The new resource gives the global scientific community free access to advanced battery imaging data, helping to support further research into battery materials, performance and the development of next-generation technologies, while providing valuable datasets for the development of AI and new approaches to image analysis. 

Researchers have developed a multi‑step crosslinking strategy to construct UiO‑66‑AC@PEG by covalently bridging maleic anhydride‑prefunctionalized UiO‑66‑NH₂ with polyethylene glycol, enabling simultaneous interfacial engineering and pore regulation. The resulting crosslinked architecture incorporates abundant amide and ether oxygen moieties, generating CO₂‑philic domains that enhance CO₂ sorption through dipole‑quadrupole interactions. Meanwhile, crosslinking induces pore constriction within UiO‑66‑NH₂, strengthening size‑sieving effects toward CO₂/N₂ separation. The optimized UiO‑66‑AC@PEG/Pebax membrane achieved a CO₂ permeability of 125.8 Barrer and a CO₂/N₂ selectivity of 81.1, corresponding to increases of 56.6 % and 33.5 %, respectively, compared with the UiO‑66‑NH₂/Pebax MMM.

 Researchers have systematically reviewed the application of electromagnetic induction heating in energy chemical engineering, focusing on three susceptor categories: carbon‑based materials, macroscopic metals, and magnetic nanoparticles. Carbon‑based susceptors enable reactant‑heat source integration for high‑temperature endothermic reactions such as calcium carbide synthesis, achieving solid‑phase synthesis with reaction temperatures reduced by 400‑500 °C compared to the electric arc process. Macroscopic metal susceptors combine heat generation and catalytic functions, with iron‑based induction heating reducing ammonia decomposition activation energy by more than half and maintaining high conversion at ultra‑high space velocities. Magnetic nanoparticles generate heat precisely at catalytic sites, enabling nanoscale hot spots under mild macroscopic temperatures for reactions such as reverse water‑gas shift and dry reforming.

As brain-computer interfaces become increasingly precise in reading human emotions, a practical question arises: Will a model trained today still be able to recognize the emotions of the same person tomorrow, in a few weeks, or even months later? Recently, the team led by Shuang Liu from the Medical School of Tianjin University/Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration published a research paper titled "Toward Sustainable Cross-Time Affective Brain–Computer Interfaces: Explicit Modeling and Structural Calibration of Temporal Variability" in the National Science Review (NSR). This study offers a cross-time "EEG ruler" for affective brain-computer interfaces (aBCIs). To address temporal variability in cross-time emotion recognition, the researchers propose a Multi-level Dynamic Integrated Perception Network (MDIN), which explicitly models and structurally calibrates temporal variability on three levels: emotional neural representation, dynamic functional connectivity, and cross-time discriminative structures, offering a new technical pathway for long-term stable affective brain-computer interfaces. This work may further accelerate the transition of aBCIs from short-term experimental validation to long-term, adaptive use in real-world mental health monitoring and human-machine interaction.

High‑throughput workflows have revolutionized experimental heterogeneous catalysis by enabling rapid, parallel synthesis and screening of catalyst libraries. This review traces the evolution of high‑throughput technology from manual experiments in the early 1900s to today‘s autonomous, artificial intelligence‑driven platforms that integrate robotics, machine learning, and self‑driving laboratories. The state‑of‑the‑art mode encompasses five key steps: experiment design, catalyst synthesis, catalyst testing and screening, catalyst characterizations, and data handling and analysis. Despite its transformative potential, high‑throughput workflows face critical pitfalls—including gaps between microscale reactor results and industrial performance, pressure limitations, and chemical information disparity in funnel‑shaped screening—that must be addressed to fully realize its promise for rational catalyst discovery.