Explore the vast range of titles available.

Soft robots that can harvest energy from environmental resources for autonomous locomotion is highly desired; however, few are capable of adaptive navigation without human interventions. Here, we report twisting soft robots with embodied physical intelligence for adaptive, intelligent autonomous locomotion in various unstructured environments, without on-board or external controls and human interventions. The soft robots are constructed of twisted thermal-responsive liquid crystal elastomer ribbons with a straight centerline. They can harvest thermal energy from environments to roll on outdoor hard surfaces and challenging granular substrates without slip, including ascending loose sandy slopes, crossing sand ripples, escaping from burying sand, and crossing rocks with additional camouflaging features. The twisting body provides anchoring functionality by burrowing into loose sand. When encountering obstacles, they can either self-turn or self-snap for obstacle negotiation and avoidance. Theoretical models and finite element simulation reveal that such physical intelligence is achieved by spontaneously snapping-through its soft body upon active and adaptive soft bodyobstacle interactions. Utilizing this strategy, they can intelligently escape from confined spaces and maze-like obstacle courses without any human intervention. This work presents a de novo design of embodied physical intelligence by harnessing the twisting geometry and snap-through instability for adaptive soft robot-environment interactions.

The physiochemical properties of the solid-electrolyte interphase, primarily governed by electrolyte composition, have a profound impact on the electrochemical cycling of metallic lithium. Herein, we discover that the effect of nitrate anions on regulating lithium deposition previously known in ether-based electrolytes can be extended to carbonate-based systems, which dramatically alters the nuclei from dendritic to spherical, albeit extremely limited solubility. This is attributed to the preferential reduction of nitrate during solid-electrolyte interphase formation, and the mechanisms behind which are investigated based on the structure, ion-transport properties, and charge transfer kinetics of the modified interfacial environment. To overcome the solubility barrier, a solubility-mediated sustained-release methodology is introduced, in which nitrate nanoparticles are encapsulated in porous polymer gel and can be steadily dissolved during battery operation to maintain a high concentration at the electroplating front. As such, effective dendrite suppression and remarkably enhanced cycling stability are achieved in corrosive carbonate electrolytes. The solid-electrolyte interphase (SEI) is one of the governing factors for the reversibility of Li metal anode. Here, the authors reveal the impact of nitrate additive on the SEI in carbonate electrolytes, and demonstrate a method to overcome the solubility limitation of nitrate.

Developing a viable metallic lithium anode is a prerequisite for next-generation batteries. However, the low redox potential of lithium metal renders it prone to corrosion, which must be thoroughly understood for it to be used in practical energy-storage devices. Here we report a previously overlooked mechanism by which lithium deposits can corrode on a copper surface. Voids are observed in the corroded deposits and a Kirkendall-type mechanism is validated through electrochemical analysis. Although it is a long-held view that lithium corrosion in electrolytes involves direct charge-transfer through the lithium–electrolyte interphase, the corrosion observed here is found to be governed by a galvanic process between lithium and the copper substrate—a pathway largely neglected by previous battery corrosion studies. The observations are further rationalized by detailed analyses of the solid–electrolyte interphase formed on copper and lithium, where the disparities in electrolyte reduction kinetics on the two surfaces can account for the fast galvanic process. Developing a stable metallic lithium anode is necessary for next-generation batteries; however, lithium is prone to corrosion, a process that must be better understood if practical devices are to be created. A Kirkendall-type mechanism of lithium corrosion has now been observed. The corrosion is fast and is governed by a galvanic process.

