Surface Technology (表面技术)•2026•DOI: 10.16490/j.cnki.issn.1001-3660.2026.08.008
Cavitation erosion and wear failure critically limit the service life of flow-passing components such as pump impellers, turbine blades, and propeller systems subjected to high-speed liquid impact and cyclic flow-induced stresses. This work aims to design a high-performance surface coating with enhanced hardness, wear resistance, and cavitation erosion resistance by tailoring the Nb content in a CoCrNi medium-entropy alloy (MEA) system. CoCrNiNbx (x = 0, 0.2, 0.4, 0.6, 0.8, 1.0, and 1.2) coatings were fabricated on 316L stainless-steel substrates with an FL020 fiber laser under identical processing parameters. The effect of Nb addition on the phase constitution, microstructure, mechanical properties, tribological behavior, and cavitation performance of the coatings was comprehensively investigated to determine the optimal composition for balanced mechanical and anti-erosion properties. Phase analysis by X-ray diffraction (XRD) showed that increasing Nb content promoted a transition from a single face-centered cubic (FCC) solid solution to a dual FCC + hexagonal close-packed (HCP) phase structure. The emergence and growth of the Nb-rich HCP phase were accompanied by pronounced lattice distortion and precipitation strengthening. Microstructural characterization using field-emission scanning electron microscopy (SEM) combined with energy-dispersive spectroscopy (EDS) revealed that Nb preferentially segregated along interdendritic regions, where fine HCP-phase precipitates gradually formed a semi-continuous strengthening network. Electron backscatter diffraction (EBSD) analysis further quantified grain size and phase distribution. The average microhardness of the coatings initially increased and then decreased with increasing Nb molar ratio x, peaking at 689 HV0.1 for x = 0.6, approximately 3.7 times that of the substrate. Wear performance followed the same trend. Cavitation erosion tests demonstrated that the CoCrNiNb1.0 coating exhibited optimal cavitation erosion resistance, with mass loss significantly lower than that of the 316L substrate, achieving an order-of-magnitude improvement. The optimal Nb addition (x = 0.6–1.0) balances strength and toughness, significantly enhancing the wear and cavitation erosion resistance of CoCrNi-based MEA laser-cladded coatings. This study provides experimental evidence and process references for engineering applications of CoCrNi-based MEA coatings in high-flow-velocity liquid impact environments.
Nano-Micro Letters•2026•DOI: 10.1007/s40820-025-01999-4
With the widespread application of lithium batteries in electric vehicles and energy storage systems, battery-related safety and reliability issues have become increasingly prominent. Conventional monitoring methods often struggle to address dynamic changes under complex operando. In recent years, flexible sensing technology has emerged as a promising solution for battery health monitoring due to its high adaptability and conformability to complex structures. Meanwhile, empowered by artificial intelligence (AI) for data analysis, the collected data enables efficient and accurate state assessment, offering robust support for accident prevention. Against this background, this paper first explores the integrated applications of flexible sensors in battery health monitoring and their unique advantages in addressing complex battery operating conditions, while analyzing the potential of AI in battery state analysis. Subsequently, it systematically reviews mainstream flexible sensing technologies (e.g., film sensors, thermocouples, and optical fiber sensors), elucidating their mechanisms for revealing intricate internal battery processes during operation. Finally, the paper discusses AI’s role in enhancing monitoring efficiency and accuracy, and envisions future research directions and application prospects. This work aims to provide technical references for the battery health monitoring field as well as promote the application of flexible sensing technologies in improving battery system safety and reliability.
Nano-Micro Letters•2025•DOI: 10.1007/s40820-025-01661-z
Solar steam generation (SSG) offers a cost-effective solution for producing clean water by utilizing solar energy. However, integrating effective thermal management and water transportation to develop high-efficiency solar evaporators remains a significant challenge. Here, inspired by the hierarchical structure of the stem of bird of paradise, a three-dimensional multiscale liquid metal/polyacrylonitrile (LM/PAN) evaporator is fabricated by assembling LM/PAN fibers. The strong localized surface plasmon resonance of LM particles and porous structure of LM/PAN fibers with interconnected channels lead to efficient light absorption up to 90.9%. Consequently, the multiscale biomimetic LM/PAN evaporator achieves an outstanding water evaporation rate of 2.66 kg m−2 h−1 with a solar energy efficiency of 96.5% under one sun irradiation and an exceptional water rate of 2.58 kg m−2 h−1 in brine. Additionally, the LM/PAN evaporator demonstrates a superior purification performance for seawater, with the concentration of Na+, Mg2+, K+ and Ca2+ in real seawater dramatically decreased by three orders to less than 7 mg L−1 after desalination under light irradiation. The multiscale LM/PAN evaporator with hierarchical structure regulates the water transportation as well as thermal management for highly effective solar-driven evaporation, providing valuable insight into the structural design principles for advanced SSG systems.
