1D WS2©MoTe2 Nanostructures In their Research Article (10.1002/sstr.202500258), Vojtech Kundrat, Reshef Tenne and co-workers synthesize core-shell WS2©WTe2 and inverted WTe2©WS2 nanotubes together with the WS2©MoTe2 nanotubular analogues by van der Waals epitaxy. The synthesized nanostructures are characterized and analyzed thoroughly using combined experimental and theoretical tools. The synthesized nanotubes reveal unique optical characteristics which are confirmed by computational models. The cover art displays models of both WS2©WTe2 and WTe2©WS2 nanotubes placed on a TEM grid. The inset shows an exploded view of the TEM image of a WS2©WTe2 nanotube with the model overlaid on its structure.

Small molecules often suffer from poor druggability, and their binding conformations with nucleic acids play a critical role in determining biological function and application potential. The interaction between small molecules and functional nucleic acids (FNAs) offers key insights into nucleic acid conformational mechanisms and structural adaptability. Among them, malachite green (MG), a triphenylmethane dye, has emerged as a model molecule due to its unique photophysical properties, especially its fluorescence activation upon conformational restriction. Importantly, MG has also drawn significant attention due to its known toxicity and widespread use in aquaculture and industry, raising concerns in areas such as food safety and environmental health. Therefore, studying MG's interaction with FNAs advances molecular design and bioengineering while supporting safer, more effective detection and detoxification strategies. Using MG as a representative case, this review systematically discusses its binding mechanisms with five typical FNA structures—RNA aptamers, DNA aptamers, G‐quadruplexes, double‐stranded DNA, and thymine‐Hg(II)‐thymine motifs. Herein, eight major application areas of MG‐FNA complexes, including MG biosensing, target biosensing, bioimaging, synthetic biology, structural monitoring, biocatalysis, masking application, and physiological regulation, is further summarized. Finally, the future directions for enhancing MG‐FNA‐based molecular systems are highlighted, offering guidance for advancing nucleic acid nanotechnology.

High‐performance materials occupying unexplored regions of material property space must be developed for scientific advancement and engineering applications. Advanced fabrication techniques and approaches have been developed and used to fabricate 3D structures with complicated geometries and nanoscale feature sizes, leading to unprecedented mechanical properties by optimizing topologies and controlling feature sizes. Shell‐based lattice materials, characterized by their smooth and continuous geometries, exhibit distinct advantages over truss‐ or plate‐based lattices, including fabrication‐friendly open‐cell topologies and uniform strain‐energy distributions. The combination of shell‐based topology, nanoscale feature size, and robust constituent materials imparts superior mechanical properties to shell‐based nanoarchitected materials, such as high modulus, high strength (even achieving theoretical limits), remarkable mechanical resilience, and significant energy‐absorption capacities. Herein, the recent advances in design, fabrication, and mechanics of shell‐based nanoarchitected materials are reviewed. First, the design and optimization of shell‐based topology and associated fabrication techniques are introduced. Subsequently, their mechanical behaviors and properties are summarized, including Young's modulus, compressive strength, deformation mechanisms, and energy‐absorption characteristics. Furthermore, recent advances in multifunctional applications are discussed, highlighting their potential applications in the thermal, biomedical, optical, electromagnetic, acoustic, and catalytic fields. Finally, unresolved issues and future challenges in the field of shell‐based nanoarchitected materials are discussed.

The development of high‐performance photocatalysts is essential for sustainable, carbon‐free hydrogen production. To address the limitations of rapid charge recombination in single component systems, heterojunction architectures combining distinct semiconductors have emerged as a promising strategy. In this study, 2D Cu 2− x S/CdS heterostructures are constructed by partial cation exchange of single‐crystalline Cu 2− x S nanoplates, yielding a series of heterojunctions with tunable Cu‐to‐Cd ratios and well‐aligned epitaxial interfaces with minimal lattice mismatch. Comprehensive structural and spectroscopic analyses confirm the formation of type‐II band alignment, in which Cu 2− x S serves as a hole acceptor and CdS acts as a primary light absorber, hence a site for hydrogen evolution reaction (HER). The optimized Cu 2− x S/CdS‐65 composition, with a 65% Cd molar fraction, achieves a hydrogen production rate of 5.75 mmol g −1 h −1 , 7.6 times higher than that of pure CdS and superior to Cu/Cd sulfide‐based photocatalysts. This enhanced performance is attributed to the synergistic combination of rapid interfacial charge transfer facilitated by epitaxial p–n junctions, as well as sufficient CdS domains ensuring efficient light absorption and surface reactivity for HER. These results highlight the crucial role of interfacial engineering and composition control in guiding the rational design of high‐performance 2D photocatalytic systems for solar‐to‐hydrogen energy conversion.

