X-ray absorption spectroscopy (XAS) provides valuable information about a material’s properties and electronic states. However, it requires extensive expertise and manual effort for conventional analysis. Now, researchers from Japan have developed a novel artificial intelligence-based approach for analyzing XAS data that can enable rapid, autonomous, and object material identification. This novel approach outperforms the previous studies in terms of higher accuracy, accelerating the development of new materials.

Crystalline-amorphous composites comprise crystalline grains separated by amorphous boundaries. The combined role of grain size (D) and amorphous boundary thickness (l) on material properties has not been explored. Now, writing in National Science Review, a team from the Hong Kong University of Science and Technology reports simulation results of mechanical properties across the (D, l) parameter space. They identify optimal (D, l) values that provide maximum strength while also enhancing ductility, successfully circumventing the classic strength-ductility tradeoff.

Scientists have designed a gradient sodium-tin alloy/sodium bilayer anode that solves the two problems of dendrite growth and sodium loss in sodium batteries. This innovative structure features an upper "ion-buffering" layer that guides sodium ions for dendrite-free deposition and a bottom reservoir that dynamically compensates for lost sodium. The resulting batteries achieve an unprecedented energy density and ultralong cyclability in lab tests, paving the way for more powerful and durable energy storage.

Nanyang Technological University, Singapore (NTU Singapore) and Zero Gravity (0G), a decentralised AI infrastructure company, have announced a S$5 million partnership to establish a joint research hub advancing blockchain-based artificial intelligence (AI) technologies that will be more accessible and accountable. This marks 0G’s first university collaboration globally and will fund multiple projects exploring decentralised AI training, blockchain-integrated model alignment, and proof-of-useful-work consensus mechanisms. 

Avram Miller, a world-renowned scientist and innovator, has been appointed by the Italian Institute of Technology (IIT) as the Institute’s first IIT Fellow. With this recognition, Avram Miller inaugurates a new IIT initiative to honor outstanding leaders in science and innovation, in line with prestigious academic and industrial traditions worldwide.

Human-machine intelligent interaction (HMII) technology, which is an advanced iteration of human-machine interaction technology, has garnered widespread attention owing to its significant achievements in healthcare and virtual reality research. Herein, this work reports a self-powered, transistor-like iontronics pressure sensor based on an MXene/Bi 2D heterojunction for advanced human-machine intelligent interaction. The free-standing device uses MXene@Zn and MXene@Bi interdigitated electrodes, a PVDF-HFP-GO solid electrolyte and a CNF isolation layer to mimic a p-type MOSFET, where pressure “gates” ion transport and generates encodable voltage outputs. The sensor exhibits 1.1 V open-circuit voltage, millisecond-level response (66.59 ms), excellent linearity (99.5%) and durability over 50,000 cycles, enabling self-powered monitoring of physiological signals and robotic motions, wireless transmission, and deep-learning-assisted gesture recognition with 95.83% accuracy in a single-device HMII system.