society uses computers.
Eventually, quantum computers may become part of the fabric of our technological society. Quantum computers could become integrated into a quantum internet, for example, which would be more secure than what exists today (SN: 10/15/16, p. 13).
“Quantum computers and quantum communication effectively allow you to do things in a much more private way,” says physicist Seth Lloyd of MIT, who envisions Web searches that not even the search engine can spy on.
There are probably plenty more uses for quantum computers that nobody has thought up yet.
“We’re not sure exactly what these are going to be used for. That makes it a little weird,” Monroe says. But, he maintains, the computers will find their niches. “Build it and they will come.”
Tsutomu Miyasaka was on a mission to build a better solar cell. It was the early 2000s, and the Japanese scientist wanted to replace the delicate molecules that he was using to capture sunlight with a sturdier, more effective option.
So when a student told him about an unfamiliar material with unusual properties, Miyasaka had to try it. The material was “very strange,” he says, but he was always keen on testing anything that might respond to light. Other scientists were running electricity through the material, called a perovskite, to generate light. Miyasaka, at Toin University of Yokohama in Japan, wanted to know if the material could also do the opposite: soak up sunlight and convert it into electricity. To his surprise, the idea worked. When he and his team replaced the light-sensitive components of a solar cell with a very thin layer of the perovskite, the illuminated cell pumped out a little bit of electric current.
The result, reported in 2009 in the Journal of the American Chemical Society, piqued the interest of other scientists, too. The perovskite’s properties made it (and others in the perovskite family) well-suited to efficiently generate energy from sunlight. Perhaps, some scientists thought, this perovskite might someday be able to outperform silicon, the light-absorbing material used in more than 90 percent of solar cells around the world. Initial excitement quickly translated into promising early results. An important metric for any solar cell is how efficient it is — that is, how much of the sunlight that strikes its surface actually gets converted to electricity. By that standard, perovskite solar cells have shone, increasing in efficiency faster than any previous solar cell material in history. The meager 3.8 percent efficiency reported by Miyasaka’s team in 2009 is up to 22 percent this year. Today, the material is almost on par with silicon, which scientists have been tinkering with for more than 60 years to bring to a similar efficiency level. “People are very excited because [perovskite’s] efficiency number has climbed so fast. It really feels like this is the thing to be working on right now,” says Jao van de Lagemaat, a chemist at the National Renewable Energy Laboratory in Golden, Colo.
Now, perovskite solar cells are at something of a crossroads. Lab studies have proved their potential: They are cheaper and easier to fabricate than time-tested silicon solar cells. Though perovskites are unlikely to completely replace silicon, the newer materials could piggyback onto existing silicon cells to create extra-effective cells. Perovskites could also harness solar energy in new applications where traditional silicon cells fall flat — as light-absorbing coatings on windows, for instance, or as solar panels that work on cloudy days or even absorb ambient sunlight indoors.
Whether perovskites can make that leap, though, depends on current research efforts to fix some drawbacks. Their tendency to degrade under heat and humidity, for example, is not a great characteristic for a product meant to spend hours in the sun. So scientists are trying to boost stability without killing efficiency.
“There are challenges, but I think we’re well on our way to getting this stuff stable enough,” says Henry Snaith, a physicist at the University of Oxford. Finding a niche for perovskites in an industry so dominated by silicon, however, requires thinking about solar energy in creative ways.
Leaping electrons Perovskites flew under the radar for years before becoming solar stars. The first known perovskite was a mineral, calcium titanate, or CaTiO3, discovered in the 19th century. In more recent years, perovskites have expanded to a class of compounds with a similar structure and chemical recipe — a 1:1:3 ingredient ratio — that can be tweaked with different elements to make different “flavors.”
But the perovskites being studied for the light-absorbing layer of solar cells are mostly lab creations. Many are lead halide perovskites, which combine a lead ion and three ions of iodine or a related element, such as bromine, with a third type of ion (usually something like methylammonium). Those ingredients link together to form perovskites’ hallmark cagelike pyramid-on-pyramid structure. Swapping out different ingredients (replacing lead with tin, for instance) can yield many kinds of perovskites, all with slightly different chemical properties but the same basic crystal structure.
Perovskites owe their solar skills to the way their electrons interact with light. When sunlight shines on a solar panel, photons — tiny packets of light energy — bombard the panel’s surface like a barrage of bullets and get absorbed. When a photon is absorbed into the solar cell, it can share some of its energy with a negatively charged electron. Electrons are attracted to the positively charged nucleus of an atom. But a photon can give an electron enough energy to escape that pull, much like a video game character getting a power-up to jump a motorbike across a ravine. As the energized electron leaps away, it leaves behind a positively charged hole. A separate layer of the solar cell collects the electrons, ferrying them off as electric current.
