Aurelia Butler

Researchers have known about high-temperature superconducting copper-based materials, or cuprates, since the 1980s. Below a certain temperature (approximately -130 degree Celsius), electrical resistance vanishes from these materials and magnetic flux fields are expelled. However, the basis for that superconductivity continues to be debated and explored.
“It has been widely accepted that traditional superconductors result from electrons interacting with phonons, where the phonons pair two electrons as an entity and the latter can run in a material without resistance,” said Yao Wang, assistant professor of physics and astronomy at Clemson University.
However, in cuprates, strong repulsions known as the Coulomb force were found between electrons and were believed to be the cause of this special and high-temperature superconductivity.
Phonons are the vibrational energy that arise from oscillating atoms within a crystal. The behavior and dynamics of phonons are very different from those of electrons, and putting these two interacting pieces of the puzzle together has been a challenge.
In November 2021, writing in the journal Physical Review Letters, Wang, along with researchers from Stanford University, presented compelling evidence that phonons are in fact contributing to a key feature observed in cuprates, which may indicate their indispensable contribution to superconductivity.
The study innovatively accounted for the forces of both electrons and phonons together. They showed that phonons impact not only electrons in their immediate vicinity, but act on electrons several neighbors away. More

Computers could mimic neural networks in the brain — and be much more energy efficient — with a new computer component that mimics how the brain works by acting like a synaptic cell. It’s called an electrochemical random access memory (ECRAM), and researchers have developed materials that offer a commercially-viable way to build these components.
Researchers from KTH Royal Institute of Technology and Stanford University have now fabricated a material for computer components that enable the commercial viability of computers that mimic the human brain.
Electrochemical random access (ECRAM) memory components made with 2D titanium carbide showed outstanding potential for complementing classical transistor technology, and contributing toward commercialization of powerful computers that are modeled after the brain’s neural network. Such neuromorphic computers can be thousands times more energy efficient than today’s computers.
These advances in computing are possible because of some fundamental differences from the classic computing architecture in use today, and the ECRAM, a component that acts as a sort of synaptic cell in an artificial neural network, says KTH Associate Professor Max Hamedi.
“Instead of transistors that are either on or off, and the need for information to be carried back and forth between the processor and memory — these new computers rely on components that can have multiple states, and perform in-memory computation,” Hamedi says.
The scientists at KTH and Stanford have focused on testing better materials for building an ECRAM, a component in which switching occurs by inserting ions into an oxidation channel, in a sense similar to our brain which also works with ions. What has been needed to make these chips commercially viable are materials that overcome the slow kinetics of metal oxides and the poor temperature stability of plastics.
The key material in the ECRAM units that the researchers fabricated is referred to as MXene — a two-dimensional (2D) compound, barely a few atoms thick, consisting of titanium carbide (Ti3C2Tx). The MXene combines the high speed of organic chemistry with the integration compatibility of inorganic materials in a single device operating at the nexus of electrochemistry and electronics, Hamedi says.
Co-author Professor Alberto Salleo at Stanford University, says that MXene ECRAMs combine the speed, linearity, write noise, switching energy, and endurance metrics essential for parallel acceleration of artificial neural networks.
“MXenes are an exciting materials family for this particular application as they combine the temperature stability needed for integration with conventional electronics with the availability of a vast composition space to optimize performance, Salleo says”
While there are many other barriers to overcome before consumers can buy their own neuromorphic computers, Hamedi says the 2D ECRAMs represent a breakthrough at least in the area of neuromorphic materials, potentially leading to artificial intelligence that can adapt to confusing input and nuance, the way the brain does with thousands time smaller energy consumption. This can also enable portable devices capable of much heavier computing tasks without having to rely on the cloud.
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Materials provided by KTH, Royal Institute of Technology. Note: Content may be edited for style and length. More

