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Electrochemical conversion of nitrate to ammonia offers an efficient approach to reducing nitrate pollutants and a potential technology for low-temperature and low-pressure ammonia synthesis. However, the process is limited by multiple competing reactions and NO 3 − adsorption on cathode surfaces. Here, we report a Fe/Cu diatomic catalyst on holey nitrogen-doped graphene which exhibits high catalytic activities and selectivity for ammonia production. The catalyst enables a maximum ammonia Faradaic efficiency of 92.51% (−0.3 V(RHE)) and a high NH 3 yield rate of 1.08 mmol h −1 mg −1 (at − 0.5 V(RHE)). Computational and theoretical analysis reveals that a relatively strong interaction between NO 3 − and Fe/Cu promotes the adsorption and discharge of NO 3 − anions. Nitrogen-oxygen bonds are also shown to be weakened due to the existence of hetero-atomic dual sites which lowers the overall reaction barriers. The dual-site and hetero-atom strategy in this work provides a flexible design for further catalyst development and expands the electrocatalytic techniques for nitrate reduction and ammonia synthesis. Nitrate electroreduction to ammonia can decrease pollutants and produce high-value ammonia. Here, the authors design a Fe/Cu diatomic catalyst on nitrogen-doped graphene, which exhibits high catalytic activities of and selectivity for ammonia.

Catastrophically mechanical failure of soft self-healing materials is unavoidable due to their inherently poor resistance to crack propagation. Here, with a model system, i.e., soft self-healing polyurea, we present a biomimetic strategy of surpassing trade-off between soft self-healing and high fracture toughness, enabling the conversion of soft and weak into soft yet tough self-healing material. Such an achievement is inspired by vascular smooth muscles, where core-shell structured Galinstan micro-droplets are introduced through molecularly interfacial metal-coordinated assembly, resulting in an increased crack-resistant strain and fracture toughness of 12.2 and 34.9 times without sacrificing softness. The obtained fracture toughness is up to 111.16 ± 8.76 kJ/m 2 , even higher than that of Al and Zn alloys. Moreover, the resultant composite delivers fast self-healing kinetics (1 min) upon local near-infrared irradiation, and possesses ultra-high dielectric constants (~14.57), thus being able to be fabricated into sensitive and self-healing capacitive strain-sensors tolerant towards cracks potentially evolved in service. Catastrophically mechanical failure, of soft self-healing materials often stems from its poor resistance to crack, propagation. Here, the authors present a strategy of surpassing trade-off, between soft self-healing and high fracture toughness, enabling the, conversion of soft and weak into soft yet tough self-healing materials.

As an important vehicle in road construction, the unmanned roller is rapidly advancing in its autonomous compaction capabilities. To overcome the challenges of GNSS positioning failure during tunnel construction and diminished visual positioning accuracy under different illumination levels, we propose a feature-layer fusion positioning system based on a camera and LiDAR. This system integrates loop closure detection and LiDAR odometry into the visual odometry framework. Furthermore, recognizing the prevalence of similar scenes in tunnels, we innovatively combine loop closure detection with the compaction process of rollers in fixed areas, proposing a selection method for loop closure candidate frames based on the compaction process. Through on-site experiments, it is shown that this method not only enhances the accuracy of loop closure detection in similar environments but also reduces the runtime. Compared with visual systems, in static positioning tests, the longitudinal and lateral accuracy of the fusion system are improved by 12 mm and 11 mm, respectively. In straight-line compaction tests under different illumination levels, the average lateral error increases by 34.1% and 32.8%, respectively. In lane-changing compaction tests, this system enhances the positioning accuracy by 33% in dim environments, demonstrating the superior positioning accuracy of the fusion positioning system amid illumination changes in tunnels.

High global incidence of prostate cancer has led to a focus on prevention and treatment strategies to reduce the impact of this disease in public health. Boron compounds are increasingly recognized as preventative and chemotherapeutic agents. However, systemic administration of soluble boron compounds is hampered by their short half-life and low effectiveness. Here we report on hollow boron nitride (BN) spheres with controlled crystallinity and boron release that decrease cell viability and increase prostate cancer cell apoptosis. In vivo experiments on subcutaneous tumour mouse models treated with BN spheres demonstrated significant suppression of tumour growth. An orthotopic tumour growth model was also utilized and further confirmed the in vivo anti-cancer efficacy of BN spheres. Moreover, the administration of hollow BN spheres with paclitaxel leads to synergetic effects in the suppression of tumour growth. The work demonstrates that hollow BN spheres may function as a new agent for prostate cancer treatment. Use of soluble boron compounds in prostate cancer therapy is hampered by their short half-life time and low effectiveness. Here, the authors show that boron nitride nanospheres with controlled boron release can reduce proliferation of prostate cancer cells and inhibit tumour growth in animal models.