The amount of energy needed to kick an electron over the ravine is different for every material. And not all photon power-ups are created equal. Sunlight contains low-energy photons (infrared light) and high-energy photons (sunburn-causing ultraviolet radiation), as well as all of the visible light in between.
Photons with too little energy “will just sail right on through” the light-catching layer and never get absorbed, says Daniel Friedman, a photovoltaic researcher at the National Renewable Energy Lab. Only a photon that comes in with energy higher than the amount needed to power up an electron will get absorbed. But any excess energy a photon carries beyond what’s needed to boost up an electron gets lost as heat. The more heat lost, the more inefficient the cell. Because the photons in sunlight vary so much in energy, no solar cell will ever be able to capture and optimally use every photon that comes its way. So you pick a material, like silicon, that’s a good compromise — one that catches a decent number of photons but doesn’t waste too much energy as heat, Friedman says.
Although it has dominated the solar cell industry, silicon can’t fully use the energy from higher-energy photons; the material’s solar conversion efficiency tops out at around 30 percent in theory and has hit 20-some percent in practice. Perovskites could do better. The electrons inside perovskite crystals require a bit more energy to dislodge. So when higher-energy photons come into the solar cell, they devote more of their energy to dislodging electrons and generating electric current, and waste less as heat. Plus, by changing the ingredients and their ratios in a perovskite, scientists can adjust the photons it catches. Using different types of perovskites across multiple layers could allow solar cells to more effectively absorb a broader range of photons.
Perovskites have a second efficiency perk. When a photon excites an electron inside a material and leaves behind a positively charged hole, there’s a tendency for the electron to slide right back into a hole. This recombination, as it’s known, is inefficient — an electron that could have fed an electric current instead just stays put.
In perovskites, though, excited electrons usually migrate quite far from their holes, Snaith and others have found by testing many varieties of the material. That boosts the chances the electrons will make it out of the perovskite layer without landing back in a hole.
“It’s a very rare property,” Miyasaka says. It makes for an efficient sunlight absorber.
Some properties of perovskites also make them easier than silicon to turn into solar cells. Making a conventional silicon solar cell requires many steps, all done in just the right order at just the right temperature — something like baking a fragile soufflé. The crystals of silicon have to be perfect, because even small defects in the material can hurt its efficiency. The need for such precision makes silicon solar cells more expensive to produce.
Perovskites are more like brownies from a box — simpler, less finicky. “You can make it in an office, basically,” says materials scientist Robert Chang of Northwestern University in Evanston, Ill. He’s exaggerating, but only a little. Perovskites are made by essentially mixing a bunch of ingredients together and depositing them on a surface in a thin, even film. And while making crystalline silicon requires temperatures up to 2000° Celsius, perovskite crystals form at easier-to-reach temperatures — lower than 200°.
Seeking stability In many ways, perovskites have become even more promising solar cell materials over time, as scientists have uncovered exciting new properties and finessed the materials’ use. But no material is perfect. So now, scientists are searching for ways to overcome perovskites’ real-world limitations. The most pressing issue is their instability, van de Lagemaat says. The high efficiency levels reported from labs often last only days or hours before the materials break down.
Tackling stability is a less flashy problem than chasing efficiency records, van de Lagemaat points out, which is perhaps why it’s only now getting attention. Stability isn’t a single number that you can flaunt, like an efficiency value. It’s also a bit harder to define, especially since how long a solar cell lasts depends on environmental conditions like humidity and precipitation levels, which vary by location.
Encapsulating the cell with water-resistant coatings is one strategy, but some scientists want to bake stability into the material itself. To do that, they’re experimenting with different perovskite designs. For instance, solar cells containing stacks of flat, graphenelike sheets of perovskites seem to hold up better than solar cells with the standard three-dimensional crystal and its interwoven layers.
