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Keywords = defect engineering

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21 pages, 22656 KB  
Article
Development of a Laser Cladding Technology for Repairing First-Stage High-Pressure Turbine Blades in Gas Turbine Engines
by Stepan Tukov, Rudolf Korsmik, Grigoriy Zadykyan, Dmitrii Mukin, Ruslan Mendagaliev and Nikita Roschin
Metals 2025, 15(9), 957; https://doi.org/10.3390/met15090957 - 28 Aug 2025
Abstract
A gas turbine engine is a technological system consisting of a compressor, a combustion chamber, and other modules. All these components are subjected to dynamic and cyclic loads, which lead to fatigue cracks and mechanical damage. The aim of this work is to [...] Read more.
A gas turbine engine is a technological system consisting of a compressor, a combustion chamber, and other modules. All these components are subjected to dynamic and cyclic loads, which lead to fatigue cracks and mechanical damage. The aim of this work is to repair the worn surfaces of a series of DR-59L high-pressure turbine blades by laser powder cladding. A number of technological parameters of laser cladding were tested to obtain a defect-free structure on the witness sample. The metal powder of the cobalt alloy Stellite 21 was used as a filler material. By modeling the process of restoring rotor blades, the operating mode of laser powder cladding was determined. No defects were detected during capillary control of the restored surfaces of the rotor blades. The results of the uniaxial tension test of the restored rotor blades showed increased tensile strength and elongation. With the use of laser powder cladding technology, it was possible to restore the worn surfaces of a series of rotor blades of the DR-59L high-pressure turbine, thereby increasing the life cycle of power plant products. Full article
(This article belongs to the Section Additive Manufacturing)
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23 pages, 4352 KB  
Article
Quantifying Inter-Ply Friction and Clamping Effects via an Experimental–Numerical Framework: Advancing Non-Coherent Deformation Control of Uncured Metal–Fiber-Reinforced Polymer Laminates
by Yunlong Chen and Shichen Liu
Polymers 2025, 17(17), 2330; https://doi.org/10.3390/polym17172330 - 28 Aug 2025
Abstract
Pre-stacked uncured metal–fiber-reinforced polymer (FRP) laminates, which are critical for aerospace components like double-curved fuselage panels, wing ribs, and engine nacelles, exhibit better deformation behavior than their fully cured counterparts. However, accurate process simulation requires precise material characterization and process optimization to achieve [...] Read more.
Pre-stacked uncured metal–fiber-reinforced polymer (FRP) laminates, which are critical for aerospace components like double-curved fuselage panels, wing ribs, and engine nacelles, exhibit better deformation behavior than their fully cured counterparts. However, accurate process simulation requires precise material characterization and process optimization to achieve a defect-free structural design. This study focuses on two core material behaviors of uncured laminates—inter-ply friction at metal–prepreg interfaces and out-of-plane bending—and optimizes process parameters for their non-coherent deformation. Experimental tests included double-lap sliding tests (to quantify inter-ply friction) and clamped-beam bending tests (to characterize out-of-plane bending); a double-curved dome part was designed to assess the effects of the material constituent, fiber orientation, inter-ply friction, and clamping force, with validation via finite element modeling (FEM) in Abaqus software. The results indicate that the static–kinetic friction model effectively predicts inter-ply friction behavior, with numerical friction coefficient–displacement trends closely matching experimental data. Additionally, the flexural bending model showed that greater plastic deformation in metal layers increased bending force while reducing post-unloading spring-back depth. Furthermore, for non-coherent deformation, higher clamping forces improve FRP prepreg deformation and mitigate buckling, but excessive plastic deformation raises metal cracking risk. This work helps establish a combined experimental–numerical framework for the defect prediction and process optimization of complex lightweight components, which address the core needs of modern aerospace manufacturing. Full article
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42 pages, 5613 KB  
Article
YOLOv11-EMD: An Enhanced Object Detection Algorithm Assisted by Multi-Stage Transfer Learning for Industrial Steel Surface Defect Detection
by Weipeng Shi, Junlin Dai, Changhe Li and Na Niu
Mathematics 2025, 13(17), 2769; https://doi.org/10.3390/math13172769 - 28 Aug 2025
Abstract
To address the issues of inaccurate positioning, weak feature extraction capability, and poor cross-domain adaptability in the detection of surface defects of steel materials, this paper proposes an improved YOLOv11-EMD algorithm and integrates a multi-stage transfer learning framework to achieve high-precision, robust, and [...] Read more.