Three-dimensional graphene architectures in the macroworld can in principle maintain all the extraordinary nanoscale properties of individual graphene flakes. However, current 3D graphene products suffer from poor electrical conductivity, low surface area and insufficient mechanical strength/elasticity; the interconnected self-supported reproducible 3D graphenes remain unavailable. Here we report a sugar-blowing approach based on a polymeric predecessor to synthesize a 3D graphene bubble network. The bubble network consists of mono- or few-layered graphitic membranes that are tightly glued, rigidly fixed and spatially scaffolded by micrometre-scale graphitic struts. Such a topological configuration provides intimate structural interconnectivities, freeway for electron/phonon transports, huge accessible surface area, as well as robust mechanical properties. The graphene network thus overcomes the drawbacks of presently available 3D graphene products and opens up a wide horizon for diverse practical usages, for example, high-power high-energy electrochemical capacitors, as highlighted in this work. Three-dimensional graphene offers an ideal sheet-to-sheet connectivity of assembled graphenes, but often suffers from poor electrochemical performance. Wang et al . present a sugar-blowing technique to prepare a 3D graphene, which overcomes such problems and shows potential in supercapacitor applications.

Spatiotemporal trajectory classification is essential for intelligent perception systems but faces challenges including weak separability of dynamic features, representation collapse under limited samples, and heterogeneous conflicts in multimodal data. To address these issues, we propose K-M LLM-pro, a physics-guided cross-modal adaptation framework that integrates statistical mechanics with large language models (LLMs) to improve trajectory understanding. Our approach incorporates: (1) physics-informed prompt engineering based on Kramers-Moyal coefficients, embedding physical constraints via reproducing kernel Hilbert space projection; (2) a dynamic patching optimization mechanism combining variance maximization and Lyapunov stability criteria for unified modeling of heterogeneous trajectories; and (3) dual spatiotemporal adapters with a parameter-efficient expansion strategy, injecting domain knowledge while optimizing only 3.8% of new parameters. Experimental results on public datasets such as Geolife and AIS show that K-M LLM-pro outperforms state-of-the-art models in classification accuracy, demonstrating strong performance even in few-shot scenarios with only 1% of training data. To our knowledge, this is the first work to integrate K-M coefficients as interpretable statistical priors into LLMs, offering a lightweight and effective solution for modeling complex spatiotemporal dynamics.

The relationship between distinct etiologies of acute kidney injury (AKI) and systemic immune-inflammatory biomarkers remains poorly defined. We performed a retrospective cohort study utilizing the eICU Collaborative Research Database (eICU-CRD), a multicenter U.S. dataset. The study included all patients diagnosed with AKI while excluding those with unclear AKI etiology or chronic kidney disease. Analyzed variables encompassed demographic characteristics, admission/discharge data, laboratory parameters, severity scores, and comorbidities. We specifically evaluated four clinically accessible systemic immune-inflammatory indices. Intergroup differences were assessed using letter notations, nonlinear relationships were explored restricted cubic spline models, and confounding variables were controlled for using propensity score matching. The analysis included 2,912 patients with AKI of known etiology, categorized into six etiological subgroups. Pairwise comparisons revealed significant differences (  < 0.001) in systemic immune-inflammatory biomarkers between a high-inflammation group (sepsis-associated AKI, acute tubular necrosis [ATN], hepatorenal syndrome) and other subgroups (hypovolemic, obstructive, ischemic, and drug-induced AKI). Notable disparities emerged for the systemic immune-inflammation index (SII) (4,120 vs. 3,340) and neutrophil-to-lymphocyte ratio (NLR) (19.1 vs. 14.7). Cox regression identified independent predictors of mortality: membership in the high-inflammation group (Odds Ratio [OR] = 1.67, 95% Confidence Interval [CI] = 1.39-2.00) and septic shock (OR = 1.65, 95% CI = 1.46-1.84). Following propensity score matching, the high-inflammation group remained a significant independent risk factor for mortality. Patients with AKI primarily attributed to sepsis, ATN, or hepatorenal syndrome constitute a high-inflammation subgroup characterized by elevated systemic immune-inflammatory biomarkers. This subgroup independently predicts increased mortality risk.