In these 2-D perovskites, some of the methylammonium ions are replaced by something larger, like butylammonium. Swapping in the bigger ion forces the crystal to form in sheets just nanometers thick, which stack on top of each other like pages in a book, says chemist Aditya Mohite of Los Alamos National Laboratory in New Mexico. The butylammonium ion, which naturally repels water, forms spacer layers between the 2-D sheets and stops water from permeating into the crystal. Getting the 2-D layers to line up just right has proved tricky, Mohite says. But by precisely controlling the way the layers form, he and colleagues created a solar cell that runs at 12.5 percent efficiency while standing up to light and humidity longer than a similar 3-D model, the team reported in 2016 in Nature. Although it was protected with a layer of glass, the 3-D perovskite solar cell lost performance rapidly, within a few days, while the 2-D perovskite withered only slightly. (After three months, the 2-D version was still working almost as well as it had been at the beginning.)
Despite the seemingly complex structure of the 2-D perovskites, they are no more complicated to make than their 3-D counterparts, says Mercouri Kanatzidis, a chemist at Northwestern and a collaborator on the 2-D perovskite project. With the right ingredients, he says, “they form on their own.”
His goal now is to boost the efficiency of 2-D perovskite cells, which don’t yet match up to their 3-D counterparts. And he’s testing different water-repelling ions to reach an ideal stability without sacrificing efficiency.
Other scientists have mixed 2-D and 3-D perovskites to create an ultra-long-lasting cell — at least by perovskite standards. A solar panel made of these cells ran at only 11 percent efficiency, but held up for 10,000 hours of illumination, or more than a year, according to research published in June in Nature Communications. And, importantly, that efficiency was maintained over an area of about 50 square centimeters, more on par with real-world conditions than the teeny-tiny cells made in most research labs.
A place for perovskites? With boosts to their stability, perovskite solar cells are getting closer to commercial reality. And scientists are assessing where the light-capturing material might actually make its mark.
Some fans have pitted perovskites head-to-head with silicon, suggesting the newbie could one day replace the time-tested material. But a total takeover probably isn’t a realistic goal, says Sarah Kurtz, codirector of the National Center for Photovoltaics at the National Renewable Energy Lab.
“People have been saying for decades that silicon can’t get lower in cost to meet our needs,” Kurtz says. But, she points out, the price of solar energy from silicon-based panels has dropped far lower than people originally expected. There are a lot of silicon solar panels out there, and a lot of commercial manufacturing plants already set up to deal with silicon. That’s a barrier to a new technology, no matter how great it is. Other silicon alternatives face the same limitation. “Historically, silicon has always been dominant,” Kurtz says. For Snaith, that’s not a problem. He cofounded Oxford Photo-voltaics Limited, one of the first companies trying to commercialize perovskite solar cells. His team is developing a solar cell with a perovskite layer over a standard silicon cell to make a super-efficient double-decker cell. That way, Snaith says, the team can capitalize on the massive amount of machinery already set up to build commercial silicon solar cells. A perovskite layer on top of silicon would absorb higher-energy photons and turn them into electricity. Lower-energy photons that couldn’t excite the perovskite’s electrons would pass through to the silicon layer, where they could still generate current. By combining multiple materials in this way, it’s possible to catch more photons, making a more efficient cell.
That idea isn’t new, Snaith points out: For years, scientists have been layering various solar cell materials in this way. But these double-decker cells have traditionally been expensive and complicated to make, limiting their applications. Perovskites’ ease of fabrication could change the game. Snaith’s team is seeing some improvement already, bumping the efficiency of a silicon solar cell from 10 to 23.6 percent by adding a perovskite layer, for example. The team reported that result online in February in Nature Energy.
Rather than compete with silicon solar panels for space on sunny rooftops and in open fields, perovskites could also bring solar energy to totally new venues.
“I don’t think it’s smart for perovskites to compete with silicon,” Miyasaka says. Perovskites excel in other areas. “There’s a whole world of applications where silicon can’t be applied.”
Silicon solar cells don’t work as well on rainy or cloudy days, or indoors, where light is less direct, he says. Perovskites shine in these situations. And while traditional silicon solar cells are opaque, very thin films of perovskites could be printed onto glass to make sunlight-capturing windows. That could be a way to bring solar power to new places, turning glassy skyscrapers into serious power sources, for example. Perovskites could even be printed on flexible plastics to make solar-powered coatings that charge cell phones.
That printing process is getting closer to reality: Scientists at the University of Toronto recently reported a way to make all layers of a perovskite solar cell at temperatures below 150° — including the light-absorbing perovskite layer, but also the background workhorse layers that carry the electrons away and funnel them into current. That could streamline and simplify the production process, making mass newspaper-style printing of perovskite solar cells more doable.
Printing perovskite solar cells on glass is also an area of interest for Oxford Photovoltaics, Snaith says. The company’s ultimate target is to build a perovskite cell that will last 25 years, as long as a traditional silicon cell.
