A research team led by Profs. CHEN Xingqiu and SUN Yan from the Institute of Metal Research (IMR) of the Chinese Academy of Sciences, in collaboration with Prof. NIU Haiyang from Northwestern Polytechnical University, has uncovered a previously unknown phase transformation mechanism in monolayer molybdenum telluride (MoTe₂).

Masahiro Tatsumisago, Former OMU President and current Executive Advisor at Osaka Metropolitan University, has been selected for the NIMS Award 2026 from the National Institute for Materials Science (NIMS).

A new study published in the Journal of Bioresources and Bioproducts presents an environmentally friendly strategy for producing high-quality dissolving pulp from sugarcane bagasse. By integrating carbonate-based oxygen-alkali pulping with an enzymatic totally chlorine-free bleaching sequence, the researchers achieved selective lignin removal while preserving cellulose, significantly reducing chemical consumption and bleaching demand. The work also clarifies the underlying reaction mechanisms governing active oxygen species and enzyme-assisted bleaching, providing a promising route for converting agricultural residues into high-value cellulose products with improved environmental performance.

A research team led by Prof. Zhen Gao at Southern University of Science and Technology has achieved the first experimental observation of the three-dimensional (3D) photonic quantum Hall effect of Fermi arcs and its hallmark feature, the one-sided chiral hinge states, in a 3D inhomogeneous magnetic Weyl photonic crystal.

A research team at Peking University proposed an optoelectronic distributed computing system based on an on-chip all-optical supernode. By integrating high-speed silicon photonic transceiver chips, low-loss optical circuit switching (OCS) chips and multiple computing nodes, the system enables intermediate data between different layers of a deep neural network to be directly transmitted and switched through an on-chip high-speed all-optical supernode. This approach reduces latency overheads associated with conventional data movement, packet scheduling and buffering.

As artificial intelligence expands from cloud computing into robots, autonomous vehicles and other real-world systems, conventional electronic architectures face increasing challenges in latency, bandwidth and energy consumption. Researchers from the University of Shanghai for Science and Technology propose Brain-like Optical Computing (BOC), a new photonic framework integrating sensing, memory and computation for future edge and embodied intelligence.

Professor Minsoo Rhu’s research team at the KAIST School of Electrical Engineering presents the first quantitative analysis of AI agents’ computational cost, response latency, energy consumption, and data-center-scale power demand

AI agents consume up to 136.5 times more energy per query than conventional generative AI, highlighting the need for co-design of data centers and power infrastructure for a sustainable AI era