To address the issues of inaccurate positioning, weak feature extraction capability, and poor cross-domain adaptability in the detection of surface defects of steel materials, this paper proposes an improved YOLOv11-EMD algorithm and integrates a multi-stage transfer learning framework to achieve high-precision, robust, and low-cost industrial defect detection. Specifically, the InnerEIoU loss function is introduced to improve the accuracy of bounding box regression, the multi-scale dilated attention (MSDA) module is integrated to enhance the multi-scale feature fusion capability, and the Cross-Stage Partial Network with 3 Convolutions and Kernel size 2 Dynamic Convolution (C3k2_DynamicConv) module is embedded to improve the expression of and adaptability to complex defects. To address the problem of performance degradation when the model migrates between different data domains, a multi-stage transfer learning framework is constructed, combining source domain pre-training and target domain fine-tuning strategies to improve the model’s generalization ability in scenarios with changing data distributions. On the comprehensive dataset constructed of NEU-DET and Severstal steel defect images, YOLOv11-EMD achieved a precision of 0.942, a recall of 0.868, and an mAP@50 of 0.949, which are 3.5%, 0.8%, and 1.6% higher than the original model, respectively. On the cross-scenario mixed dataset composed of NEU-DET and GC10-DET data, the mAP@50 was 0.799, outperforming mainstream detection algorithms. The multi-stage transfer strategy can shorten the training time by 3.2% and increase the mAP by 8.8% while maintaining accuracy. The proposed method improves the defect detection accuracy, has good generalization and engineering application potential, and is suitable for automated quality inspection tasks in diverse industrial scenarios. Full article
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20 pages, 4501 KB  
Article
Performance Study of Biomass Carbon-Based Materials in Electrocatalytic Fenton Degradation of Printing and Dyeing Wastewater
by Lie Wen, Yan An and Yanhua Lei
Catalysts 2025, 15(9), 818; https://doi.org/10.3390/catal15090818 - 28 Aug 2025
Abstract
Biomass carbon materials exhibit a significant specific surface area, carbon defects, and oxygen-containing functional groups during the electrochemical cathodic oxygen reduction (ORR) process, resulting in an enhanced adsorption–desorption of reaction intermediates (e.g., *OH and *OOH) by the catalyst. In this study, a cost-effective [...] Read more.
Biomass carbon materials exhibit a significant specific surface area, carbon defects, and oxygen-containing functional groups during the electrochemical cathodic oxygen reduction (ORR) process, resulting in an enhanced adsorption–desorption of reaction intermediates (e.g., *OH and *OOH) by the catalyst. In this study, a cost-effective biomass-derived carbon material (HBC-500) was prepared through low-temperature pyrolysis at 500 °C using Spirulina as a precursor for H2O2 production. By employing surface engineering modification of the carbon-based material to promote the ORR process’s two-electron selectivity, HBC-500 demonstrated consistent experimental results with the RRDE findings at pH = 5, yielding 238.40 mg·L−1 of hydrogen peroxide within a 90 min duration at a current density of 50 mA·cm−2. Furthermore, HBC-500 accomplished over 95% degradation within 30 min at pH = 5 and maintained approximately 91.79% electrocatalytic activity after undergoing five consecutive electrocatalytic cycles lasting 300 min. These results establish HBC-500 biomass carbon material as a highly suitable candidate for H2O2 production and Fenton degradation of organic wastewater. Full article
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12 pages, 2172 KB  
Article
Instance Segmentation Method for Insulators in Complex Backgrounds Based on Improved SOLOv2
by Ze Chen, Yangpeng Ji, Xiaodong Du, Shaokang Zhao, Zhenfei Huo and Xia Fang
Sensors 2025, 25(17), 5318; https://doi.org/10.3390/s25175318 - 27 Aug 2025
Abstract
To precisely delineate the contours of insulators in complex transmission line images obtained from Unmanned Aerial Vehicle (UAV) inspections and thereby facilitate subsequent defect analysis, this study proposes an instance segmentation framework predicated upon an enhanced SOLOv2 model. The proposed framework integrates a [...] Read more.