The moon had a magnetic field for at least 2 billion years, or maybe longer.
Analysis of a relatively young rock collected by Apollo astronauts reveals the moon had a weak magnetic field until 1 billion to 2.5 billion years ago, at least a billion years later than previous data showed. Extending this lifetime offers insights into how small bodies generate magnetic fields, researchers report August 9 in Science Advances. The result may also suggest how life could survive on tiny planets or moons. “A magnetic field protects the atmosphere of a planet or moon, and the atmosphere protects the surface,” says study coauthor Sonia Tikoo, a planetary scientist at Rutgers University in New Brunswick, N.J. Together, the two protect the potential habitability of the planet or moon, possibly those far beyond our solar system.
The moon does not currently have a global magnetic field. Whether one ever existed was a question debated for decades (SN: 12/17/11, p. 17). On Earth, molten rock sloshes around the outer core of the planet over time, causing electrically conductive fluid moving inside to form a magnetic field. This setup is called a dynamo. At 1 percent of Earth’s mass, the moon would have cooled too quickly to generate a long-lived roiling interior. Magnetized rocks brought back by Apollo astronauts, however, revealed that the moon must have had some magnetizing force. The rocks suggested that the magnetic field was strong at least 4.25 billion years ago, early on in the moon’s history, but then dwindled and maybe even got cut off about 3.1 billion years ago. Tikoo and colleagues analyzed fragments of a lunar rock collected along the southern rim of the moon’s Dune Crater during the Apollo 15 mission in 1971. The team determined the rock was 1 billion to 2.5 billion years old and found it was magnetized. The finding suggests the moon had a magnetic field, albeit a weak one, when the rock formed, the researchers conclude. A drop in the magnetic field strength suggests the dynamo driving it was generated in two distinct ways, Tikoo says. Early on, Earth and the moon would have sat much closer together, allowing Earth’s gravity to tug on and spin the rocky exterior of the moon. That outer layer would have dragged against the liquid interior, generating friction and a very strong magnetic field (SN Online: 12/4/14).
Then slowly, starting about 3.5 billion years ago, the moon moved away from Earth, weakening the dynamo. But by that point, the moon would have started to cool, causing less dense, hotter material in the core to rise and denser, cooler material to sink, as in Earth’s core. This roiling of material would have sustained a weak field that lasted for at least a billion years, until the moon’s interior cooled, causing the dynamo to die completely, the team suggests.
The two-pronged explanation for the moon’s dynamo is “an entirely plausible idea,” says planetary scientist Ian Garrick-Bethell of the University of California, Santa Cruz. But researchers are just starting to create computer simulations of the strength of magnetic fields to understand how such weaker fields might arise. So it is hard to say exactly what generated the lunar dynamo, he says.
If the idea is correct, it may mean other small planets and moons could have similarly weak, long-lived magnetic fields. Having such an enduring shield could protect those bodies from harmful radiation, boosting the chances for life to survive.
August’s total solar eclipse won’t be the last time the moon cloaks the sun’s light. From now to 2040, for example, skywatchers around the globe can witness 15 such events.
Their predicted paths aren’t random scribbles. Solar eclipses occur in what’s called a Saros cycle — a period that lasts about 18 years, 11 days and eight hours, and is governed by the moon’s orbit. (Lunar eclipses follow a Saros cycle, too, which the Chaldeans first noticed probably around 500 B.C.)
Two total solar eclipses separated by that 18-years-and-change period are almost twins — compare this year’s eclipse with the Sept. 2, 2035 eclipse, for example. They take place at roughly the same time of year, at roughly the same latitude and with the moon at about the same distance from Earth. But those extra eight hours, during which the Earth has rotated an additional third of the way on its axis, shift the eclipse path to a different part of the planet. This cycle repeats over time, creating a family of eclipses called a Saros series. A series lasts 12 to 15 centuries and includes about 70 or more eclipses. The solar eclipses of 2019 and 2037 belong to a different Saros series, so their paths too are shifted mimics. Their tracks differ in shape from 2017’s, because the moon is at a different place in its orbit when it passes between the Earth and the sun. Paths are wider at the poles because the moon’s shadow is hitting the Earth’s surface at a steep angle.
Predicting and mapping past and future eclipses allows scientists “to examine the patterns of eclipse cycles, the most prominent of which is the Saros,” says astrophysicist Fred Espenak, who is retired from NASA’s Goddard Spaceflight Center in Greenbelt, Md.