Int. Journal of Mining Science and Technology (采矿与安全工程)•2025•DOI: 10.1016/j.ijmst.2025.06.006
To ensure the safe implementation of underground reservoirs in abandoned coal mines, this study explores the mechanical behavior and failure mechanisms of coal-concrete composite structures under staged cyclic loading. Specimens with coal-to-concrete height ratios ranging from 0.5:1 to 3:1 were tested, with damage evolution continuously monitored using acoustic emission techniques. Results indicate that while the peak strength of pure materials decreases by approximately 1 MPa under cyclic stress compared to uniaxial compression, composite specimens exhibit strength enhancements exceeding 5 MPa. However, the peak strength of composite specimens decreases with increasing coal height, from 30 MPa at CR0.5 to 20 MPa at CR3.0. The damage state was assessed using the dynamic elastic strain energy index and Felicity ratio, which revealed that composite specimens are more prone to early damage accumulation. Spatial acoustic emission localization further reveals distinct failure modes across specimens with varying height ratios. To elucidate these differences, interfacial effects were incorporated into a modified twin-shear unified strength theory. The refined model accurately predicts the internal strength distribution and failure characteristics of the composite structures. These findings provide a theoretical basis for the structural design and safe operation of underground reservoir dams.
Chinese Journal of Mechanical Engineering•2025•DOI: 10.1186/s10033-025-01356-x
With the swift advancement of industrial automation, robots have emerged as an essential component in emerging industries and high-end equipment, thereby propelling industrial production towards greater intelligence and efficiency. This paper reviews the pivotal technologies for motion planning of robots engaged in contact tasks within industrial automation contexts, encompassing environmental recognition, trajectory generation strategies, and sim-to-real transfer. Environmental recognition technology empowers robots to accurately discern objects and obstacles in their operational environment. Trajectory generation strategies formulate optimal motion paths based on environmental data and task specifications. Sim-to-real transfer is committed to effectively translating strategies from simulated environments to actual production, thereby diminishing the discrepancies between simulation and reality. The article also delves into the application of artificial intelligence in robot motion planning and how embodied intelligence models catalyze the evolution of robotics technology towards enhanced intelligence and automation. The paper concludes with a synthesis of the methodologies addressing this challenge and a perspective on the myriad challenges that warrant attention.
Chinese Journal of Mechanical Engineering•2025•DOI: 10.1186/s10033-025-01206-w
Predictive cruise control (PCC) is an intelligence-assisted control technology that can significantly improve the overall performance of a vehicle by using road and traffic information in advance. With the continuous development of cloud control platforms (CCPs) and telematics boxes (T-boxes), cloud-based predictive cruise control (CPCC) systems are considered an effective solution to the problems of map update difficulties and insufficient computing power on the vehicle side. In this study, a vehicle-cloud hierarchical control architecture for PCC is designed based on a CCP and T-box. This architecture utilizes waypoint structures for hierarchical and dynamic cooperative inter-triggering, enabling rolling optimization of the system and commanding parsing at the vehicle end. This approach significantly improves the anti-interference capability and resolution efficiency of the system. On the CCP side, a predictive fuel-saving speed-planning (PFSP) algorithm that considers the throttle input, speed variations, and time efficiency based on the waypoint structure is proposed. It features a forward optimization search without requiring weight adjustments, demonstrating robust applicability to various road conditions and vehicles equipped with constant cruise (CC) system. On the vehicle-side T-box, based on the reference control sequence with the global navigation satellite system position, the recommended speed is analyzed and controlled using the acute angle principle. Through analyzing the differences of the PFSP algorithm compared to dynamic programming (DP) and Model predictive control (MPC) algorithms under uphill and downhill conditions, the results show that the PFSP achieves good energy-saving performance compared to CC without exhibiting significant speed fluctuations, demonstrating strong adaptability to the CC system. Finally, by building an experimental platform and running field tests over a total of 2000 km, we verified the effectiveness and stability of the CPCC system and proved the fuel-saving performance of the proposed PFSP algorithm. The results showed that the CPCC system equipped with the PFSP algorithm achieved an average fuel-saving rate of 2.05%–4.39% compared to CC.
Int. Journal of Mining Science and Technology (采矿与安全工程)•2024•DOI: 10.1016/j.ijmst.2024.12.007
Salt cavern energy storage technology contributes to energy reserves and renewable energy scale-up. This study focuses on salt cavern gas storage in Jintan to assess the long-term stability of its surrounding rock under frequent operation. The fatigue test results indicate that stress holding significantly reduces fatigue life, with the magnitude of stress level outweighing the duration of holding time in determining peak strain. Employing a machine learning approach, the impact of various factors on fatigue life and peak strain was quantified, revealing that higher stress limits and stress holding adversely impact the fatigue index, whereas lower stress limits and rate exhibit a positive effect. A novel fatigue-creep composite damage constitutive model is constructed, which is able to consider stress magnitude, rate, and stress holding. The model, validated through multi-path tests, accurately captures the elasto-viscous behavior of salt rock during loading, unloading, and stress holding. Sensitivity analysis further reveals the time- and stress-dependent behavior of model parameters, clarifying that strain changes stem not only from stress variations but are also influenced by alterations in elasto-viscous parameters. This study provides a new method for the mechanical assessment of salt cavern gas storage surrounding rocks.