To address the conflict between pressure relief and support effectiveness caused by large-diameter boreholes in roadway surrounding rock, this paper proposes a method involving variable-diameter boreholes for pressure relief and energy dissipation. With a typical rock burst coal mine as the engineering context, the study establishes a mechanical model for variable-diameter boreholes through theoretical analysis to examine the elastic stress distribution around boreholes within the coal body. Physical similarity simulation tests are conducted to investigate the influence of conventional borehole and variable diameter borehole on the transmission pattern of dynamic load stress waves. Furthermore, numerical simulations are employed to explore the effects of reaming diameter, depth, and spacing on pressure relief, energy dissipation, and attenuation of dynamic stress wave transmission in roadway surrounding rock. The results demonstrate that stress within the coal surrounding the variable-diameter borehole correlates with the borehole radius, lateral pressure coefficient, and distance from the point to the borehole center, the extent of the plastic zone is influenced by borehole diameter, spacing, and depth. Increased diameter, reduced spacing, and greater depth of deep reaming holes exacerbate the transfer of stress concentration from the surrounding rock of the roadway to the deeper regions, facilitating the formation of stress double peak areas. Moreover, the variable diameter position should be within the original stress peak position of the surrounding rock in the roadway, with deep reaming passing through the stress concentration area for optimal results. This study offers guidance on the prevention and control technology for rock bursts in deep coal mining operations.

Large-diameter pressure relief boreholes are one of the primary measures for preventing coal mine rockburst. However, the implementation of these boreholes disrupts the original support structure of the roadway surrounding rock, leading to conflicts with surrounding rock control. Therefore, the pressure relief and energy dissipation behavior of variable-diameter boreholes in roadway surrounding rock was studied. Using a typical rockburst-prone coal mine as the engineering background. Based on elastic–plastic mechanics theory, the elastic solution for the stress distribution around the borehole and the extent of the pressure relief zone are analyzed. Numerical simulation software was used to study the effects of variable diameter drilling parameters (deep reaming diameter, deep reaming depth, and deep reaming spacing) on the pressure relief of roadway surrounding rock, energy dissipation in the roadway, and roadway deformation. The research results indicate that the distribution range of the pressure relief zone is influenced by the vertical stress, lateral pressure coefficient, cohesion, and internal friction angle of the coal body. The maximum radius of the pressure relief zone increases with the borehole diameter. As the deep reaming diameter increases and the borehole spacing decreases, the stress concentration in the surrounding rock of the roadway shifts more significantly toward the deeper region, making it easier to form a dual-peak stress zone. This enhances the pressure relief and stress transfer effect on the surrounding rock of the roadway, leading to greater energy dissipation. From the perspective of energy dissipation, it is concluded that the optimal location for the variable-diameter borehole should be within the peak vertical stress zone of the surrounding rock that has not been relieved. This study provides guidance for the prevention and control of dynamic disasters in deep coal and rock.

Unmanned rollers are typically equipped with satellite-based positioning systems for positional monitoring. However, satellite-based positioning systems may result in unmanned rollers driving out of the specified compaction areas during asphalt road construction, which affects the compaction quality and has potential safety hazards. Additionally, satellite-based positioning systems may encounter signal interference and cannot locate unmanned rollers. To solve this problem, a lateral positioning method for unmanned rollers is proposed to realize the positioning of unmanned rollers relative to asphalt road. First, we captured images from different perspectives and developed a dataset for asphalt road construction. Second, a method for boundary extraction of asphalt road is proposed to accurately locate pixels of asphalt road boundary. Subsequently, the lateral distances are measured by the designed lateral positioning methods. Finally, field validation experiments are conducted to evaluate the effectiveness of the proposed lateral positioning method. The results indicate that the method excels in extracting the asphalt road boundary. Furthermore, the proposed lateral positioning method shows excellent performance, with a mean relative error of 3.40% and a frequency of 6.25 Hz. The proposed lateral positioning method meets the performance requirements for lateral positioning in both accuracy and real-time in asphalt road construction for unmanned rollers.