He would know. Espenak and his colleague Jean Meeus, a retired Belgian astronomer, have mapped solar eclipse paths from 2000 B.C. to A.D. 3000. For archaeologists and historians peering backward, the maps help match up accounts of long-ago eclipses with actual paths. For eclipse chasers peering forward, the data are an itinerary.
“I got interested in figuring out how to calculate eclipse paths for my own use, for planning … expeditions,” says Espenak, who was 18 when he witnessed his first total solar eclipse. It was in 1970, and he secured permission to drive the family car from southern New York to North Carolina to see it. Since then, Espenak, nicknamed “Mr. Eclipse,” has been to every continent, including Antarctica, for a total eclipse of the sun.
“It’s such a dramatic, spectacular, beautiful event,” he says. “You only get a few brief minutes, typically, of totality before it ends. After it’s over, you’re craving to see it again.”
When escaping from humans, narwhals don’t just freeze or flee. They do both.
These deep-diving marine mammals have similar physiological responses to those of an animal frozen in fear: Their heart rate, breathing and metabolism slow, mimicking a “deer in the headlights” reaction. But narwhals (Monodon monoceros) take this freeze response to extremes. The animals decrease their heart rate to as slow as three beats per minute for more than 10 minutes, while pumping their tails as much as 25 strokes per minute during an escape dive, an international team of researchers reports in the Dec. 8 Science. “That was astounding to us because there are other marine mammals that can have heart rates that low but not typically for that long a period of time, and especially not while they’re swimming as hard as they can,” says Terrie Williams, a biologist at the University of California, Santa Cruz. So far, this costly escape has been observed only after a prolonged interaction with humans.
Usually, narwhals will escape natural predators such as killer whales by stealthily slipping under ice sheets or huddling in spots too shallow for their pursuers, Williams says. But interactions with humans — something that will happen increasingly as melting sea ice opens up the Arctic — may be changing that calculus. “When narwhals detect humans, they often dive quickly and disappear from sight,” says Kristin Laidre, an ecologist at the University of Washington in Seattle who studies marine mammals in the Arctic. Williams and her colleagues partnered with indigenous hunters in East Greenland to capture narwhals in nets. Then, the researchers stuck monitoring equipment to the narwhals’ backs with suction cups and released the creatures. The team tracked the tail stroke rate and cardiovascular response of the narwhals after their release, and determined how much energy the animals used during their deep escape dives.
During normal dives, narwhals reduce their heart rate to about 10 to 20 beats per minute to conserve oxygen while spending prolonged time underwater. These regular deep dives to forage for food don’t require rigorous exercise. But during escape dives after being entangled in a net for an hour or longer, “the heart rates were going down to levels of three and four beats per minute, and being maintained at that level for 10 minutes at a time,” Williams says.
The narwhals were observed making multiple dives to depths of 45 to 473 meters in the hours following escape. When fleeing, the tusked animals expended about three to six times as much energy as they normally burn while resting. The authors calculated that the frantic getaway, combined with what they called “cardiac freeze,” severely and rapidly depletes the narwhals’ available oxygen in their lungs, blood and muscles — using 97 percent of the creatures’ oxygen stores compared with 52 percent on normal dives of similar depth and duration.
“There is a concern from our group that this is just pushing the biology of these animals beyond what they can do,” Williams says. As human activity increases in the Arctic, there may be more chance of inciting this potentially harmful escape response in narwhals.
The creatures may also become more vulnerable to other human-caused disturbances, such as seismic exploration, hunting and noise from large vessels and fishing boats. The researchers plan to investigate whether these activities cause the same flee-and-freeze reaction, and whether this extreme response affects narwhals’ long-term health.
This study “provides a new physiological angle on the vulnerability of narwhals to anthropogenic disturbance, which is likely to increase in the Arctic with sea ice loss,” Laidre says. Better understanding the human impacts on narwhals is essential for conservation of this species, she adds.
Discoveries of planets around distant stars have become almost routine. But finding seven exoplanets in one go is something special. In February, a team of planet seekers announced that a small, cool star some 39 light-years away, TRAPPIST-1, hosts the most Earth-sized exoplanets yet found in one place: seven roughly Earth-sized worlds, at least three of which might host liquid water (SN: 3/18/17, p. 6).
