A recent study led by researchers from Tsinghua University and Southwest University of Science and Technology has introduced a new method to directly regenerate heavily degraded lithium cobalt oxide [LiCoO₂ (LCO)] cathodes from spent lithium-ion batteries. Using a ball milling process to convert the damaged crystalline structure into a uniform amorphous phase, the team rebuilt lithium replenishment pathways and restored electrochemical performance through high-temperature sintering. The regenerated cathodes delivered a discharge capacity of 179.10 mAh·g⁻¹—comparable to commercial materials. This approach not only sidesteps the environmental and energy drawbacks of conventional recycling but also presents a scalable and economically viable solution for the reuse of retired battery components.

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.

The rapid advancement of artificial intelligence (AI) has significantly increased the computational load on data centers, resulting in substantial carbon emissions. To mitigate these emissions, future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands. This study aims to investigate how much carbon emission reduction can be achieved by using a carbon-oriented demand response to guide the optimal planning and operation of data centers. An empirical study based on the proposed models is conducted on real-world data from China. The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province, Ningxia Hui Autonomous Region, Sichuan Province, Inner Mongolia Autonomous Region, and Qinghai Province, accounting for 57% of the total national increase in server capacity. 33% of the computational load from Eastern China should be transferred to the West, which could reduce the overall load carbon emissions by 26%.

Energy sector decarbonization is a key battleground in China’s march toward carbon neutrality, and understanding what it means and takes is crucial for its policy-making and sustainable implementation. Here we analyze the structural challenges and review the necessary supporting technologies and systems of energy sector decarbonization in China. This work could achieve a better understanding of technology development and deployment, and to make more thoughtful policy recommendations.

The CCT sensitivity calculation method proposed in this study provides a new tool for stability analysis in power systems with a high proportion of power electronic devices. By considering the effects of current limiting and control switching, as well as scenarios where stability boundaries are controlled by periodic orbits (POs), the method quantifies the impact of various parameters on system stability and offers guidance for adjusting inverter control parameters. Future research could further refine the CCT calculation method and integrate CCT sensitivity into optimization models to enhance system stability through improved inverter control strategies.