Tianjin Normal University (Prof. Cheng-Peng Li) and Southeast Normal University (Prof. Yan-Qian Lan) have developed crystalline porous framework (CPF) composite beads to trap ⁹⁹TcO₄^– in nuclear wastewater. 1 g of beads processed 4.8 L of pre-treated simulated waste, with residual Tc levels reduced to 0.026 ppb—significantly below the WHO (0.159 ppb) and U.S. EPA (0.053 ppb) drinking water standards (calculated from nonradioactive surrogate ReO₄^–). This scalable strategy enables deep purification of trace radionuclide, enabling industrial deployment of nanoscale adsorbent technologies.

Found in knee replacements and bone plates, aircraft components, and catalytic converters, the exceptionally strong metals known as multiple principal element alloys (MPEA) are about to get even stronger through to artificial intelligence.

Sanket Deshmukh, associate professor in chemical engineering, and his team have designed a new MPEA with superior mechanical properties using a data-driven framework that leverages the supercomputing power of explainable artificial intelligence (AI). Their findings, supported by funding from the National Science Foundation, were recently published in Nature’s npj Computational Materials.

An international study led by Ca’ Foscari researchers has been published in the journal Geoscientific Model Development and is expected to become a reference tool for those studying future glacier melt scenarios.

A research team consisting of Kazumasa Uehara, Associate Professor in the Department of Computer Science and Engineering at Toyohashi University of Technology, and Yuya Fukuda, a pre-doctoral candidate in the same department, demonstrated that scalp electroencephalogram (EEG) power modulation of 4–8 Hz theta oscillation, known as frontal midline theta (FMT), observed in the medial frontal cortex just before initiating a movement is likely a key neural indicator explaining individual differences in the speed of motor skill acquisition. Analysis of scalp EEG data during a motor learning task integrating vision and motor action revealed that subjects who learned more quickly exhibited higher FMT power just before movement onset. These findings would contribute to the future development of personalized learning support and training methods based on EEG. Such methods could be applied in physical education fields such as rehabilitation and sports training, which require motor learning, as well as in enhancing musical instrument performance skills. The results of this research were published online in Experimental Brain Research on May 15, 2025.