•Shared Parameter Mechanism of Large Convolutional Kernels: This study proposes a brain tumor segmentation model using shared-parameter large convolutional kernels. It combines 11×11 kernels (capturing global features via broad receptive fields) with 5×5 kernels (extracting fine details). To reduce parameter overload, a sharing mechanism is implemented: central 3×3 regions retain independent parameters for local precision, while peripheral areas share parameters to maintain wide spatial perception. This dual-scale strategy balances computational efficiency with segmentation accuracy, effectively decreasing model complexity while preserving crucial tumor boundary and texture information. The design achieves robust performance through optimized feature extraction across different scales.•Dynamic Channel Sampling Method Enhances Segmentation Accuracy: To enhance segmentation accuracy, this study introduces a dynamic channel sampling method that strategically addresses two critical challenges associated with 1×1 convolutional channel compression: spatial feature information loss and elevated memory access demands. By implementing an adaptive mechanism to dynamically adjust channel sampling strategies during processing, the proposed approach effectively preserves essential spatial features while concurrently optimizing memory utilization. This dual improvement not only mitigates performance degradation caused by rigid compression techniques but also yields a significant enhancement in slice segmentation accuracy, demonstrating the method's capability to balance computational efficiency with feature preservation in medical imaging tasks.•Experimental Validation and Performance Advantages: The model was rigorously validated on BraTs2020, BraTs2024, and Medical Segmentation Decathlon Brain 2018 datasets, outperforming state-of-the-art ConvNet and Transformer architectures in Dice coefficient, Hausdorff distance, and sensitivity. By addressing traditional channel compression limitations, it achieved superior segmentation accuracy and set new benchmarks. The framework’s efficacy in balancing global and fine-grained features enabled precise tumor boundary delineation while maintaining computational efficiency. These results provide critical methodological insights for developing lightweight, high-precision medical image segmentation models. The advancements offer practical solutions to clinical neuroimaging challenges, enhancing diagnostic reliability and paving the way for scalable deployment in resource-constrained healthcare environments. This study centers around the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in the domain of computer vision, delving deeply into the issues pertaining to parameters and computational complexity that stem from the utilization of large convolutional kernels. Even though the size of the convolutional kernels has been extended up to 51×51, the enhancement of performance has hit a plateau, and moreover, striped convolution incurs a performance degradation. Enlightened by the hierarchical visual processing mechanism inherent in humans, this research innovatively incorporates a shared parameter mechanism for large convolutional kernels. It synergizes the expansion of the receptive field enabled by large convolutional kernels with the extraction of fine-grained features facilitated by small convolutional kernels. To address the surging number of parameters, a meticulously designed parameter sharing mechanism is employed, featuring fine-grained processing in the central region of the convolutional kernel and wide-ranging parameter sharing in the periphery. This not only curtails the parameter count and mitigates the model complexity but also sustains the model's capacity to capture extensive spatial relationships. Additionally, in light of the problems of spatial feature information loss and augmented memory access during the 1 × 1 convolutional channel compression phase, this study further puts forward a dynamic channel sampling approach, which markedly elevates the accuracy of tumor subregion segmentation. To authenticate the efficacy of the proposed methodology, a comprehensive evaluation has been conducted on three brain tumor segmentation datasets, namely BraTs2020, BraTs2024, and Medical Segmentation Decathlon Brain 2018. The experimental results evince that the proposed model surpasses the current mainstream ConvNet and Transformer architectures across all performance metrics, proffering novel research perspectives and technical stratagems for the realm of medical image segmentation.

Kirigami, a traditional paper cutting art, offers a promising strategy for 2D-to-3D shape morphing through cut-guided deformation. Existing kirigami designs for target 3D curved shapes rely on intricate cut patterns in thin sheets, making the inverse design challenging. Motivated by the Gauss-Bonnet theorem that correlates the geodesic curvature along the boundary with the Gaussian curvature, here, we exploit programming the curvature of cut boundaries rather than the complex cut patterns in kirigami sheets for target 3D curved morphologies through both forward and inverse designs. The strategy largely simplifies the inverse design. Leveraging this strategy, we demonstrate its potential applications as a universal and nondestructive gripper for delicate objects, including live fish, raw egg yolk, and a human hair, as well as dynamically conformable heaters for human knees. This study opens a new avenue to encode boundary curvatures for shape-programing materials with potential applications in soft robotics and wearable devices. Kirigami, a traditional paper cutting art, offers a promising strategy for 2D-to-3D shape morphing through cut-guided deformation. Here, authors report a simple strategy of cut boundary curvature-guided 3D shape morphing and its applications in non-destructive grippers and dynamically conformable heaters.

In 2020, China proposed the goal of achieving carbon emission peaks by 2030 and carbon neutrality by 2060. For China, whose energy consumption structure has long been dominated by fossil energy, carbon trading and new energy are crucial for the realization of the emission target. By establishing a connectedness network model, this paper studies the static and dynamic spillovers between the Hubei carbon trading market, new energy stock market, crude oil market, coal market, and natural gas market in China, and draws the following conclusions: (1) the static spillover index of the carbon–energy–stock system is 3.57% and the dynamic spillover index fluctuates between 7.67% and 22.62%, indicating that the spillover effect of the system is low; (2) for the whole system, whether from a static or dynamic perspective, the carbon market always plays the role of net information receiver, while new energy is the net information transmitter; (3) the new energy stock market and the coal market always act as net information transmitters to the carbon market; and (4) the spillover effect of the system is asymmetric, wherein the system is more sensitive to negative information about price returns, and this asymmetry is much greater when the system is active.

