Search Results (377)

With the rapid development of the new energy vehicle industry, the quality control of battery pack glue application processes has become a critical factor in ensuring the sealing, insulation, and structural stability of the battery. However, existing detection methods face numerous challenges in complex industrial environments, such as metal reflections, interference from heating film grids, inconsistent orientations of glue strips, and the difficulty of accurately segmenting elongated targets, leading to insufficient precision and robustness in glue dimension measurement and glue break detection. To address these challenges, this paper proposes a battery pack glue application detection method that integrates the YOLOv11 deep learning model with pixel-level geometric analysis. The method first uses YOLOv11 to precisely extract the glue region and identify and block the heating film interference area. Glue strips orientation correction and image normalization are performed through adaptive binarization and Hough transformation. Next, high-precision pixel-level measurement of glue strip width and length is achieved by combining connected component analysis and multi-line statistical strategies. Finally, glue break and wire drawing defects are reliably detected based on image slicing and pixel ratio analysis. Experimental results show that the average measurement errors in glue strip width and length are only 1.5% and 2.3%, respectively, with a 100% accuracy rate in glue break detection, significantly outperforming traditional vision methods and mainstream instance segmentation models. Ablation experiments further validate the effectiveness and synergy of the modules. This study provides a high-precision and robust automated detection solution for glue application processes in complex industrial scenarios, with significant engineering application value. Full article

The increasing use of vinyl-coated metal (VCM) sheets in home appliances requires robust forming processes to prevent defects such as delamination and wrinkling, especially under elevated temperatures and humidity. This study presents a deep neural network (DNN)-based multi-objective optimization framework to determine optimal stamping parameters for VCM sheets. A delamination limit diagram (DLD) is experimentally established by combining limit dome height tests with immersion tests, defining the critical strain boundary under environmentally conditions. A finite element (FE) based dataset of four process variables was then used to train a DNN surrogate model with high predictive accuracy. Using the trained DNN model, Pareto-based optimization identifies nondominated solutions balancing delamination and wrinkling. The optimal condition was validated by FE simulation, confirming simultaneous suppression of both defects within the DLD. The proposed DNN–Pareto framework provides and efficient and reliable tool for defect prediction and optimization in VCM stamping, ensuring high surface quality and environmental durability. Full article

With the advancement of aerospace equipment toward high-speed and heavy-duty applications, conventional forced lubrication systems are facing significant challenges in terms of reliability and adaptability to complex operating conditions. Porous medium materials, owing to their unique self-lubricating and oil-retention capabilities, are regarded as an ideal lubrication solution. However, their seepage behavior is governed by the strong coupling effects of microscopic pore structures and fluid physicochemical properties, the mechanisms of which remain inadequately understood, thereby severely constraining the design and application of high-performance lubricating materials. To address this, this paper systematically reviews recent research progress on seepage behavior in porous media, with the aim of establishing a correlation between microstructural characteristics and macroscopic performance. Starting from the characterization of porous media, this work comprehensively analyzes the structure–seepage relationships in porous polymers, metal foams, and porous ceramics, and constructs a multi-scale theoretical framework encompassing macroscopic continuum theories, mesoscopic lattice Boltzmann methods (LBM), pore network models, and microscopic molecular dynamics. The advantages and limitations of experimental measurements and numerical simulation approaches are also compared. In particular, this study critically highlights the current neglect of key interfacial parameters such as surface wettability and pore roughness, and proposes an in-depth investigation into the seepage mechanisms of polyimide porous cage materials based on LBM. Furthermore, the potential application of emerging research paradigms such as data-driven approaches and intelligent computing in seepage studies is discussed. Finally, it is emphasized that future efforts should focus on developing deeply integrated cross-scale simulation methodologies, strengthening multi-physics coupling and artificial intelligence-assisted research, and advancing the development of intelligent porous lubricating materials with gradient structures or stimulus-responsive characteristics. This is expected to provide a solid theoretical foundation and technical pathway for the rational design and optimization of high-performance lubrication systems. Full article