These worlds instantly became top priorities in the search for life outside the solar system. “TRAPPIST-1 is on everybody’s wish list,” says exoplanet astronomer Lisa Kaltenegger of Cornell University. But the planets and their dim star have also stoked a raging debate about what makes a planet habitable in the first place. Astrophysicist Michaël Gillon of the University of Liège in Belgium and colleagues found the family of worlds orbiting the ultracool dwarf star, dubbed TRAPPIST-1 for the small telescope in Chile used to discover its planets.
“I don’t think the cachet of that system is going away anytime soon,” says exoplanet expert Sara Seager of MIT.
The TRAPPIST telescope team first announced in May 2016 that the star had three temperate, rocky planets. Staring at the system with the Spitzer Space Telescope for almost three weeks straight revealed that the third planet was actually four more — all Earth-sized, and three of them are in the star’s habitable zone, the region where temperatures are right for liquid water on a planet’s surface. A seventh planet was caught crossing the star as well, though follow-up observations showed it is too cold for life as we know it (SN: 6/24/17, p. 18). Similar but different Planets orbiting the star TRAPPIST-1 are a lot alike in some ways and distinct in others. The slideshow below shows each planet’s specs, including how long it takes to orbit the dwarf star, distance from the star (in astronomical units), and radius and mass relative to Earth. The number of worlds alone makes the TRAPPIST-1 system a good spot to look for life. An alien observing our solar system would think Venus, Earth and Mars all fall in the habitable zone. But only one is inhabited. The fact that TRAPPIST-1 has so many options increases the odds that the system hosts life, Seager says.
As an ultracool dwarf, TRAPPIST-1 rides the edge of what counts as a star. Such stars burn through their nuclear fuel so slowly that they can live for many billions of years, which gives any life on their planets a long time to grow and evolve. This star’s habitable zone is also incredibly close in, offering astronomers many chances to observe the planets orbiting their star.
The three planets in the habitable zone cross in front of the star every 6.10, 9.21 and 12.35 days. If two or more turn out to be habitable, then they could share life among them, either by tossing meteorites back and forth or — in the case of spacefaring civilizations — by deliberate space travel. Future space-based observatories will be able to see starlight filtering through the planets’ atmospheres, if the planets have atmospheres. Gillon and colleagues are looking for signs of escaping hydrogen, a signal that an atmosphere might be there. “We’re already preparing,” he says.
But ultracool dwarfs are also ill-tempered. They tend to emit frequent, powerful stellar flares, which could rip away a planet’s atmosphere, threatening any potential for life. The planet-hunting Kepler space telescope recently watched TRAPPIST-1 for 80 days and saw it flare 42 times. One of those flares was as strong as Earth’s 1859 Carrington Event, among the strongest geomagnetic storms ever observed.
But there are other promising systems. Recently, a similar star, Ross 128, only 11 light-years from Earth and much calmer than TRAPPIST-1, was found to have an Earth-mass planet, making it a better place to look for life, researchers reported in November in Astronomy & Astrophysics.
Whether such stars are good or bad for life is an old and open question (SN: 6/24/17, p. 18). TRAPPIST-1’s advantage is in its numbers. “We can check it, not just with one planet but with many planets,” Kaltenegger says. “You have hotter than Earth, like Earth and colder than Earth. If you wanted Goldilocks, this is the ideal scenario.”
TRAPPIST-1 is just an opening act. A bigger, more sensitive observatory called SPECULOOS is expected to be fully operational in the Chilean desert in early 2019, Gillon says. SPECULOOS will seek planets around 1,000 ultracool dwarf stars over 10 years. “We are at the edge of maybe detecting life around another star,” he says. “It’s really a possibility.”
The holiday onslaught is upon us. For some families with children, the crush of holiday gifts — while wonderful and thoughtful in many ways — can become nearly unmanageable, cluttering both rooms and minds.
This year, I’m striving for simplicity as I pick a few key presents for my girls. I will probably fail. But it’s a good goal, and one that has some new science to back it. Toddlers play longer and more creatively with toys when there are fewer toys around, researchers report November 27 in Infant Behavior and Development. Researchers led by occupational therapist Alexia Metz at the University of Toledo in Ohio were curious about whether the number of toys would affect how the children played, including how many toys they played with and how long they spent with each toy. The researchers also wondered about children’s creativity, such as the ability to imagine a bucket as a drum or a hat.
In the experiment, 36 children ages 18 to 30 months visited a laboratory playroom twice while cameras caught how they played. On one visit, the room held four toys. On the other visit, the room held 16 toys.
When in the playroom with 16 toys, children played with more toys and spent less time with each one over a 15-minute session, the researchers found. When the same kids were in a room with four toys, they stuck with each toy longer, exploring other toys less over the 15 minutes.
