<jats:title>Abstract</jats:title>
<jats:p>
Aqueous zinc‐ion batteries (AZIBs) offer intrinsic safety and low cost, positioning them as promising candidates for large‐scale energy storage. However, uncontrolled Zn dendrite growth and parasitic hydrogen evolution reactions (HER) at the anode impede practical deployment. To address this, a dual‐interfacial layer (BN@Sn@Zn) combining zincophilic Sn and zincophobic boron nitride (BN) is engineered. This architecture regulates initial Zn
<jats:sup>2</jats:sup>
⁺ nucleation and subsequent deposition, suppressing dendrite formation while exploiting BN's high hydrogen adsorption energy to inhibit HER. The low ionic diffusion barrier of this bilayer synergistically enhances kinetics and enables rapid Zn
<jats:sup>2</jats:sup>
⁺ transport, facilitating homogeneous deposition. Consequently, the modified anode achieves a prolonged cycling lifespan of 1050 h at 6 mA cm
<jats:sup>−2</jats:sup>
, 6 mAh cm
<jats:sup>−2</jats:sup>
. In full‐cell configuration with MnO
<jats:sub>2</jats:sub>
cathode, it retains 75 mAh g
<jats:sup>−1</jats:sup>
after 2000 cycles, demonstrating superior stability versus bare Zn counterparts. This work innovates dual‐functional interfaces to optimize Zn deposition dynamics, advancing high‐performance AZIBs for sustainable energy storage systems.
</jats:p>

Mechanical metamaterials with customized microstructures are increasingly shaping robotic design and functionality, enabling the integration of sensing, actuation, control, and computation within the robot body. This Review outlines how metamaterial design principles—mechanics-inspired architectures, shape-reconfigurable structures, and material-driven functionality—enhance adaptability and distributed intelligence in robotics. We also discuss how artificial intelligence supports metamaterial robotics in design, modeling, and control, advancing systems with complex sensory feedback, learning capability, and adaptive physical interactions. This Review aims to inspire the community to explore the transformative potential of metamaterial robotics, fostering innovations that bridge the gap between materials engineering and intelligent robotics.

<jats:title>Abstract</jats:title><jats:p>The desolvation behavior of Li<jats:sup>+</jats:sup> is widely regarded as primarily governed by the electrolyte composition, while the dynamic decoupling mechanism between the electrode interface and solvation structure has received limited attention. Herein, a competitive adsorption‐driven, fast‐ion‐conducting host was innovatively developed based on the Li<jats:sub>15</jats:sub>Si<jats:sub>4</jats:sub>/Li<jats:sub>3</jats:sub>N microrods. Guided by density functional theory (DFT) calculations and molecular dynamics (MD) simulations, it was revealed for the first time that the dual‐phase interface exhibits competitive adsorption interactions with solvent molecules and anions within the solvation sheath. This unique interfacial interaction triggers dynamic reconstruction of the solvation structure at the interface, significantly reducing the Li<jats:sup>+</jats:sup> desolvation barrier by 33%. Benefiting from the integration of these multifunctional advantages, the symmetric cell delivers an ultra‐long cycle lifespan of over 5000 h at 1 mA cm<jats:sup>−2</jats:sup> with an exceptionally low overpotential of 13 mV. Moreover, the full cell coupled with LiFePO<jats:sub>4</jats:sub> cathodes achieves an impressive cycling stability of 2000 cycles at 5 C, with a capacity retention of 107%. This work unveils a substrate‐mediated solvation structure decoupling mechanism, offering a new paradigm for the rational design of dendrite‐free Li metal anodes with 3D architectures.</jats:p>

Abstract

Optimizing ion transport dynamics in nanoporous electrodes is crucial for advancing electrochemical energy storage and conversion technologies. However, rapid charge relaxation and architectural complexity in conventional electrodes impede a comprehensive understanding of ionic behavior. Here, convoluted ion migration routes are decoupled into two distinct pathways by precisely engineering the density and spatial arrangement of 3D carbon‐interconnected nanoporous architectures. The findings reveal that ions exhibit time‐optimized transport, prioritizing pathways that minimize temporal resistance over shorter spatial distances. This behavior, enabled by rational electrode design, enhances the performance of quasi‐ideal (low‐curvature) electrodes by 20% at ultrahigh scan rates of 1 0000 mV s⁻¹. Through finite element simulations and experimental validation, it is further demonstrated that uniformly distributed nanoporous configurations outperform localized and gradient designs in charging dynamics. These insights provide a framework for designing high‐efficiency nanoporous electrodes, with significant implications for next‐generation electrochemical devices.