Unmanned Aerial Vehicles (UAVs) are integral components of future 6G networks, offering rapid deployment, enhanced line-of-sight communication, and flexible coverage extension. However, UAV communications in low-altitude environments face significant challenges, including rapid link variations due to attitude instability, severe signal blockage by urban obstacles, and critical sensitivity to transmitter–receiver alignment. While traditional planar reconfigurable intelligent surfaces (RIS) show promise for mitigating these issues, they exhibit inherent limitations such as angular sensitivity and beam squint in wideband scenarios, compromising reliability in dynamic UAV scenarios. To address these shortcomings, this paper proposes and evaluates a spherical-cap reflective intelligent surface (ScRIS) specifically designed for dynamic low-altitude communications. The intrinsic curvature of the ScRIS enables omnidirectional reflection capabilities, significantly reducing sensitivity to UAV attitude variations. A rigorous analytical model founded on Generalized Sheet Transition Conditions (GSTCs) is developed to characterize the electromagnetic scattering of the curved metasurface. Three distinct 1-bit RIS unit cell coding arrangements, namely alternate, chessboard, and random, are investigated via numerical simulations utilizing CST Microwave Studio and experimental validation within a mechanically stirred reverberation chamber. Our results demonstrate that all tested ScRIS coding patterns markedly enhance electromagnetic field uniformity within the chamber and reduce the lowest usable frequency (LUF) by approximately 20% compared to a conventional metallic spherical reflector. Notably, the random coding pattern maximizes phase entropy, achieves the most uniform scattering characteristics and substantially reduces spatial field autocorrelation. Furthermore, the combined curvature and coding functionality of the ScRIS facilitates simultaneous directional focusing and diffuse scattering, thereby improving multipath diversity and spatial coverage uniformity. This effectively mitigates communication blind spots commonly encountered in UAV applications, providing a resilient link environment despite UAV orientation changes. To validate these findings in a practical context, we conduct link-level simulations based on a reproducible system model at 3.5 GHz, utilizing electromagnetic scale invariance to bridge the fundamental scattering properties observed in the RC to the application band. The results confirm that the ScRIS architecture can enhance link throughput by nearly five-fold at a 10 km range compared to a baseline scenario without RIS. We also propose a practical deployment strategy for urban blind-spot compensation, discuss hybrid planar-curved architectures, and conduct an in-depth analysis of a DRL-based adaptive control framework with explicit convergence and complexity analysis. Our findings validate the significant potential of ScRIS as a passive, energy-efficient solution for enhancing communication stability and coverage in multi-band 6G networks. Full article

The synthesis and electrocatalytic properties of amorphous first- and third-row transition metal sulfides (a-TMS) for green hydrogen generation have been comprehensively reviewed. These electrocatalysts can be prepared by several solution processes, including chemical bath deposition, electrodeposition, sol–gel, hydrothermal reaction and thermolysis. The deposition method strongly influences the electrochemical properties of the synthesized a-TMS electrocatalyst. Based on overpotential at 10 mA/cm², the electrocatalytic activity of mono-metallic a-TMS for hydrogen evolution is ranked as follows: a-NiS_x > a-CuS_x > a-CoS_x > a-WS_x > a-FeS_x. The best performing a-NiS_x prepared by chemical bath deposition has an overpotential at 10 mA/cm² of 53 mV and Tafel slope of 68 mV/dec in 1 M KOH electrolyte. The integration of Ni into the a-TMS network structure is crucial to achieving high activity in multi-metallic a-TMS electrocatalyst, as demonstrated by the bifunctional (NiFe)S_x/NiFe(OH)_y nanocomposite catalyst. The critical role of Ni in a-TMS catalyst design can be attributed to the lower free energy change for hydrogen adsorption on Ni. Finally, the emerging catalyst design strategy of amorphous–crystalline heterostructures with a three-dimensional morphology will be discussed together with the need to identify hydrogen adsorption sites on a-TMS electrocatalysts in future. Full article