Atomic force microscopy (AFM) has become an indispensable tool for characterizing DNA nanostructures. However, conventional AFM analysis relies heavily on manual operation, which is inherently time‐consuming and prone to subjective bias. Herein, a transfer learning‐assisted AFM single‐particle analysis (AFMSPA) tool is presented, which enables automated high‐throughput AFM structural analysis of DNA origami nanostructures. AFMSPA leverages a U‐Net‐based neural network trained on experimentally acquired datasets to automatically detect and classify individual DNA origami in raw AFM images. It further enables the on‐demand extraction of morphological parameters by users. The tool currently supports fully automated quantitative analysis for four representative 2D DNA origami nanostructures, i.e., triangle, rectangle, hexagon, and diatom, each achieving a detection accuracy above 0.95. Moreover, by integrating a human‐in‐the‐loop transfer learning strategy, AFMSPA can be readily adapted to a broad range of DNA origami nanostructures, offering a scalable solution for the statistical analysis of AFM datasets in the field of self‐assembled nanomaterials.

Traditional wound dressings, such as gauze and film, have been widely used to protect and close wound areas. However, they often face challenges, such as weak adhesion, incomplete hemostasis, and poor adaptability to irregular surfaces. Bioactive adhesive hydrogels have potential in wound healing because they seal wounds and actively promote tissue regeneration. Among the various strategies, incorporating bioactive inorganic ions, particularly zinc ions (Zn 2+ ), attracts increasing attention because of their crucial roles in hemostasis, immunomodulation, cell proliferation, angiogenesis, and collagen remodeling. Herein, Zn 2+ ‐releasing adhesive hydrogels are developed via ZnO 2 ‐mediated disulfide bonding, Zn 2+ ‐thiol metal coordination, and thiol‐ene reactions between thiolated and maleimide‐conjugated gelatin. These hydrogels exhibit tunable gelation time (11–21 s), mechanical strength (520–810 Pa), and enzymatic degradation behavior depending on the ZnO 2 concentration. They also provide sustained Zn 2+ and H 2 O 2 release for up to 21 days. Notably, the hydrogels show strong adhesion (142–165 kPa), outperforming fibrin glue, and adhere to various tissue surfaces. In vivo, Zn 2+ ‐releasing hydrogels significantly enhance wound healing by improving hemostasis, anti‐inflammatory effects, angiogenesis, and tissue remodeling. The results suggest that these hydrogels have substantial potential as multifunctional wound adhesives for effective wound management.

Autonomous Laboratory Instrumentation Control In article number 2500173, Yong Xie, Andres Castellanos-Gomez, and Kexin He present a stylized representation of a fully autonomous experimental setup driven by a language model, visualizing real-time decision-making, data feedback, and robotic control. The image highlights the synergy between artificial intelligence and lab automation, representing a paradigm shift in experimental material science.

Resistive Random Access Memories The cover image illustrates a flexible parylene-C-based resistive random access memory (RRAM) array incorporating a graphene diffusion barrier. The graphene layer suppresses excessive metal ion diffusion, thereby controlling the growth of conductive filaments. Artificial neural network simulations further demonstrate the feasibility of these devices for neuromorphic computing applications. More details can be found in article number 2500056 by Dong-Wook Park, Yoon Kim, and co-workers.

Filler‐reinforced rubber materials are used in tires, belts, and other items indispensable to the daily lives of people. The characteristic required to realize a circular economy is an improved fracture resistance, i.e., a longer product lifetime. To improve the fracture resistance, understanding the fracture mechanism of materials is essential. Fracture results from the appearance of microscopic cavities, but the understanding of the heterogeneous structural deformation factors (fillers and molecular chains) responsible for cavities remains inadequate. Herein, the first capture of fracture precursor phenomena using simultaneous X‐ray scattering/absorption–computed tomography measurements is reported. As precursor phenomena, deformation induces a grain structure in which each grain has different alignment of silica particles and orientation of molecular chains, and exfoliation of molecular chains from the silica particle surface occurs between the grains. Capturing the precursor phenomena from the heterogeneity perspective provides new insight into the design of materials.

Direct modulation of radio frequency (RF) signals has broad applications in wireless communications, radar detection, and other fields. However, the traditional methods face the limitations of high bandwidth requirements and poor noise resistance in scenarios of high data rates and complex environments, and hence, they are gradually replaced by digital modulation technologies. Recently, metasurface has become a significant innovation in RF direct modulation due to its flexibility and low cost. By controlling the amplitude and phase responses of metasurface units, efficient RF signal modulations have been achieved. However, the current metasurface modulation techniques are mainly focused on harmonic modulations and are difficult to implement the baseband modulation. To address these challenges, A high‐order amplitude‐phase modulated metasurface offers precise amplitude and phase control, enabling a spatial vector‐signal generator for efficient baseband and complex signal generation. This innovative approach enhances flexibility and dynamic adaptability in RF modulation,,demonstrating significant potentialfor high‐speed wireless communication, radar detection, and high‐resolution imaging.