To precisely delineate the contours of insulators in complex transmission line images obtained from Unmanned Aerial Vehicle (UAV) inspections and thereby facilitate subsequent defect analysis, this study proposes an instance segmentation framework predicated upon an enhanced SOLOv2 model. The proposed framework integrates a preprocessed edge channel, generated through the Non-Subsampled Contourlet Transform (NSCT), which augments the model’s capability to accurately capture the edges of insulators. Moreover, the input image resolution to the network is heightened to 1200 × 1600, permitting more detailed extraction of edges. Rather than the original ResNet + FPN architecture, the improved HRNet is utilized as the backbone to effectively harness multi-scale feature information, thereby enhancing the model’s overall efficacy. In response to the increased input size, there is a reduction in the network’s channel count, concurrent with an increase in the number of layers, ensuring an adequate receptive field without substantially escalating network parameters. Additionally, a Convolutional Block Attention Module (CBAM) is incorporated to refine mask quality and augment object detection precision. Furthermore, to bolster the model’s robustness and minimize annotation demands, a virtual dataset is crafted utilizing the fourth-generation Unreal Engine (UE4). Empirical results reveal that the proposed framework exhibits superior performance, with AP0.50 (90.21%), AP0.75 (83.34%), and AP[0.50:0.95] (67.26%) on a test set consisting of images supplied by the power grid. This framework surpasses existing methodologies and contributes significantly to the advancement of intelligent transmission line inspection. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Intelligent Fault Diagnostics)
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25 pages, 2872 KB  
Article
Vibration Analysis for Diagnosis of Diesel Engines with Air Flow Sensor Failure
by Ali Helali, Ines Belkacem, Jamila Abdellaoui and Achraf Zegnani
Technologies 2025, 13(9), 380; https://doi.org/10.3390/technologies13090380 - 27 Aug 2025
Abstract
Carrying out automobile stability and dynamic comfort involves a close examination of engine performance, such that fault detection at the early stage must be of the highest priority to reliability and effectiveness. The study evaluates the impact of malfunctions in mass air flow [...] Read more.
Carrying out automobile stability and dynamic comfort involves a close examination of engine performance, such that fault detection at the early stage must be of the highest priority to reliability and effectiveness. The study evaluates the impact of malfunctions in mass air flow (MAF) sensors on diesel engine performance and stability, particularly on vibratory emissions. Employing experimental methods, defect and normal engine vibrations were analyzed in both time-domain and frequency spectral domain methodologies. Some statistical values, such as root mean square (RMS), kurtosis, mean, standard deviation, clearance factor, and shape factor, were employed to compare and characterize the vibration pattern. The results indicate that malfunctions in the MAF sensor are characterized by striking vibration amplitude enhancement and instability at high engine revolutions. These defects cause poor starting, misfire, and rough engine running, which affect combustion efficiency. Conclusions show excellent correlation among MAF sensor fault, combustion activity, and engine vibration, and this confirms the need for fault detection at the initial stage. With its enhancement in vibration analysis diagnostic capability, this contribution is significant to condition monitoring and predictive maintenance activities. Lastly, the study contributes to improving engine reliability, efficiency in operation, and performance overall in the automotive industry. Full article
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28 pages, 20856 KB  
Article
Influence of Porosity on the Morpho-Structure, Physical-Chemical and Biochemical Characteristics of Polylactic Acid and/or Polycaprolactone Scaffolds
by Anca Peter, Manuel Brendon Monea, Anca Mihaly Cozmuta, Camelia Nicula, Leonard Mihaly Cozmuta, Zorica Vosgan, Zsolt Szakacs, Goran Drazic, Klara Magyari, Marieta Muresan-Pop and Lucian Baia
Polymers 2025, 17(17), 2311; https://doi.org/10.3390/polym17172311 - 26 Aug 2025
Abstract
The design and development of scaffolds play a crucial role in tissue engineering. In this regard, the study aims to establish the influence of porosity on the morpho-structural, physical–chemical, and biochemical characteristics of the polylactic acid (PLA) and/or polycaprolactone (PCL) scaffolds, in order [...] Read more.
