SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4432-9
Sodium vanadium phosphate (Na3V2(PO4)3, NVP) with NASICON structure is a promising cathode for sodium-ion batteries but suffers from low electronic conductivity and a high energy barrier for the V4+/V5+ redox couple, limiting practical energy density. A medium-entropy tuning strategy yields the multi-element substituted Na3.2V1.5Cr0.1Fe0.1Mn0.1Ni0.1Ti0.1(PO4)3 (ME-NVP). Entropy modulation tailors the microscopic electronic structure, enabling reversible V4+/V5+ redox at 4.0 V. Analyses reveal a synergistic diffusion mechanism that accelerates Na+ transport and enhances multiple-electron redox kinetics. Ex-situ X-ray diffraction confirms highly reversible structural evolution during cycling. The ME-NVP cathode delivers 116.8 mAh g-1 at 0.1C and retains 83.9% of initial capacity after 1000 cycles at 20C, with excellent performance from -12 to 50 °C. This work demonstrates that configurational entropy regulation unlocks high-energy polyanion cathodes for advanced sodium-ion batteries.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4234-3
The pursuit of high-energy-density sodium-ion batteries (SIBs) necessitates the development of stable high-capacity anodes. While amorphous carbon is a promising anode candidate for SIBs, its practical application is hindered by limited capacity. Herein, we design a composite anode by chemically confining a high-content (~25 wt%) short-chain sulfur into hierarchical porous hard carbon microspheres (SHHC) derived from microbe yeast. The SHHC anode exhibits a high reversible capacity of ~807 mAh g−1 at 0.03 A g−1 (~3 times that of conventional amorphous carbon) along with superior rate capability, and extraordinary long-term cyclability (almost 100% capacity retention after 2000 cycles at 1.0 A g−1). The high-content sulfur species contribute to superb redox reactivity for high-capacity sodium storage via a surface-dominated storage mechanism. The carbon matrix features an enlarged interlayer distance, which facilitates Na-ion intercalation and deintercalation for high-rate capability. Furthermore, the hierarchical porous structure with built-in cavities facilitates the Na-ion transfer and effectively accommodates the electrode’s volume expansion, achieving fast electrode kinetics and outstanding cyclability. Such a combination of favored properties leads to state-of-the-art comprehensive battery performance for Na-ion storage. Our finding envisions a new perspective on building stable high-capacity anode materials for SIBs.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3725-1
Conductive hydrogel-based stretchable electronics have been extensively investigated, with strain sensors being the most prominently studied. While mechanical properties significantly affect device performance, the systematic correlation between specific mechanical parameters and sensing performance remains rarely explored. This work compares the influences of Young’s modulus and mechanical hysteresis on sensing performance between highly entangled PAM-Li and double-network PAM-Li-Agar-3 strain sensors. Owing to the brittle agar network, which imparts a higher Young’s modulus and pronounced mechanical hysteresis to the double-network PAM-Li-Agar-3 hydrogel, the corresponding sensor requires a greater driving force for deformation and yields signals with poor reproducibility. In contrast, the PAM-Li hydrogel, characterized by highly entangled polymer chains, exhibits a lower Young’s modulus and negligible mechanical hysteresis. Consequently, signals from the PAM-Li strain sensor demonstrate enhanced sensitivity and stability. Therefore, this work demonstrates that a low Young’s modulus and minimal mechanical hysteresis are critical factors for achieving superior sensing performance in strain sensors, as systematically validated through comparative analyses across diverse application scenarios.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3958-8
Visual systems are the primary interface for humans to perceive the external environment. Mimicking the human eye, which integrates adjustable lenses with a curved retina, bio-inspired curved image sensors effectively mitigate field curvature and vignetting. To realize focus-tunable imaging, sensors must possess dynamic curvature while maintaining high sensitivity and mechanical stability. However, transitioning from rigid architectures to flexible devices often results in poor surface conformity through simple bending. Flexible sensors have explored intrinsic and structural designs for better flexibility and less stress concentration. Recent advances suggest that ultrathin devices with mesh-inspired designs offer a superior strategy, achieving seamless alignment with the curved surface without compromising optoelectronic performance. He et al. have developed a focus-tunable real-time curved imaging system inspired by the human visual system, based on an ultrathin perovskite curved image sensor with a hierarchical mesh architecture. They introduced an ultrathin image sensor with 5.4 μm thickness and soft interconnections, enabling it to be readily deformed into a hemispherical geometry. The ultrathin structure significantly reduces intrinsic mechanical behaviors, while interconnections effectively release twisting and stretching stress among pixels under various curvature conditions. As a result, the curved sensor array achieves a low detection limit of 10 nW cm−2, approaching the light sensitivity level of human photoreceptors. The focus-tunable imaging system integrates a curved image sensor with a shape-tunable convex lens, forming a conformal, skin-like architecture on a hemispherical surface. Finite element analysis revealed that when deformed to a curvature radius of 17.8 mm, the maximum strain on the Parylene C substrate reaches 5.72% and is primarily localized at pixel interconnections and edge regions. The curved image sensor achieves an overall thickness of approximately 5.4 μm and integrates a perovskite photodetector array comprising 127 pixels, enabling mechanically robust operation under pronounced curvature.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3911-7
The seamless integration of electronics with the human body is pivotal for next-generation human-machine interfaces (HMI) and personalized healthcare. Traditional high-density sensor arrays, while capable of capturing complex biomechanical data, impose significant power and comfort penalties. This study introduces the Generative EMG Network (GenENet), a framework that synergizes generative artificial intelligence with soft bioelectronics to reduce hardware complexity. By leveraging a 32-channel stretchable sensor array as a 'teacher' dataset, GenENet employs a masked autoencoder architecture to learn spatiotemporal correlations within high-density electromyography (EMG) data. The trained model enables a simplified 6-channel wearable band to replicate the performance of the full 32-channel array. The sensor device utilizes a polydimethylsiloxane (PDMS) substrate, liquid metal (EGaln) interconnects, and a conductive PEDOT:PSS hydrogel interface, achieving low skin-contact impedance and high signal-to-noise ratios under mechanical strain. This approach addresses the bottleneck of data throughput and power consumption in wearable HMIs, offering a path toward minimalist, personalized devices for applications such as sign language decoding and gait analysis. The findings underscore the potential of generative AI to transform wearable bioelectronics by shifting computational burden from hardware to software.