The development of highly efficient and durable bifunctional catalysts with minimal precious metal usage is critical for advancing proton exchange membrane water electrolysis (PEMWE). We present an iridium–platinum nanoalloy (IrPt) supported on lanthanum and nickel co-doped cobalt oxide, featuring a core–shell architecture with an amorphous IrPtO_x shell and an IrPt core. This catalyst exhibits exceptional bifunctional activity for oxygen and hydrogen evolution reactions in acidic media, achieving 2 A cm⁻² at 1.72 V in a PEMWE device with ultralow loadings of 0.075 mg_Ir cm⁻² and 0.075 mg_Pt cm⁻² at anode and cathode, respectively. It demonstrates outstanding durability, sustaining water splitting for over 646 h with a degradation rate of only 5 μV h⁻¹, outperforming state-of-the-art Ir-based catalysts. In situ X-ray absorption spectroscopy and density functional theory simulations reveal that the optimized charge redistribution between Ir and Pt, along with the IrPt core–IrPtO_x shell structure, enhances performance. The Ir–O–Pt active sites enable a bi-nuclear mechanism for oxygen evolution reaction and a Volmer–Tafel mechanism for hydrogen evolution reaction, reducing kinetic barriers. Hierarchical porosity, abundant oxygen vacancies, and a high electrochemical surface area further improve electron and mass transfer. This work offers a cost-effective solution for green hydrogen production and advances the design of high-performance bifunctional catalysts for PEMWE.

This paper proposes a deep learning framework F-GCN that integrates multiple wavelet bases, and extracts MI brain electrical features based on the functional topological relationships between electrodes. The average accuracy of the fused features reaches 92.44%, which is significantly higher than that of a single wavelet basis (coif4: 67.67%, db4: 82.93%, sym4: 73.10%). It also demonstrates good stability and individual convergence in the leave-one-out verification, proving the effectiveness of the method.

With the advancement of detection technologies and the increasing complexity of camouflage scenarios, dynamically tunable devices capable of responding to multispectral detection demonstrate tremendous potential for camouflage applications. Towards this goal, Scientist in China proposed a VO₂-based multispectral dynamic modulator, leveraging the material's broad-spectrum tunability and flexible performance adjustability to achieve dynamic spectral modulation in visible and mid-infrared bands. The technique will provide a new technological pathway for the realization of multispectral dynamic camouflage technologies.

The rapid advancement of artificial intelligence (AI) presents significant opportunities for both societies and industries. At the same time, however, it raises growing concerns about the increasing frequency and sophistication of cyberattacks. These cyber risks can lead not only to substantial direct financial losses for firms, but also to indirect losses stemming from reputational damage and cascading effects within interconnected systems. To enhance resilience in the face of such events, it is essential for scholars across disciplines to engage in rigorous analysis and interdisciplinary dialogue on the assessment and management of cyber risks.

With global energy demand climbing and climate challenges intensifying, researchers are exploring transformative new ways to make chemical manufacturing sustainable. In a newly published review, an international team led by Dr. Yong Jiang and colleagues from Fujian Agriculture and Forestry University, Technical University of Denmark, and Tsinghua University highlight “biohybrid” synthesis systems—an innovative technology integrating living cells with advanced materials—to unlock clean production of chemicals for a greener future.