Printing 3D Metallic Structures in Porous Matrix.

The fabrication of metallic micro/nanostructures has great potential for advancing optoelectronic microdevices. Over the past decade, femtosecond laser direct writing (FsLDW) technology has played a crucial role in driving progress in this field. In this study, silica gel glass is used as a supporting medium, and FsLDW is employed to reduce gold and palladium ions using 7-Diethylamino-3-thenoylcoumarin (DETC) as a two-photon sensitizer, enabling the printing of conductive multilayered and 3D metallic structures. How the pore size of the silica gel glass affects the electrical conductivity of printed metal wires is systematically examined. This 3D printing method is versatile and offers expanded opportunities for applying metallic micro/nanostructures in optoelectronic devices.

Automation and machine learning augmented by large language models in a catalysis study.

Recent advancements in artificial intelligence and automation are transforming catalyst discovery and design from traditional trial-and-error manual mode into intelligent, high-throughput digital methodologies. This transformation is driven by four key components, including high-throughput information extraction, automated robotic experimentation, real-time feedback for iterative optimization, and interpretable machine learning for generating new knowledge. These innovations have given rise to the development of self-driving labs and significantly accelerated materials research. Over the past two years, the emergence of large language models (LLMs) has added a new dimension to this field, providing unprecedented flexibility in information integration, decision-making, and interacting with human researchers. This review explores how LLMs are reshaping catalyst design, heralding a revolutionary change in the fields.