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New 3D device harnesses living brain cells for computing

Princeton researchers have combined brain cells and advanced electronics into a single 3D device that can be programmed to recognize patterns using computational techniques. Past attempts at using brain cells to do computation have relied on 2D cultures grown in a petri dish or 3D clusters that are probed and monitored from outside. The Princeton device takes a different approach, working from the inside out.

Using advanced fabrication techniques, the team created a 3D mesh made of microscopic metal wires and electrodes supported by a thin epoxy coating. Because the coating is so thin, it has just the right amount of flexibility to interface with the soft neurons that grow around it. The team used the mesh as a scaffold to culture tens of thousands of neurons into a vast 3D network that can be used to do computation.

The study was published in Nature Electronics on Apr. 23.

Quasiparticles reveal a magneto-optical transport phenomenon

Excitons are being explored in materials science and information technology as a means of storing light. These luminous quasiparticles move through individual layers of quantum materials and can absorb and emit light with high efficiency. They form when a laser pulse excites an electron, leaving behind a positively charged “hole.” The electron and hole attract each other and behave together like a new, independent particle. When the quasiparticle recombines, it emits light and can be detected in high-tech laboratories.

Excitons in ultrathin quantum materials have been intensively studied for more than a decade, including by Alexey Chernikov and his team. At the Cluster of Excellence ctd.qmat—Complexity, Topology and Dynamics in Quantum Matter—at the Universities of Würzburg and Dresden, Chernikov and an international research team based in Dresden have now made a surprising discovery: excitons can be carried along by the magnetic excitations of a quantum material and, as a result, accelerated to ultrahigh speeds. The findings are published in the journal Nature Nanotechnology.

“The fact that the motion of optical particles can be controlled by magnetism is new. Until now, we only knew that the transport of electrons could be controlled by the magnetic order in a quantum material—this is how some sensors in smartphones work, for example. This newly discovered link between optics and magnetism could open up entirely new technological possibilities,” explains Florian Dirnberger, head of an Emmy Noether Junior Research Group at the Technical University of Munich and formerly a postdoctoral researcher in Alexey Chernikov’s Chair of Ultrafast Microscopy and Photonics, where he was responsible for carrying out the research project.

Atomic-level snapshots reveal how a key copper enzyme powers nature’s chemistry

Researchers from the University of Liverpool, Japan, and Argentina have captured atomic-resolution images of an important copper-containing enzyme using advanced X-ray Free Electron Laser (XFEL) technology at SACLA in Japan. XFEL technology generates ultra-bright, ultra-short X-ray pulses, enabling atomic-scale imaging and real-time observation of chemical, biological, and physical processes.

The international team—led by Dr. Svetlana Antonyuk and Professor Samar Hasnain at the University of Liverpool, Professor Takehiko Tosha at the University of Hyogo, and Dr. Masaki Yamamoto at RIKEN SPring-8—studied a protein that plays a key role in the global nitrogen cycle. This protein converts nitrite, an essential nitrogen intermediate, into nitric oxide gas.

The new details reveal how an enzyme called copper nitrite reductase (CuNiR) from three different organisms converts nitrite to nitric oxide gas, using an electron and a proton—a vital process for both biology and the environment.

Milky Way’s ‘little cousins’ may hold clues about infant universe

Ultra-faint dwarf galaxies—tiny satellite galaxies orbiting the Milky Way—have long been seen as cosmic fossils. Now, a new study published today in Monthly Notices of the Royal Astronomical Society uses an unprecedented set of simulations to show just how powerfully these faint systems can reflect the conditions of the early universe and tell us why some galaxies grew and others did not.

They could also reveal what the universe’s earliest “climate” was like—for example, the level of radiation and how this impacted whether and where stars formed.

Dwarf galaxies are often described as small cousins of the Milky Way. They form in small dark matter halos which are predicted by the standard model of cosmology. The faintest examples of such systems are extreme in both size and fragility, and lie on the boundary of our knowledge about galaxy formation and dark matter.

Moon dust could stop being a nuisance and start reshaping how humans may build beyond Earth

As space agencies and private companies look toward a sustained human presence on the moon, a fundamental challenge centers on how to build strong, durable infrastructure without hauling every material from Earth. New research from Rice University points to an unexpected solution—transforming one of the moon’s most stubborn obstacles, its abrasive dust, into a valuable building resource. The study demonstrates that lunar regolith simulant, a terrestrial stand-in for the moon’s fine, abrasive dust, can be used to strengthen advanced composite materials. The work, published in Advanced Engineering Materials, was also selected for the cover of the journal’s latest issue.

The research was led by Denizhan Yavas, assistant teaching professor of mechanical engineering at Rice, in collaboration with Ashraf Bastawros of Iowa State University.

“This work started with a simple but powerful question,” Yavas said. “Lunar dust is typically viewed as a major obstacle to exploration because of how abrasive and pervasive it is. We asked whether that same material could instead be used as a resource—something that could actually improve the performance of structural materials.”

AI automates quantum dot voltage tuning for scaling up quantum computing

Semiconductor spin qubits are a promising candidate for the building blocks of next-generation quantum computers due to their high potential for integration and compatibility with existing semiconductor technologies. Qubits—like the 0s and 1s of a traditional computer—serve as a basic unit of information for quantum computers. However, the practical realization of these computers requires a massive number of qubits, making the development of more efficient adjustment methods a critical challenge for the field.

A research group including Yui Muto from Tohoku University’s Graduate School of Engineering, Assistant Professor Motoya Shinozaki and Associate Professor Tomohiro Otsuka from the Advanced Institute for Materials Research (WPI-AIMR), and their colleagues have successfully demonstrated a method that may help make this massive number of qubits much more manageable, moving us one step closer toward scaling up quantum computing. The findings are published in Scientific Reports.

AI accelerators deliver accurate models for challenging quantum chemistry calculations

The most demanding calculations in quantum chemistry can now be solved with graphics processing unit (GPU) supercomputers. A recently published study shows that software adapted to use GPU hardware can provide not just speed, but also the accuracy needed to solve complex chemistry problems. The work solved the two chemical structures often seen as too complex and expensive to tackle. The advance, published in the Journal of Chemical Theory and Computation, could allow researchers to make meaningful progress in designing new catalysts and improve predicted behaviors of magnetic and electronic materials.

Specifically, the research team—led by computational chemists from NVIDIA, Sandbox AQ, the Wigner Research Centre in Hungary, the Institute for Advanced Study of the Technical University of Munich in Germany, and the Department of Energy’s Pacific Northwest National Laboratory—showed that NVIDIA Blackwell architecture effectively tackles complex simulations. Here, the researchers used a mixture of mathematically precise and approximated approaches to accomplish their goal.

“Our study shows that AI-oriented hardware can do more than provide speed—it can also power chemically accurate, strongly correlated quantum chemistry at the frontier of what is computationally feasible,” said Sotiris Xantheas, a computational chemist at PNNL and study author. Xantheas also serves as the principal investigator of Scalable Predictive methods for Excitations and Correlated phenomena (SPEC), a Department of Energy initiative.

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