The design and development of scaffolds play a crucial role in tissue engineering. In this regard, the study aims to establish the influence of porosity on the morpho-structural, physical–chemical, and biochemical characteristics of the polylactic acid (PLA) and/or polycaprolactone (PCL) scaffolds, in order to be considered candidates for tissue reconstruction. The results indicated that binary PLA-PCL and PCL matrices are more suitable than PLA, due to their higher crystallization degree, this contributing to the superior mechanical properties and lower network defects. The preponderance of molecular interactions decreases with porosity. Porosity induced a decrease in the degree of crystallization of PLA-PCL and an increase in water, glucose and blood components uptake by 188, 178, and 28%, respectively. The PLA-PCL scaffold was found to be more stable to lipase action than neat PLA as a result of the reduced enzyme access due to the higher crystallinity and thermodynamic stability of the hydrocarbon linear chain in PCL, which is higher than that of the side methyl group in PLA. Lactobacillus growth increases with porosity and was more pronounced on the PLA-PCL matrix. All these results show that varying the porosity and composition of the polymer mixture leads to valuable materials with nutrient absorption capacity and biodegradability superior to neat PLA or PCL materials. Full article
(This article belongs to the Section Polymer Applications)
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16 pages, 2240 KB  
Article
Defect-Engineered MnO2@Ni Foam Electrode for Zinc-Ion Batteries Toward Mobile Robotics Applications
by Shilin Li, Dong Xie, Taoyun Zhou, Qiaomei Zhao, Muzhou Liu and Xinyu Li
Nanomaterials 2025, 15(17), 1312; https://doi.org/10.3390/nano15171312 - 26 Aug 2025
Abstract
Aqueous zinc-ion batteries (AZIBs) have gained significant attention as promising candidates for next-generation energy storage systems, especially in mobile robotics, due to their inherent safety, environmental friendliness, and low cost. However, the practical application of AZIBs is often hindered by slow Zn2+ [...] Read more.
Aqueous zinc-ion batteries (AZIBs) have gained significant attention as promising candidates for next-generation energy storage systems, especially in mobile robotics, due to their inherent safety, environmental friendliness, and low cost. However, the practical application of AZIBs is often hindered by slow Zn2+ diffusion and the poor structural stability of the cathode materials under high-rate or long-term operation. To address these challenges, a defect-engineered, binder-free MnO2 electrode, with a MnO2 loading of 1.35 mg·cm−2, is synthesized via in situ hydrothermal growth of ultrathin MnO2 nanosheets directly on a 3D conductive nickel foam scaffold, followed by reductive annealing to introduce abundant oxygen vacancies. These oxygen-rich defect sites significantly enhance Zn2+ adsorption, improve charge transfer kinetics, and contribute to enhanced pseudocapacitive behavior, further improving overall electrochemical performance. The intimate contact between the MnO2 and Ni substrate ensures efficient electron transport and robust structural integrity during repeated cycling. With this synergistic architecture, the MnO2@Ni electrode achieves a high specific capacity of 122.9 mAh·g−1 at 1 A·g−1, demonstrating excellent cycling durability with 94.24% capacity retention after 800 cycles and nearly 99% coulombic efficiency. This study offers a scalable strategy for designing high-performance, structurally stable Zn-ion battery cathodes with improved rate capability, making it a promising candidate for energy-intensive mobile robotic and flexible electronic systems. Full article
(This article belongs to the Special Issue Novel Electrode Materials for Solid-State Batteries)
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27 pages, 913 KB  
Article
Criticality Assessment of Wind Turbine Defects via Multispectral UAV Fusion and Fuzzy Logic
by Pavlo Radiuk, Bohdan Rusyn, Oleksandr Melnychenko, Tomasz Perzynski, Anatoliy Sachenko, Serhii Svystun and Oleg Savenko
Energies 2025, 18(17), 4523; https://doi.org/10.3390/en18174523 - 26 Aug 2025
Abstract
Ensuring the structural integrity of wind turbines is crucial for the sustainability of wind energy. A significant challenge remains in transitioning from mere defect detection to objective, scalable criticality assessment for prioritizing maintenance. In this work, we propose a novel comprehensive framework that [...] Read more.