As hydrogen emerges as a key vector in the shift toward cleaner energy systems, the evaluation of storage technologies becomes essential to support its integration across diverse applications. This work provides a comparative analysis of four hydrogen storage methods, compressed gas, metal hydrides, metal–organic frameworks (MOFs), and carbon-based materials, using a structured multi-criteria decision-making (MCDM) approach, specifically the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The evaluation is based on a comprehensive set of technical, economic, and environmental criteria, including safety, storage capacity, efficiency, cycle durability, technological maturity, environmental impact, cost, and scalability. The analysis adopts a technology-oriented perspective, focusing on the intrinsic performance and feasibility of hydrogen storage systems rather than on a detailed techno-economic optimization. The results show that metal hydrides offer the most balanced performance, driven by high volumetric capacity and solid-phase stability, followed closely by compressed hydrogen, which stands out for its technological maturity and well-established infrastructure, despite facing significant challenges related to safety and space efficiency due to high-pressure storage requirements. Carbon-based materials and MOFs, although promising in specific aspects such as safety, storage density, or material sustainability, are hindered by technological immaturity and operational limitations. Full article

Simultaneous catalytic elimination of nitrogen oxides (NO_x) and volatile organic compounds (VOCs) represents a promising technology for addressing the synergistic pollution of fine particulate matters of <2.5 μm diameter (PM_2.5) and O₃. Nevertheless, it has been maintaining significant challenges in practical implementation, particularly the inherent mismatch in temperature windows between NO_x reduction and VOCs oxidation pathways, coupled with catalyst poisoning and deactivation phenomena. These limitations have hindered the industrial application of bifunctional catalysts for the removal of concurrent pollutant. This review systematically explored the fundamental mechanisms and functional roles of active sites in controlling synchronous catalytic processes. The mechanism of catalyst deactivation caused by multiple toxic substances has been comprehensively analyzed, including sulfur dioxide (SO₂), water vapor (H₂O), chlorine-containing species (Cl*), reaction by-products, and heavy metal contaminants. Furthermore, we critically evaluated the strategies of doping regulation, nanostructure engineering and morphology optimization to enhance the performance and toxicity resistance of catalysts. Meanwhile, emerging regeneration techniques and reactor design optimizations are discussed as potential solutions to improve the durability of catalysts. Based on the above critical aspects, this review aims to provide insights and guidelines for developing robust catalytic systems capable of controlling multi-pollutants in practical applications, and to offer theoretical guidance and technical solutions to bridge the gap between laboratory research and industrial environmental governance applications. Full article

A novel fluorescent probe (PYB) for selective and sensitive detection of Cu²⁺ ions was rationally designed and synthesized via a multi-step organic reaction using pyrene as the fluorophore and salicylaldehyde-diethylenetriamine Schiff base as the recognition moiety. The structural characterization of PYB was confirmed by ¹H NMR, ¹³C NMR, and high-resolution mass spectrometry (HRMS). Photophysical properties investigation revealed that the probe exhibited strong fluorescence emission at 362 nm in DMF/HEPES-NaOH buffer solution (v:v = 1:1, pH 7.4), which underwent a significant fluorescence quenching response (quenching efficiency up to 77%) upon the addition of Cu²⁺, attributed to the formation of a 1:1 PYB-Cu²⁺ complex (binding constant K = 799.65 M⁻¹). The probe showed excellent selectivity for Cu²⁺ over other common metal ions (Ba²⁺, Na⁺, Mg²⁺, Zn²⁺, Cd²⁺, Ca²⁺, Mn²⁺, Pb²⁺, Hg²⁺, Fe³⁺, Co²⁺), with a low detection limit of 8.35 × 10⁻⁷ M, which is well below the maximum allowable concentration of Cu²⁺ in drinking water specified by the World Health Organization (WHO). Furthermore, a portable fluorescent test strip based on PYB was successfully fabricated, enabling rapid and visual detection of Cu²⁺ under UV light. Fluorescence imaging experiments in living HepG2 cells demonstrated that PYB could penetrate cell membranes efficiently and realize the intracellular detection of exogenous Cu²⁺. These results collectively indicate that PYB holds great potential as a practical tool for Cu²⁺ detection in environmental monitoring, food safety, and biological systems. Full article

