Polyoxometalates (POMs) have broad applicability and significant potential in electrocatalysis and photocatalysis. However, the practical application of pure POMs is significantly constrained by their decomposition in polar media (such as neutral and alkaline solutions). The modification of POMs with metal-calixarene clusters is beneficial for fabricating functional hybrid materials with the combined merits of the two components. Four new thiacalixarene-functionalized polyoxometalate clusters were synthesized by researchers at School of Petrochemical Engineering, Liaoning Petrochemical University, China. These four clusters were characterized by Keggin-type PM₄Mo₈ motifs, which confer redox properties similar to those of PMo₁₂O₄₀³⁻ (PMo₁₂) while providing superior structural stability and electrocatalytic reduction of IO₃⁻. The substitution of four metal ions in PMo₁₂, along with the capping TC4A ligand and VO unit, significantly modulated visible-light absorption, enhancing photothermal conversion in the solid state and organic solutions.

For thousands of years, humans have combined metals to collectively harness properties found in individual components, producing such practical materials as bronze, brass and, more recently, steel. However, predicting the exact microstructures underpinning these alloys to understand how specific properties of the constituent materials may manifest across scales is still a complex mystery researchers are working to solve. Now, thanks to a team based in Japan, that work could take minutes instead of years.

Analog repeaters dramatically enhance millimeter-wave (mmWave) coverage in mobile networks by overcoming signal blockage, report researchers from Science Tokyo. As demonstrated in a field experiment at Ookayama Campus, low-cost repeaters connected either wirelessly or via optical fiber offer a promising solution for 5G and 6G networks. Both configurations achieved over 1 Gbps throughput and enhanced mmWave signal stability, showing strong potential for practical deployment in urban and high-traffic areas.

Conventional handheld photoacoustic and ultrasound Imaging (PAUS), while offering flexibility, offers only a narrow view of the target region, providing limited information on its structure. Alternative methods require external sensors and bulky equipment, which can also make measurements inaccurate. In a new study, researchers developed a MoGLo-Net, a deep-learning model for 3D reconstruction of 2D PAUS images. This method does not require any external sensors and can make treatments more accessible, safer, and effective.