Scientists in Barcelona have today launched GENIGMA, a videogame that enlists players to solve puzzles while generating real-world scientific data that can detect alterations in genomic sequences and ultimately advance breast cancer research.
The game, out today on iOS and Android and available in English, Spanish, Catalan and Italian, is the result of a two-and-a-half-year long citizen science project developed by a team of researchers at the Centre for Genomic Regulation (CRG), the Centro Nacional de Análisis Genómico (CNAG-CRG) and game professionals.
The game was created to boost worldwide research efforts that depend on cancer cell lines, a critical resource used by scientists to study cancer and test new drugs to treat the disease. One of the limitations of cancer cell lines are a lack of high-resolution genome reference maps, which are necessary to help researchers interpret their scientific results, for example pinpointing the location of genes of therapeutic interest or potential mutation sites.
“Cell lines are responsible for the discovery of vaccines, chemotherapies for cancer or IVF for infertility. This makes them a pillar of modern biology,” explains ICREA Research Professor Marc A. Marti-Renom, with dual affiliation at the CRG and CNAG-CRG and whose research underpins GENIGMA. “However, the lack of genome reference maps limits current scientific progress. It’s like asking people to navigate modern cities using maps from the past. With the help of other people, we can update these maps, which will allow us to make fast progress in breast cancer research.”
Professor Marti-Renom’s research group has developed methods to create genomic reference maps by visualising the genome in three-dimensional space. However, this requires significant time and resources to train artificial intelligence, as well as vast computational power.
The researchers launched GENIGMA because they believe that data generated by players could be a more effective method of updating the reference maps compared to using AI alone. The ‘herd intelligence’ of players can also provide creative solutions in ways that AI might not be able to. More

A recent Oregon State University study analyzing the impact of a shorter school week for high schools found that 11th-grade students participating in a four-day week performed worse on standardized math tests than students who remained on five-day schedules.
The effect was amplified among students in non-rural schools and was limited to math; no significant gap appeared in reading achievement across different school-week schedules.
K-12 schools nationwide are increasingly moving to a four-day week as a way to provide non-monetary incentives for teachers, adjust for students’ extracurricular schedules or to cut district costs. As of the 2018-19 school year, 1,607 schools nationwide — 1.2% of all K-12 schools — had shifted to a four-day week. The loss of instruction time due to COVID-related closures has prompted more to consider how the school week can best accommodate both students and teachers.
But the shift must be made thoughtfully to be effective, researchers say.
“These bigger cuts seem to be happening in non-rural areas that haven’t thought through all the details of implementation — they may be moving to four-day school for short-term reasons, like cost savings,” said Paul Thompson, lead author on the study and a professor in OSU’S College of Liberal Arts. “That’s different from what we’re seeing in rural areas, where it’s really a lifestyle choice for these schools, and they’ve thought a lot about how they should structure their schedule.”
Oregon has the fourth-highest number of schools on a four-day week in the country, with 137 schools across 80 districts opting for the shorter school week, or roughly 11% of the more than 1,200 K-12 schools in the state. The majority of these schools are in rural areas, particularly in Eastern Oregon. More

The shape-shifting clouds of starling birds, the organization of neural networks or the structure of an anthill: nature is full of complex systems whose behaviors can be modeled using mathematical tools. The same is true for the labyrinthine patterns formed by the green or black scales of the ocellated lizard. A multidisciplinary team from the University of Geneva (UNIGE) explains, thanks to a very simple mathematical equation, the complexity of the system that generates these patterns. This discovery contributes to a better understanding of the evolution of skin color patterns: the process allows for many different locations of green and black scales but always leads to an optimal pattern for the animal survival. These results are published in the journal Physical Review Letters.
A complex system is composed of several elements (sometimes only two) whose local interactions lead to global properties that are difficult to predict. The result of a complex system will not be the sum of these elements taken separately since the interactions between them will generate an unexpected behavior of the whole. The group of Michel Milinkovitch, Professor at the Department of Genetics and Evolution, and Stanislav Smirnov, Professor at the Section of Mathematics of the Faculty of Science of the UNIGE, have been interested in the complexity of the distribution of colored scales on the skin of ocellated lizards.
Labyrinths of scales
The individual scales of the ocellated lizard (Timon lepidus) change color (from green to black, and vice versa) over the course of the animal’s life, gradually forming a complex labyrinthine pattern as it reaches adulthood. The UNIGE researchers have previously shown that the labyrinths emerge on the skin surface because the network of scales constitutes a so-called ‘cellular automaton’. “This is a computing system invented in 1948 by the mathematician John von Neumann in which each element changes its state according to the states of the neighboring elements,” explains Stanislav Smirnov.
In the case of the ocellated lizard, the scales change state — green or black — depending on the colors of their neighbors according to a precise mathematical rule. Milinkovitch had demonstrated that this cellular automaton mechanism emerges from the superposition of, on one hand, the geometry of the skin (thick within scales and much thinner between scales) and, on the other hand, the interactions among the pigmentary cells of the skin.
The road to simplicity
Szabolcs Zakany, a theoretical physicist in Michel Milinkovitch’s laboratory, teamed up with the two professors to determine whether this change in the color of the scales could obey an even simpler mathematical law. The researchers thus turned to the Lenz-Ising model developed in the 1920’s to describe the behavior of magnetic particles that possess spontaneous magnetization. The particles can be in two different states (+1 or -1) and interact only with their first neighbors.
“The elegance of the Lenz-Ising model is that it describes these dynamics using a single equation with only two parameters: the energy of the aligned or misaligned neighbors, and the energy of an external magnetic field that tends to push all particles toward the +1 or -1 state,” explains Szabolcs Zakany.
A maximum disorder for a better survival
The three UNIGE scientists determined that this model can accurately describe the phenomenon of scale color change in the ocellated lizard. More precisely, they adapted the Lenz-Ising model, usually organized on a square lattice, to the hexagonal lattice of skin scales. At a given average energy, the Lenz-Ising model favors the formation of all state configurations of magnetic particles corresponding to this same energy. In the case of the ocellated lizard, the process of color change favors the formation of all distributions of green and black scales that each time result in a labyrinthine pattern (and not in lines, spots, circles, or single-colored areas…).
“These labyrinthine patterns, which provides ocellated lizards with an optimal camouflage, have been selected in the course of evolution. These patterns are generated by a complex system, that yet can be simplified as a single equation, where what matters is not the precise location of the green and black scales, but the general appearance of the final patterns,” enthuses Michel Milinkovitch. Each animal will have a different precise location of its green and black scales, but all of these alternative patterns will have a similar appearance (i.e., a very similar ‘energy’ in the Lenz-Ising model) giving these different animals equivalent chances of survival.
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Materials provided by Université de Genève. Note: Content may be edited for style and length. More