In underground mining environments, 4D mmWave radar performance is severely constrained by ghost noise issues resulting from multipath reflections, metal structure interference, and complex terrain, creating significant challenges for target detection, mapping, and autonomous navigation tasks. Existing research lacks efficient automated methods and technical workflows for ghost point labeling in these scenarios. This paper presents a LiDAR-assisted two-stage ghost noise automatic labeling method. The technical workflow first achieves precise mapping between radar and LiDAR point clouds through multi-sensor spatiotemporal alignment (time synchronization and spatial registration) and then labels ghost points using a two-stage strategy that combines distance threshold filtering with density-based clustering analysis (DBSCAN). Experiments covering three typical underground mining scenarios (straight tunnels, straight tunnels with side tunnels, and cross-tunnel turns) demonstrate that the proposed method significantly outperforms single distance threshold or clustering methods in terms of precision (95.15%, 98.81%, and 98.85%, respectively), recall (97.44%, 94.68%, and 98.03%, respectively, slightly lower than distance threshold methods in straight tunnels and cross-tunnel turns), and F1 Score (95.48%, 96.70%, and 98.01%, respectively). The method exhibits efficient ghost noise detection capability and robustness in underground mining environments, providing a practical solution for optimizing radar data quality in complex confined scenarios, with potential for application in similar industrial settings. Full article

This study aimed to explore innovative sorbent materials for the remediation of contaminated marine environments, with a focus on metal removal from seawater. Adsorption tests were carried out to evaluate the performance of single-cell proteins (SCPs), a protein-rich biomass derived from industrial by-products, in comparison with commercial activated carbon (AC). Given the increasing need for sustainable and effective approaches in sediment remediation and water treatment, identifying alternatives to conventional sorbents is of particular relevance. Results showed that SCPs exhibited higher affinity for Cr than for Zn, while multi-metal solutions improved adsorption, suggesting synergistic interactions possibly linked to surface charge effects and ternary complex formation. Importantly, SCPs demonstrated competitive and, in some cases, superior performance compared to AC, highlighting their potential as an innovative and sustainable material. Moreover, when the absorbent materials were combined, SCP and AC mixes outperformed both the individual adsorbents and the expected additive efficiencies, achieving significantly higher removal yields for both metals, particularly at low concentrations. Overall, these findings suggest that SCPs, alone or in combination with AC, represent a promising strategy for the removal of heavy metals from marine systems, offering new opportunities for the treatment of contaminated sediments and seawater. Full article

The aim of the current study is to optimize the bead geometries of 80B2, namely, the bead height (BH) and bead width (BW), utilizing a mild steel substrate and a wire-arc additive manufacturing (WAAM) technique based on gas metal arc welding (GMAW). Single-layer depositions with different wire feed speed (WFS), voltage (V), and travel speed (TS) were accomplished by applying the Box–Behnken design methodology. Multivariable nonlinear regression models were developed and validated through ANOVA, revealing WFS as the most significant parameter influencing both BW and BH. The minimal influence of the error factor on each response proved the accuracy of the ANOVA findings. The favorable assessment of residual plots confirmed the appropriateness and reliability of the developed regression equations and ANOVA results. A metaheuristic Passing Vehicle Search (PVS) algorithm was applied for single-objective and multi-objective optimization, yielding a minimum BW of 5.874 mm and a maximum BH of 14.153 mm. Main effect and residual plots confirmed the accuracy and reliability of the predictive models. The parametric settings of WFS: 18 mm/min, TS: 7 mm/s, V: 19 V were obtained for simultaneous optimization of BW with 7.78 mm and BH with 10.87 mm. Pareto points were also generated, which provide non-dominated unique solutions. The study emphasizes the critical role of precise process parameter control in improving WAAM build quality and offers a robust framework for optimizing bead morphology, ultimately enhancing the efficiency and applicability of WAAM for structural component fabrication. These optimized parameters will be used in the future to manufacture a thin-walled, multi-layered structure. Full article

Bio-gels are a class of functional polymeric materials with three-dimensional network structures. Their exceptional biocompatibility, biodegradability, high specific surface area, and tunable physicochemical properties make them highly promising for environmental remediation. This article systematically reviews the classification of bio-gels based on source, cross-linking mechanisms, and functional attributes. It also elaborates on their fundamental properties such as porous structure, high water absorbency, stimuli-responsiveness, and mechanical stability and examines how these properties influence their environmental remediation efficiency. This review comprehensively analyze the mechanisms and efficacy of bio-gels in adsorbing heavy metal ions, removing organic dyes, improving soil water retention, and restoring ecosystems. Special attention is given to the interactions between surface functional groups and contaminants, the role of porous structures in mass transfer, and the ecological effects within soil–plant systems. Additionally, this review explores extended applications of bio-gels in medical tissue engineering, controlled release of drugs and fertilizers, and enhanced oil recovery, highlighting their versatility as multifunctional materials. Finally, based on current progress and challenges, this review outline key future research directions. These include elucidating microscopic interaction mechanisms, developing low-cost renewable feedstocks, designing multi-stimuli-responsive structures, improving long-term stability, and establishing full life-cycle environmental safety assessments. These efforts will help advance the efficient, precise, and sustainable use of bio-gels in environmental remediation, offering innovative solutions to complex environmental problems. Full article

