Solid-state lithium (Li) batteries offer high-energy density and operational safety but face sluggish Li⁺ transport in polymer/ceramic composite solid-state electrolytes. Herein, we propose a bioinspired polyphenol-gated interfacial engineering that mimics ion-selective protein channels to enhance Li⁺-selective transport across the polymer–ceramic interface. Polyphenols such as polydopamine, poly-tannic acid, and poly-gallic acid chemically couple La_0.56Li_0.33TiO₃ ceramic nanofibers and glycidyl polyether matrix. Within this interface, carbonyl groups selectively coordinate Li⁺ and facilitate directional migration. On the other hand, hydroxyl and amino groups immobilize anions via hydrogen bonding. This chemical gating nearly doubles interfacial Li⁺ concentration and boosts transference number to 0.68. The corresponding Li||LiFePO₄ battery exhibits stable cycling over 600 cycles with 85.5% capacity retention at 1 C, while the pouch cell delivers reliable operation under mechanical stress caused by bending and puncturing. This work demonstrates that polyphenol-gated interfaces are essential for promoting selective and efficient cross-phase Li⁺ transport for high-performance solid-state lithium-metal batteries.

A task-driven AI-native architecture for 6G is unveiled in  Engineering study, addressing key challenges in fusing AI with next-gen mobile networks. The design balances diverse AI service needs, network reliability and resource efficiency, with validated feasibility and clear 6G standardization guidance for seamless AI-communication integration.

A research paper by scientists from Tongji University developed a groundbreaking mechanotransduction-aware therapeutic platform that enhances the efficacy of mesenchymal stem cell (MSC) therapy for acute lung injury (ALI).

The new research paper, published on Mar. 24 in the journal Cyborg and Bionic Systems, proposed a mechanotransduction-aware strategy for enhancing MSC potency via 3D culture and localized delivery.

Curious how 6G will power unmanned operations in remote spots like mining sites or disaster zones? A new Engineering study designs 6G space-air-ground integrated networks around the SC³ loop, a reflex arc-like structure, ditching traditional link-focused 5G designs. It tests a task-oriented framework that cuts control costs and tackles key 6G network challenges for autonomous systems.

Researchers at the University of Tokyo clarified how membrane lipid composition determines the surface charge of extracellular vesicles (EVs). They show that differences between exosomes and membrane-derived EVs arise from phospholipid asymmetry, particularly phosphatidylserine distribution. The study proposes zeta potential as a key indicator for EV classification and quality control, offering a foundation for standardization and rational design of EV-based therapeutics. This work was conducted as part of a JST COI-NEXT program, led by Innovation Center of NanoMedicine (iCONM).

A research team has developed a Gaussian Splatting processing platform that supports end-to-end processing from data acquisition to multi-platform rendering. Their framework provides a solid foundation for the large-scale adoption and future research of Gaussian Splatting technology.

Florida’s coral reefs are under siege from fast-spreading diseases like Stony Coral Tissue Loss Disease, yet their hidden structural impacts remain poorly understood. FAU researchers used advanced micro-CT imaging and deep learning to analyze coral skeletons in 3D, revealing subtle changes in porosity, density and thickness with 98% accuracy. This innovative approach offers a powerful new tool to rapidly assess reef health and better guide conservation strategies in the face of escalating environmental threats.

Researchers at EPFL have developed a deep-learning framework that dramatically improves vehicle re-identification in large-scale drone traffic monitoring. By combining visual features with traffic-based travel-time predictions grounded in shockwave theory, the system can reliably match vehicles that appear nearly identical from a bird’s-eye view. Tested on one of the largest multi-UAV traffic datasets, the approach boosts ReID accuracy by 36.8 percent, enabling more robust urban vehicle tracking.