What’s more, the quality of the children’s play seemed to be better when fewer toys were available. The researchers noted more creative uses of the toys when only four were present versus 16. Metz and colleagues noticed that initial attempts to play with a toy were often superficial and simple. But if a kid’s interest stuck, those early pokes and bangs turned into more sophisticated manners of playing. This type of sustained engagement might help children learn to focus their attention, a skill Metz likened to a “muscle that they have to exercise.” This attentional workout might not happen if kids are perpetually exposed to lots of distracting toys.
The toys used in the study didn’t include electronic devices such as tablets. Only one of the four toys and only four of the 16 toys used batteries. Noisy toys may have their own troubles. They can cut down on parent-child conversations, scientists have found. It’s possible that electronics such as televisions or tablets would have even greater allure than other toys.
Nor do the researchers know what would happen if the study had been done in kids’ houses and with their own toys. It’s possible that the novelty of the new place and the new toys influenced the toddlers’ behavior. (As everyone knows, the toys at a friend’s house are way better than the toys a kid has at home, even when they are literally the exact same toy.)
The results don’t pinpoint the optimal number of toys for optimal child development, Metz says. “It’s a little preliminary to say this is good and that is bad,” she says. But she points out that many kids are not in danger of having too few toys. In fact, the average number of toys the kids in the study had was 87. Five families didn’t even provide toy counts, instead answering “a lot.”
“Because of the sheer abundance of toys, there’s no harm in bringing out a few at a time,” Metz says.
That’s an idea that I’ve seen floating around, and I like it. I’ve already started packing some of my kids’ toys out of sight, with the idea to switch the selection every so often (or more likely, never). Another recommendation I’ve seen is to immediately hide away some of the new presents, which aren’t likely to be missed in the holiday pandemonium, and break them out months later when the kids need a thrill.
If more nerve cells mean more smarts, then dogs beat cats, paws down, a new study on carnivores shows. That harsh reality may shock some friends of felines, but scientists say the real surprises are inside the brains of less popular carnivores. Raccoon brains are packed with nerve cells, for instance, while brown bear brains are sorely lacking.
By comparing the numbers of nerve cells, or neurons, among eight species of carnivores (ferret, banded mongoose, raccoon, cat, dog, hyena, lion and brown bear), researchers now have a better understanding of how different-sized brains are built. This neural accounting, described in an upcoming Frontiers in Neuroanatomy paper, may ultimately help reveal how brain features relate to intelligence. For now, the multispecies tally raises more questions than it answers, says zoologist Sarah Benson-Amram of the University of Wyoming in Laramie. “It shows us that there’s a lot more out there that we need to study to really be able to understand the evolution of brain size and how it relates to cognition,” she says.
Neuroscientist Suzana Herculano-Houzel of Vanderbilt University in Nashville and colleagues gathered brains from the different species of carnivores. For each animal, the researchers whipped up batches of “brain soup,” tissue dissolved in a detergent. Using a molecule that attaches selectively to neurons in this slurry, researchers could count the number of neurons in each bit of brain real estate.
For most animals, the team found the expected numbers of neurons, given a certain brain size. Those expectations came in part from work on other mammals’ brains. That research showed that with the exception of primates (which pack in lots of neurons without growing bigger brains), there’s a predictable relationship between the size of the cerebral cortex — the wrinkly outer layer of the brain that’s involved in thinking, learning and remembering — and the number of neurons contained inside it.
Story continues below interactive graphic Feeling brainy Comparing brain size and number of nerve cells in the cerebral cortex among several animal species revealed some surprises. Golden retrievers, for example, have many more nerve cells than cats, and brown bears have an unexpectedly low number of nerve cells given the relatively large size of their brain. Raccoons have a surprising number of nerve cells considering their small noggin. It’s too early, however, to say how neuron number relates to animal intelligence.
Tap or click the graph below for more information.
But some of the larger carnivores with correspondingly larger cortices had surprisingly few neurons. In fact, a golden retriever — with 623 million neurons packed into its doggy cortex —topped both lions and bears, the team found. (For scale, humans have roughly 16.3 billion neurons in the cortex.)
The brown bear is especially lacking. Despite being about 10 times bigger than a cat’s cortex, the bear’s cortex contained roughly the same number of neurons, about 250 million. “It’s just flat out missing 80 percent of the neurons that you would expect,” Herculano-Houzel says. She suspects that there’s a limit to how much food a big predator can catch and eat, especially one that hibernates. That caloric limit might also cap the number of energetically expensive neurons.
