A research team has broken down walls, or moved magnetic domain walls to be exact, in the field of spintronics. They uncovered a new mechanism that enables faster and more efficient movement of magnetic domain walls in spintronic materials. By harnessing dual spin torques in a cobalt–iridium–platinum multilayer structure, the team demonstrated a unique form of spin-driven motion. The findings could inform the design of next-generation, low-power spintronic memory devices. 

In the marine green alga Codium fragile, unusual carotenoids rapidly dissipate harmful chlorophyll triplet states, protecting the organism from light-induced damage. Using EPR spectroscopy and quantum chemical simulations, the study revealed the structural and electronic principles behind this photoprotection, offering insights for potential bio-inspired solar technologies.

The National Institute of Information and Communications Technology (NICT) and the Nagoya Institute of Technology (NITech), collaborated with the Japan Aerospace Exploration Agency (JAXA), have achieved the world’s first successful demonstration of next-generation error correction codes, mitigating the impact of atmospheric turbulence on ground-to-satellite laser communications.

Atmospheric turbulence in ground-to-satellite laser links is known to cause fading, resulting in burst data errors. Error correction codes are one of the key technologies to mitigate such effects. In this experiment, we transmitted next-generation error correction codes with high correction capability (5G NR LDPC and DVB-S2) and successfully corrected burst data errors caused by atmospheric turbulence in the laser link. This result confirmed that both codes can significantly improve communication quality compared to conventional schemes.

This achievement is expected to contribute to the practical implementation of ground-to-satellite laser communications by applying these codes.

The University of Osaka researchers developed a world-first sustainable method for synthesizing pharmaceutical-grade NOBIN. By cooperatively utilizing a vanadium catalyst and energy-efficient LED light, the process eliminates byproducts, reduces waste, and allows for ideal raw material ratios, paving the way for greener and more efficient chiral molecule production.

The confinement performance of magnetically confined fusion plasmas is affected by turbulence at various scales. Understanding not only the effects of turbulence at each scale but also the interactions between these turbulent eddies is a critical research challenge for realizing efficient fusion power reactors.

A research group led by Professor Tokihiko Tokuzawa and Project Professor Katsumi Ida of the National Institute for Fusion Science, graduate student Tatsuhiro Nasu of the Graduate University for Advanced Studies, and Professor Shigeru Inagaki of Kyoto University has developed a precise measurement system capable of simultaneously observing turbulence at different scales at the same location within the high-temperature plasma of the Large Helical Device (LHD). They discovered that large turbulent eddies deform smaller turbulent eddies, thereby suppressing their growth. Conventional models of plasma confinement did not account for this cross-scale interaction mechanism. This finding provides important insights for predicting the plasma confinement performance in future fusion power reactors.

 

A paper detailing these research findings was published in the journal Communications Physics on October 6th.

By combining ground-based direct imaging and radial velocity measurements with precise astrometric acceleration data from the Gaia space telescope, astronomers have discovered a brown dwarf companion orbiting a nearby red dwarf star about 55 light-years from Earth. The team accurately determined its mass (≈60 Jupiter masses) and orbital distance (≈4.3 AU) using Subaru’s Infrared Doppler instrument (IRD) under the IRD Strategic Program, Keck high-contrast imaging, and Gaia data. The object, classified as a late-T-type brown dwarf, shows roughly 30% variability in near-infrared brightness, offering a valuable benchmark for studying atmospheric clouds and circulation. This marks the first successful application of Gaia-only astrometric acceleration to precisely characterize a companion beyond Hipparcos’ detection limits.

The ability of CD8⁺ T cells, a type of immune cell, to rapidly proliferate inside tumors is key to the success of cancer immunotherapy. In a new study, scientists from Tokyo University of Science, Japan, have identified a set of ‘signature’ genes that can determine whether these immune cells will multiply or stall within the tumor. Their findings provide a powerful pan-immunotherapy biomarker for treatment monitoring and pave the way for next-generation immunodynamic therapies.

High costs have long held back hydrogen production from water, with electrolyzers priced at $2,000–$2,600 per kilowatt in 2024. Now, researchers from Japan have found that modifying platinum cathodes with naturally occurring purine bases can boost the hydrogen evolution reaction (HER) activity, the key step where water is split into hydrogen, up to four times. This approach can significantly reduce platinum requirements, bringing affordable, large-scale hydrogen production closer to reality.

Traditional geotechnical investigations provide data only at discrete borehole locations, leaving vast areas uncharacterized. This spatial gap often leads to unforeseen ground conditions during construction, causing costly delays, design modifications, and occasionally catastrophic failures. Now, a novel integrated geophysical-machine learning approach, using k-means clustering technique, by a team of researchers from Shibaura Institute of Technology provides continuous subsurface characterization, enabling evidence-based decision-making throughout project lifecycles.

Understanding molecular diversity is fundamental to biomedical research and diagnostics, but existing analytical tools struggle to distinguish subtle variations in the structure or composition among biomolecules. A new method using solid-state nanopores — tiny tunnels that a protein or other molecule can pass through — and machine learning suggests a way to help identify and classify proteins more accurately.