A novel ‘zeolite blending’ method has successfully produced CON-type zeolites with unprecedentedly high aluminum content, report researchers from Institute of Science Tokyo. By combining multiple zeolite precursors to guide the synthesis process, this innovative strategy overcomes long-standing limitations in controlling aluminum content in zeolite frameworks. The proposed approach will open new possibilities for catalyst development across various industrial applications, including petrochemical processing, fine chemicals production, and environmental remediation.

Seoul National University College of Engineering announced that Professor Yousung Jung’s research team in the Department of Chemical and Biological Engineering has successfully developed a technology that utilizes Large Language Models (LLMs) to predict the synthesizability of novel materials and interpret the basis for such predictions. This study was conducted in collaboration with Fordham University in the United States. The findings of this research are expected to contribute to the novel material design process by filtering out material candidates with low synthesizability in advance or optimizing previously challenging-to-synthesize materials into more feasible forms. The study, with Postdoctoral Researcher Seongmin Kim as the first author, was published in two renowned international chemistry journals: the Journal of the American Chemical Society (JACS) on July 11, 2024, and Angewandte Chemie International Edition on February 13, 2025.

 Research team proposed DeepSwarm, a collective deep learning framework integrating swarm intelligence for bidirectional optimization of data acquisition and processing. It improves accuracy and resource efficiency in IoT scenarios, showing significant results in video analysis and federated learning.

Osaka Metropolitan University researchers have discovered the mechanism that drives the parasitic vine Cuscuta campestris to insert organs into plants after making contact with the hosts.

Transition metal valence state, a key property steering heterogeneous catalysis, has remained underexplored for its role in tuning S cathode kinetics in Li–S batteries. This study reported that CuO and Cu₂O particulate catalysts exhibited distinct interactions with sulfur species, dictating the kinetics of sulfur reduction reactions. While Cu²⁺ enables direct (poly)thiosulfate redox, both Cu²⁺ and Cu⁺ indirectly modulate lithium (poly)sulfide chemisorption by altering O^2- reactivity and Li bond strength. CuO with stronger interactions enabled faster sulfur reduction reaction kinetics, achieving enhanced Li–S battery performance. This work expands the scope of metal-valence-state effects in catalysis and offers an unconventional strategy to optimize Li–S battery performance via indirect Li-bond regulation.

Professor Ngai Wong and Dr Zhengwu Liu from the Department of Electrical and Electronic Engineering at the Faculty of Engineering at the University of Hong Kong (HKU), in collaboration with research teams at Tsinghua University and Tianjin University, have conducted groundbreaking research on memristor-based brain-computer interfaces (BCIs). Published in Nature Electronics, this research presents an innovative approach for implementing energy-efficient adaptive neuromorphic decoders in BCIs that can effectively co-evolve with changing brain signals.