Researchers from Harwell Science and Innovation Campus explore methanol dynamics in copper-loaded zeolites using neutron scattering, revealing insights into diffusion behavior and its impact on catalytic processes.

The electromagnetic responses of metamaterial microstructural units are typically described using classical polarization theory models from dielectric physics, such as the Lorentz and Drude models. However, there has been a notable absence of the Debye model, which holds significant importance in dielectric physics. Chinese scientists have now successfully uncovered a novel broadband electromagnetic response mechanism in metamaterial microstructures based on polarization theory - Debye relaxation.

The core of multispectral compatible camouflage technology lies in multispectral modulation, which requires the design of spectral characteristics based on the operational bands and principles of detectors. The intrinsic properties of natural materials depend on the inherent attributes of their microscopic components (e.g., molecules, atoms) and their arrangement, limiting their ability to modulate electromagnetic fields and making arbitrary tuning according to practical needs difficult. As a two-dimensional form of metamaterials, metasurfaces overcome the optical limitations of natural materials by designing artificial unit structures at a microscopic scale with periodicities between microscopic particles and operational wavelengths, enabling the modulation of macroscopic physical properties. Leveraging the metasurface's ability to flexibly control the amplitude and phase of electromagnetic waves, subwavelength structures can be designed on demand across different bands, providing critical support for the development of multispectral-compatible camouflage technologies. Ideal photoelectric camouflage materials must maintain compatibility with various detectors across the full spectrum while ensuring structural simplicity and ultrathin characteristics to reduce fabrication complexity and enhance practicality. However, a challenging contradiction exists between multifunctional multispectral integration and structural simplicity: whether in one-dimensional photonic crystals or metasurfaces, increasing the number of tunable bands often relies on multilayer stacking or complex configurations, significantly escalating design and fabrication difficulties.

Magnetic soft robots, providing excellent flexibility and precise control, are transforming fields from surgery to environmental exploration. Focusing on the relationship between structural configurations and locomotion modes of magnetic soft robots, a new review article in FlexTech systematically summarizes the material composition, fabrication methods, locomotion modes, and applications of existing magnetic soft robots. Furthermore, this article also analyzes and discusses the current challenges and future development directions of structured magnetic soft robots.

A research team led by Professor Xiaonan Wang from Tsinghua University has published a comprehensive review on AI-enhanced multi-scale smart systems for decarbonizing the chemical industry. The study, featured in Technology Review for Carbon Neutrality, explores innovations from micro-level materials discovery to macro-level industrial park optimization, highlighting how intelligent approaches enhance efficiency, sustainability, and carbon neutrality. It also examines cross-scale modeling for complex chemical processes and identifies key challenges such as data management and industrial integration. The review concludes with future research directions, advocating interdisciplinary strategies to drive the industry toward a greener and more efficient future.

A study from East China Normal University explores how Large Language Models (LLMs) can revolutionize education by automating teaching tasks. It highlights two key applications: generating customized materials and streamlining assessment. While LLMs reduce educators’ workload, human oversight remains crucial. The research suggests a collaborative model where teachers act as orchestrators and LLMs serve as assistants, ensuring AI integration enhances personalized education while maintaining instructional quality and adaptability.