Abstract

The inverse design of metamaterials is critical for advancing their practical applications. Although deep learning has transformed this process, challenges remain, particularly with insufficient data and less realistic, diverse generation for 3D metamaterials represented as voxels. To address these limitations, a data augmentation technique is developed based on topological perturbation and introduced a 3D conditional diffusion model (3D‐CDM) to optimize 3D metamaterial generation. This original dataset, comprising 200 voxel representations of lattices and triply periodic minimal surfaces, is labeled with effective physical properties computed using homogenization methods. This dataset is expanded to 5000 entries using the proposed data augmentation technique. Training the 3D‐CDM with the augmented dataset significantly improved the quality and accuracy of generated designs. The model successfully produces realistic 3D metamaterials with targeted properties, including volume fraction, Young's modulus, and thermal conductivity, outperforming existing voxel‐based generative models in terms of fidelity and diversity. The 3D‐CDM can be further optimized and extended for the inverse design of a broader range of material microstructures.

ABSTRACT

Hydrogen is a highly promising energy carrier because of its renewable and clean qualities. Among the different methods for H₂ production, photoelectrocatalysis (PEC) water splitting has garnered significant interest, thanks to the abundant and perennial solar energy. Single‐atom catalysts (SACs), which feature well‐distributed atoms anchored on supports, have gained great attention in PEC water splitting for their unique advantages in overcoming the limitations of conventional PEC reactions. Herein, we comprehensively review SAC‐incorporated photoelectrocatalysts for efficient PEC water splitting. We begin by highlighting the benefits of SACs in improving charge transfer, catalytic selectivity, and catalytic activity, which address the limitations of conventional PEC reactions. Next, we provide a comprehensive overview of established synthetic techniques for optimizing the properties of SACs, along with modern characterization methods to confirm their unique structures. Finally, we discuss the challenges and future directions in basic research and advancements, providing insights and guidance for this developing field.

Abstract

Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modeling methods of material microstructures, requiring experienced designers with expertise and several rounds of trial and error. Although recent advances in deep generative networks have enabled the inverse design of material microstructures, most studies involve property‐conditional generation and focus on a specific type of structure, resulting in limited generation diversity and poor human–computer interaction. In this study, a pioneering text‐to‐microstructure deep generative network (Txt2Microstruct‐Net) is proposed that enables the generation of 3D material microstructures directly from text prompts without additional optimization procedures. The Txt2Microstruct‐Net model is trained on a large microstructure‐caption paired dataset that is extensible using the algorithms provided. Moreover, the model is sufficiently flexible to generate different geometric representations, such as voxels and point clouds. The model's performance is also demonstrated in the inverse design of material microstructures and metamaterials. It has promising potential for interactive microstructure design when associated with large language models and could be a user‐friendly tool for material design and discovery.

Abstract

Sodium metal has become one of the most promising anodes for next‐generation cheap and high‐energy‐density metal batteries; however, challenges caused by the uncontrollable sodium dendrites growth and fragile solid electrolyte interphase (SEI) restrict their large‐scale practical applications in low‐cost and wide‐voltage‐window carbonate electrolytes. Herein, a novel multifunctional separator with lightweight and high thinness is proposed, assembled by the cobalt‐based metal‐organic framework nanowires (Co‐NWS), to replace the widely applied thick and heavy glass fiber separator. Benefitting from its abundant sodiophilic functional groups and densely stacked nanowires, Co‐NWS not only exhibits outstanding electrolyte wettability and effectively induces uniform Na⁺ ions flux as a strong ion‐redistributor but also favors constructing the robust N,F‐rich SEI layer. Satisfactorily, with 10 μL carbonate electrolyte, a Na|Co‐NWS|Cu half‐cell delivers stable cycling (over 260 cycles) with a high average Coulombic efficiency of 98%, and the symmetric cell shows a long cycle life of more than 500 h. Remarkably, the full cell shows a long‐term lifespan (over 1500 cycles with 92% capacity retention) at high current density in the carbonate electrolyte. This work opens up a strategy for developing dendrites‐free, low‐cost, and long‐lifespan SMBs in carbonate electrolytes.

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Abstract

The inability of damaged load-bearing cartilage to regenerate and self-repair remains a long-standing challenge in clinical settings. In the past, the use of PVA hydrogels as cartilage replacements has been explored; however, both pristine and annealed PVA are not ideal for load-bearing cartilage applications, and new materials with improved properties are highly desirable. In this work, we developed a novel hybrid hydrogel system consisting of glycerol-modified PVA hydrogel reinforced by a 3D printed PCL-graphene composite scaffold. The composition of the hydrogel within the hybrid material was optimized to achieve high water retention and enhanced stiffness. The hybrid hydrogel formed by reinforcement with a 3D printed PCL-graphene scaffold with optimized architecture demonstrated desirable mechanical properties (stiffness, toughness, and tribological properties) matching those of natural load-bearing cartilage. Our novel hydrogel system has also been designed to provide drug release and on-demand photothermal conversion functions and at the same time offers excellent biocompatibility with low cell adhesion. These promising properties may allow our unique hybrid hydrogel system to be used for potential applications, such as load-bearing cartilage repair/replacement, as well as targeting certain challenging clinical conditions, such as the treatment of severe arthritis.