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A research team from the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and the University of Salerno in Italy has discovered that thin films of elemental bismuth exhibit the so-called non-linear Hall effect, which could be applied in technologies for the controlled use of terahertz high-frequency signals on electronic chips.

Bismuth combines several advantageous properties not found in other systems to date, as the team reports in Nature Electronics. In particular, the quantum effect is observed at room temperature. The thin-layer films can be applied even on plastic substrates and could therefore be suitable for modern high-frequency technology applications.

“When we apply a current to certain materials, they can generate a voltage perpendicular to it. We physicists call this phenomenon the Hall effect, which is actually a unifying term for effects with the same impact, but which differ in the underlying mechanisms at the electron level. Typically, the Hall voltage registered is linearly dependent on the applied current,” says Dr. Denys Makarov from the Institute of Ion Beam Physics and Materials Research at HZDR.

Every time a cell divides, its DNA is duplicated so that the two daughter cells have the same genetic material as their parent. This means that, millions of times a day, a biochemical wonder takes place in the body: the copying of the DNA molecule. It is a high-precision job carried out by specific proteins and includes systems to protect against potential errors that could lead to diseases such as cancer.

One of these anti-failure systems has just been discovered by researchers in the DNA Replication Group at the Spanish National Cancer Research Centre (CNIO), led by Juan Méndez. It is based on a protein that ensures that DNA is copied only once, as it should be, and not twice or more.

The work is published in The EMBO Journal.

April 17, 2021, was a day like any other day on the sun, until a brilliant flash erupted and an enormous cloud of solar material billowed away from our star. Such outbursts from the sun are not unusual, but this one was unusually widespread, hurling high-speed protons and electrons at velocities nearing the speed of light and striking several spacecraft across the inner solar system.

In fact, it was the first time such high-speed protons and electrons—called solar energetic particles (SEPs)—were observed by spacecraft at five different, well-separated locations between the sun and Earth as well as by spacecraft orbiting Mars. And now these diverse perspectives on the solar storm are revealing that different types of potentially dangerous SEPs can be blasted into space by different solar phenomena and in different directions, causing them to become widespread.

“SEPs can harm our technology, such as satellites, and disrupt GPS,” said Nina Dresing of the Department of Physics and Astronomy, University of Turku in Finland. “Also, humans in space or even on airplanes on polar routes can suffer harmful radiation during strong SEP events.”

Scientists at the University of Florida have pioneered a method for using semiconductor technology to manufacture processors that significantly enhance the efficiency of transmitting vast amounts of data across the globe. The innovation, featured on the current cover of the journal Nature Electronics, is poised to transform the landscape of wireless communication at a time when advances in AI are dramatically increasing demand.

Traditionally, wireless communication has relied on planar processors, which, while effective, are limited by their two-dimensional structure to operate within a limited portion of electromagnetic spectrum. The UF-designed approach leverages the power of semiconductor technology to propel wireless communication into a new dimension—quite literally.

Researchers have successfully transitioned from planar to three-dimensional processors, ushering in a new era of compactness and efficiency in data transmission.

A global race to build powerful computer chips that are essential for the next generation of artificial intelligence (AI) tools could have a major impact on global politics and security.

The US is currently leading the race in the design of these chips, also known as semiconductors. But most of the manufacturing is carried out in Taiwan. The debate has been fueled by the call by Sam Altman, CEO of ChatGPT’s developer OpenAI, for a US$5 trillion to US$7 trillion (£3.9 trillion to £5.5 trillion) global investment to produce more powerful chips for the next generation of AI platforms.

The amount of money Altman called for is more than the chip industry has spent in total since it began. Whatever the facts about those numbers, overall projections for the AI market are mind blowing. The data analytics company GlobalData forecasts that the market will be worth US$909 billion by 2030.

After the advent of 5G, engineers have been trying to devise techniques to further enhance wireless communication technology. To increase these systems’ data transmission rate, they will ultimately need to extend their carrier frequency beyond 100 gigahertz, reaching the terahertz range.

Existing devices and technologies, however, have proved to be unable to achieve such high carrier frequencies. One proposed solution to reach this goal entails the use of some quantum materials that exhibit the so-called non-linear Hall effect.

Researchers at Helmholtz-Zentrum Dresden-Rossendorf (HZDR) e. V. and University of Salerno have identified a promising material for the development of next generation wireless communication systems, namely thin film elemental bismuth. Their paper, published in Nature Electronics, shows that this material exhibits a room-temperature nonlinear Hall effect.

Draw a line between P and Q. That line will intersect the curve at a third point, R. (Mathematicians have a special trick for dealing with the case where the line doesn’t intersect the curve by adding a “point at infinity.”) The reflection of R across the x-axis is your sum P + Q. Together with this addition operation, all the solutions to the curve form a mathematical object called a group.

Mathematicians use this to define the “rank” of a curve. The rank of a curve relates to the number of rational solutions it has. Rank 0 curves have a finite number of solutions. Curves with higher rank have infinite numbers of solutions whose relationship to one another using the addition operation is described by the rank.

Continue reading “Elliptic Curve ‘Murmurations’ Found With AI Take Flight” »

Scientists from the University of Rochester have developed new electrochemical approaches to clean up pollution from “forever chemicals” found in clothing, food packaging, firefighting foams, and a wide array of other products. A new Journal of Catalysis study describes nanocatalysts developed to remediate per-and polyfluoroalkyl substances known as PFAS.

The researchers, led by assistant professor of chemical engineering Astrid Müller, focused on a specific type of PFAS called Perfluorooctane sulfonate (PFOS), which was once widely used for stain-resistant products but is now banned in much of the world for its harm to human and animal health. PFOS is still widespread and persistent in the environment despite being phased out by US manufacturers in the early 2000s, continuing to show up in water supplies.

Generative AI is getting plenty of attention for its ability to create text and images. But those media represent only a fraction of the data that proliferate in our society today. Data are generated every time a patient goes through a medical system, a storm impacts a flight, or a person interacts with a software application.

Using generative AI to create realistic synthetic data around those scenarios can help organizations more effectively treat patients, reroute planes, or improve software platforms—especially in scenarios where real-world data are limited or sensitive.

For the last three years, the MIT spinout DataCebo has offered a generative software system called the Synthetic Data Vault to help organizations create synthetic data to do things like test software applications and train machine learning models.