Another exception — but in the opposite direction — was the raccoon, which has a cat-sized brain but a doglike neuron number, a finding that fits the nocturnal mammal’s reputation as a clever problem-solver. Benson-Amram cautions that it’s not clear how these neuron numbers relate to potential intelligence. Raccoons are very dexterous, she says, and it’s possible that a beefed-up brain region that handles touch, part of the cortex, could account for the neuron number.
Herculano-Houzel expected large predators such as lions to have lots of neurons. “We went into this study with the expectation that being a predator would require smarts,” she says. But in many cases, a predator didn’t seem to have more neurons than its prey. A lion, for instance, has about 545 million neurons in its cerebral cortex, while a blesbok antelope, which has a slightly smaller cortex, has about 571 million, the researchers previously found.
It’s too early to say how neuron number relates to animal intelligence. By counting neurons, “we’ve figured out one side of that equation,” Herculano-Houzel says. Those counts still need to be linked to animals’ thinking abilities.
Some studies, including one by Benson-Amram, have found correlations between brain size, neuron number and problem-solving skills across species. But finding ways to measure intelligence across different species is challenging, she says. “I find it to be a really fun puzzle, but it’s a big challenge to think, ‘Are we asking the right questions?’”
The hardy souls who manage to push shorts season into December might feel some kinship with the thirteen-lined ground squirrel.
The critter hibernates all winter, but even when awake, it’s less sensitive to cold than its nonhibernating relatives, a new study finds. That cold tolerance is linked to changes in a specific cold-sensing protein in the sensory nerve cells of the ground squirrels and another hibernator, the Syrian hamster, researchers report in the Dec. 19 Cell Reports. The altered protein may be an adaptation that helps the animals drift into hibernation. In experiments, mice, which don’t hibernate, strongly preferred to hang out on a hot plate that was 30° Celsius versus one that was cooler. Syrian hamsters (Mesocricetus auratus) and the ground squirrels (Ictidomys tridecemlineatus), however, didn’t seem to notice the chill until plate temperatures dipped below 10° Celsius, notes study coauthor Elena Gracheva, a neurophysiologist at Yale University.
Further work revealed that a cold-sensing protein called TRPM8 wasn’t as easily activated by cold in the squirrels and hamsters as in rats. Found in the sensory nerve cells of vertebrates, TRPM8 typically sends a sensation of cold to the brain when activated by low temperatures. It’s what makes your fingertips feel chilly when you’re holding a glass of ice water. It’s also responsible for the cooling sensation in your mouth after you chew gum made with menthol.
The researchers looked at the gene that contains the instructions to make the TRPM8 protein in ground squirrels and switched up parts of it to find regions responsible for tolerance to cold. The adaptation could be pinned on six amino acid changes in one section of the squirrel gene, the team found. Cutting-and-pasting the rat version of this gene fragment into the squirrel gene led to a protein that was once again cold-sensitive. Hamster TRPM8 proteins also lost their cold tolerance with slightly different genetic tweaks in the same region of the gene.
The fact that it’s possible to make a previously cold-resistant protein sensitive to cold by transferring in a snippet of genetic instructions from a different species is “really quite striking,” says David McKemy, a neurobiologist at the University of Southern California in Los Angeles. As anyone who’s lain awake shivering in a subpar sleeping bag knows, falling asleep while cold is really hard. Hibernation is different than sleep, Gracheva emphasizes, but the squirrels and hamsters’ tolerance to cold may help them transition from an active, awake state to hibernation. If an animal feels chilly, its body will expend a lot of energy trying to warm up — and that’ll work against the physiological changes needed to enter hibernation. For example, while hibernating, small mammals like the ground squirrel slow their pulse and breathing and can lower their core body temperature to just a few degrees above freezing.
Modifications to TRPM8 probably aren’t the only factors that help ground squirrels ignore the cold, Gracheva says, especially as the thermometer drops even closer to freezing. “We think this is only part of the mechanism.”
Scientists also aren’t sure exactly how TRPM8 gets activated by cold in the first place. A detailed view of TRPM8’s structure, obtained using cryo-electron microscopy, was published by a different research group online December 7 in Science. “This is a big breakthrough. We were waiting for this structure for a long period of time,” Gracheva says. Going forward, she and colleagues hope that knowing the protein’s structure will help them link genetic adaptations for cold tolerance in TRPM8 with specific structural changes in the protein.