Like the transistors in a classical computer, superconducting qubits are the building blocks of a quantum computer. While engineers have been able to shrink transistors to nanometer scales, however, superconducting qubits are still measured in millimeters. This is one reason a practical quantum computing device couldn’t be miniaturized to the size of a smartphone, for instance.
MIT researchers have now used ultrathin materials to build superconducting qubits that are at least one-hundredth the size of conventional designs and suffer from less interference between neighboring qubits. This advance could improve the performance of quantum computers and enable the development of smaller quantum devices.
The researchers have demonstrated that hexagonal boron nitride, a material consisting of only a few monolayers of atoms, can be stacked to form the insulator in the capacitors on a superconducting qubit. This defect-free material enables capacitors that are much smaller than those typically used in a qubit, which shrinks its footprint without significantly sacrificing performance.
In addition, the researchers show that the structure of these smaller capacitors should greatly reduce cross-talk, which occurs when one qubit unintentionally affects surrounding qubits.
“Right now, we can have maybe 50 or 100 qubits in a device, but for practical use in the future, we will need thousands or millions of qubits in a device. So, it will be very important to miniaturize the size of each individual qubit and at the same time avoid the unwanted cross-talk between these hundreds of thousands of qubits. This is one of the very few materials we found that can be used in this kind of construction,” says co-lead author Joel Wang, a research scientist in the Engineering Quantum Systems group of the MIT Research Laboratory for Electronics.
Wang’s co-lead author is Megan Yamoah ’20, a former student in the Engineering Quantum Systems group who is currently studying at Oxford University on a Rhodes Scholarship. Pablo Jarillo-Herrero, the Cecil and Ida Green Professor of Physics, is a corresponding author, and the senior author is William D. Oliver, a professor of electrical engineering and computer science and of physics, an MIT Lincoln Laboratory Fellow, director of the Center for Quantum Engineering, and associate director of the Research Laboratory of Electronics. The research is published today in Nature Materials.
Qubit quandaries More

The human brain is finely tuned not only to recognize particular sounds, but also to determine which direction they came from. By comparing differences in sounds that reach the right and left ear, the brain can estimate the location of a barking dog, wailing fire engine, or approaching car.
MIT neuroscientists have now developed a computer model that can also perform that complex task. The model, which consists of several convolutional neural networks, not only performs the task as well as humans do, it also struggles in the same ways that humans do.
“We now have a model that can actually localize sounds in the real world,” says Josh McDermott, an associate professor of brain and cognitive sciences and a member of MIT’s McGovern Institute for Brain Research. “And when we treated the model like a human experimental participant and simulated this large set of experiments that people had tested humans on in the past, what we found over and over again is it the model recapitulates the results that you see in humans.”
Findings from the new study also suggest that humans’ ability to perceive location is adapted to the specific challenges of our environment, says McDermott, who is also a member of MIT’s Center for Brains, Minds, and Machines.
McDermott is the senior author of the paper, which appears today in Nature Human Behavior. The paper’s lead author is MIT graduate student Andrew Francl.
Modeling localization
When we hear a sound such as a train whistle, the sound waves reach our right and left ears at slightly different times and intensities, depending on what direction the sound is coming from. Parts of the midbrain are specialized to compare these slight differences to help estimate what direction the sound came from, a task also known as localization. More