Gas Detection Systems (GDSs) are critical safety technologies deployed in semiconductor wafer fabrication facilities to monitor the presence of hazardous gases. A GDS receives input from gas detectors equipped with consumable gas sensors, such as electrochemical (EC) and metal oxide semiconductor (MOS) types, which are used to detect toxic, flammable, or reactive gases. However, over time, sensors degradations, accuracy drift, and cross-sensitivity to interference gases compromise their intended performance. To maintain sensor accuracy and reliability, routine manual calibration is required—an approach that is resource-intensive, time-consuming, and prone to human error, especially in facilities with extensive networks of gas detectors. This systematic review (PROSPERO on 11th October 2025 Registration number: 1166004) explored minimizing or eliminating the dependency on manual calibration. Findings indicate that using properly calibrated gas sensor data can support advanced data analytics and machine learning algorithms to correct accuracy drift and improve gas selectivity. Techniques such as Principal Component Analysis (PCA), Support Vector Machines (SVMs), multivariate regression, and calibration transfer have been effectively applied to differentiate target gases from interferences and compensate for sensor aging and environmental variability. The paper also explores the emerging potential for integrating calibration-free or self-correcting gas sensor systems into existing GDS infrastructures. Despite significant progress, key research challenges persist. These include understanding the dynamics of sensor response drift due to prolonged gas exposure, synchronizing multi-sensor data collection to minimize time-related drift, and aligning ambient sensor signals with gas analytical references. Future research should prioritize the development of application-specific datasets, adaptive environmental compensation models, and hybrid validation frameworks. These advancements will contribute to the realization of intelligent, autonomous, and data-driven gas detection solutions that are robust, scalable, and well-suited to the operational complexities of modern industrial environments. Full article

The development of highly selective, sensitive and recyclable chemosensors for CN⁻ is critical due to the widespread use of cyanide derivatives in industrial processes and its extreme toxicity to environmental and biological systems. Herein, we report the synthesis and characterization of a water-soluble arene ruthenium metalla-assembly specifically designed to operate in aqueous solutions and under environmentally relevant conditions. The arene ruthenium assembly incorporates functionalized building blocks that enable a selective multi-site recognition of cyanide according to pH by either nucleophilic addition or hydrogen bond interactions. The system exhibits a distinct colorimetric response upon cyanide binding, resulting in a rapid “turn-on” color change. An excellent selectivity and reversibility for cyanide recognition is observed over multiple cycles, with a detection limit in the low micromolar range, thus laying the ground for the future development of sensing technology with supramolecular metal-based assemblies. Full article

This study systematically investigates the effect of substituting Copper (Cu) with Nickel (Ni) on the glass-forming ability (GFA) and corrosion resistance of a Pt-based bulk metallic glass (BMG). We demonstrate that a minor substitution of 5 at.% Ni for Cu in the Pt₄₀Pd₂₀Cu₂₀P₂₀ base alloy significantly enhances both properties. The GFA is markedly improved, as evidenced by the supercooled liquid region (ΔTx) widening from 68 K to 91 K. The optimized Pt₄₀Pd₂₀Cu₁₅Ni₅P₂₀ alloy exhibits a compressive fracture strength of 1.38 GPa. Electrochemical tests in a 3.5 wt.% NaCl solution reveal a substantial improvement in corrosion resistance. Compared to the Ni-free baseline alloy, the passive film resistance (R_f) and charge-transfer resistance (R_ct) of the Ni-containing alloy are enhanced by factors of 2.75 and 2.60, respectively. This superior performance is attributed to a synergistic effect wherein Ni alloying both stabilizes the amorphous structure and promotes the formation of a more robust passive film. This work presents a viable strategy for designing cost-effective, high-performance multi-component BMGs for applications in aggressive chloride environments. Full article