Lithium cobalt oxide (LiCoO₂, LCO) stands as one of the most representative cathode materials in lithium-ion batteries. The improvement in energy density of LiCoO₂ is essentially achieved by increasing the charge cut-off voltage. However, under high-voltage conditions, the electrolyte solvent molecules undergo severe oxidative decomposition at the cathode interface. Moreover, a series of issues including transition metal dissolution, oxygen release, and deteriorated thermal stability, have become bottlenecks that severely restrict its practical application.

To address this challenge, Prof. Yang Sun (Sun Yat-sen University), Associate Researcher Maolin Yang (Xiamen University), Professors Qiaobao Zhang (Xiamen University), Yu Qiao (Xiamen University), and Academician Shi-gang Sun (Xiamen University) have proposed a novel strategy to stabilize high-voltage LiCoO₂ cathodes by coating them with a conformal zeolite layer using a scalable high-speed solid-phase mechanofusion technique. The sub-nano channels of zeolite enable complete Li⁺ desolvation, creating a locally supersaturated electrolyte that suppresses solvent decomposition. The Zeolite@LCO cathode achieves ~83% capacity retention after 300 cycles at 4.8 V and stable operation at 5.0 V, with significantly enhanced thermal safety. This work provides a translatable paradigm for high-energy-density lithium-ion batteries. This work was recently published online in Science Bulletin.

A new review traces more than 70 years of microecology research in China, from early ideas about microbial balance to today’s work in precision medicine, sustainable agriculture and environmental restoration. The authors argue that combining artificial intelligence, synthetic biology and international collaboration could help turn microbiome science into practical solutions for human, animal and environmental health.

Researchers developed physics-embedded inverse learning (PEIL), which uses a fixed, differentiable physical solver to turn signal-recovery error into supervision for parameter estimation without parameter labels. In high-mobility wireless tests, PEIL generalized to unseen channel profiles and speeds without retraining and matched a parameter-supervised baseline using one twentieth as many training samples. PEIL was also used to estimate coil sensitivity maps for parallel MRI.

The novel physics-constrained AI framework PHICS realizes a full closed loop spanning multiscale inverse design and experimental validation. Leveraging interface, phase, and carrier engineering, PHICS breaks long-standing material trade-offs to yield composite phase-change materials that achieve simultaneous enhancements in thermal conductivity and electrical insulation while maintaining high thermal storage density, paving new pathways for designing advanced electronic thermal management materials.

Embodied AI can act in the physical world, but the purpose guiding its actions often remains buried in prompts, rewards or predefined goals. In a Review published in National Science Review, Nanning Zheng proposes Intent-Driven Embodied Artificial Intelligence (IDEAI), a four-layer framework built around explicit, revisable and verifiable intent. Seven technical pillars map how that intent could shape knowledge, planning, safety and value alignment in real-world systems.