Whereas standard transmission electron microscopy studies are unable to preserve the native state of chemically reactive and beam-sensitive battery materials after operation, such materials remain pristine at cryogenic conditions. It is then possible to atomically resolve individual lithium metal atoms and their interface with the solid electrolyte interphase (SEI). We observe that dendrites in carbonate-based electrolytes grow along the (preferred), , or directions as faceted, single-crystalline nanowires. These growth directions can change at kinks with no observable crystallographic defect. Furthermore, we reveal distinct SEI nanostructures formed in different electrolytes.

The practical applications of lithium metal anodes in high-energy-density lithium metal batteries have been hindered by their formation and growth of lithium dendrites. Herein, we discover that certain protein could efficiently prevent and eliminate the growth of wispy lithium dendrites, leading to long cycle life and high Coulombic efficiency of lithium metal anodes. We contend that the protein molecules function as a “self-defense” agent, mitigating the formation of lithium embryos, thus mimicking natural, pathological immunization mechanisms. When added into the electrolyte, protein molecules are automatically adsorbed on the surface of lithium metal anodes, particularly on the tips of lithium buds, through spatial conformation and secondary structure transformation from α-helix to β-sheets. This effectively changes the electric field distribution around the tips of lithium buds and results in homogeneous plating and stripping of lithium metal anodes. Furthermore, we develop a slow sustained-release strategy to overcome the limited dispersibility of protein in the ether-based electrolyte and achieve a remarkably enhanced cycling performance of more than 2000 cycles for lithium metal batteries. The practical application of lithium metal anodes in high-energy-density lithium metal batteries is hindered by the formation and growth of lithium dendrites. Here, authors report fibroin protein as an electrolyte additive to prevent and eliminate the growth of wispy lithium dendrites.

Shape-morphing capabilities are crucial for enabling multifunctionality in both biological and artificial systems. Various strategies for shape morphing have been proposed for applications in metamaterials and robotics. However, few of these approaches have achieved the ability to seamlessly transform into a multitude of volumetric shapes post-fabrication using a relatively simple actuation and control mechanism. Taking inspiration from thick origami and hierarchies in nature, we present a hierarchical construction method based on polyhedrons to create an extensive library of compact origami metastructures. We show that a single hierarchical origami structure can autonomously adapt to over 10 3 versatile architectural configurations, achieved with the utilization of fewer than 3 actuation degrees of freedom and employing simple transition kinematics. We uncover the fundamental principles governing theses shape transformation through theoretical models. Furthermore, we also demonstrate the wide-ranging potential applications of these transformable hierarchical structures. These include their uses as untethered and autonomous robotic transformers capable of various gait-shifting and multidirectional locomotion, as well as rapidly self-deployable and self-reconfigurable architecture, exemplifying its scalability up to the meter scale. Lastly, we introduce the concept of multitask reconfigurable and deployable space robots and habitats, showcasing the adaptability and versatility of these metastructures. Enabling complex shape morphing in engineering systems remains a significant challenge. In this work, authors demonstrate that a transformer-like hierarchical origami metastructure can self-reconfigure into over 1000 versatile structures with fewer than 3 actuation degrees of freedom and simple control.

Achieving multicapability in a single soft gripper for handling ultrasoft, ultrathin, and ultraheavy objects is challenging due to the tradeoff between compliance, strength, and precision. Here, combining experiments, theory, and simulation, we report utilizing angle-programmed tendril-like grasping trajectories for an ultragentle yet ultrastrong and ultraprecise gripper. The single gripper can delicately grasp fragile liquids with minimal contact pressure (0.05 kPa), lift objects 16,000 times its own weight, and precisely grasp ultrathin, flexible objects like 4-μm-thick sheets and 2-μm-diameter microfibers on flat surfaces, all with a high success rate. Its scalable and material-independent design allows for biodegradable noninvasive grippers made from natural leaves. Explicitly controlled trajectories facilitate its integration with robotic arms and prostheses for challenging tasks, including picking grapes, opening zippers, folding clothes, and turning pages. This work showcases soft grippers excelling in extreme scenarios with potential applications in agriculture, food processing, prosthesis, biomedicine, minimally invasive surgeries, and deep-sea exploration. Soft grippers can emulate human hands, but it remains challenging to achieve multiple capability in manipulating various objects in one design. Hong et al. utilize a kirigami gripper with controllable and programmable trajectories to manipulate objects spanning from ultra-soft to ultra-strong with high precision.