Ensuring the structural integrity of wind turbines is crucial for the sustainability of wind energy. A significant challenge remains in transitioning from mere defect detection to objective, scalable criticality assessment for prioritizing maintenance. In this work, we propose a novel comprehensive framework that leverages multispectral unmanned aerial vehicle (UAV) imagery and a novel standards-aligned Fuzzy Inference System to automate this task. Our contribution is validated on two open research-oriented datasets representing small on- and offshore machines: the public AQUADA-GO and Thermal WTB Inspection datasets. An ensemble of YOLOv8n models trained on fused RGB-thermal data achieves a mean Average Precision (mAP@.5) of 92.8% for detecting cracks, erosion, and thermal anomalies. The core novelty, a 27-rule Fuzzy Inference System derived from the IEC 61400-5 standard, translates quantitative defect parameters into a five-level criticality score. The system’s output demonstrates exceptional fidelity to expert assessments, achieving a mean absolute error of 0.14 and a Pearson correlation of 0.97. This work provides a transparent, repeatable, and engineering-grounded proof of concept, demonstrating a promising pathway toward predictive, condition-based maintenance strategies and supporting the economic viability of wind energy. Full article
(This article belongs to the Special Issue Optimal Control of Wind and Wave Energy Converters)
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28 pages, 5678 KB  
Article
Enhanced YOLOv8 with DWR-DRB and SPD-Conv for Mechanical Wear Fault Diagnosis in Aero-Engines
by Qifan Zhou, Bosong Chai, Chenchao Tang, Yingqing Guo, Kun Wang, Xuan Nie and Yun Ye
Sensors 2025, 25(17), 5294; https://doi.org/10.3390/s25175294 - 26 Aug 2025
Viewed by 7
Abstract
Aero-engines, as complex systems integrating numerous rotating components and accessory equipment, operate under harsh and demanding conditions. Prolonged use often leads to frequent mechanical wear and surface defects on accessory parts, which significantly compromise the engine’s normal and stable performance. Therefore, accurately and [...] Read more.
Aero-engines, as complex systems integrating numerous rotating components and accessory equipment, operate under harsh and demanding conditions. Prolonged use often leads to frequent mechanical wear and surface defects on accessory parts, which significantly compromise the engine’s normal and stable performance. Therefore, accurately and rigorously identifying failure modes is of critical importance. In this study, failure modes are categorized into notches, scuffs, and scratches based on original bearing structure images. The YOLOv8 architecture is adopted as the base framework, and a Dilated Reparameterization Block (DRB) is introduced to enhance the Dilation-Wise Residual (DWR) module. This structure uses a large convolutional kernel to capture fragmented and sparse features in wear images, ensuring a wide receptive field. The concept of structural reparameterization is incorporated into DWR to improve its ability to capture detailed target information. Additionally, the standard convolutional layer in the head of the improved DWR-DRB structure is replaced by Spatial-Depth Convolution (SPD-Conv) to reduce the loss of wear morphology and enhance the accuracy of fault feature extraction. Finally, a fusion structure combining Focaler and MPDIoU is integrated into the loss function to leverage their strengths in handling imbalanced classification and bounding box geometric regression. The proposed method achieves effective recognition and diagnosis of mechanical wear fault patterns. The experimental results demonstrate that, compared to the baseline YOLOv8, the proposed method improves the mAP50 for fault diagnosis and recognition from 85.4% to 91%. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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16 pages, 1053 KB  
Review
Hydrogels in Peri-Implant Regeneration: Strategies for Modulating Tissue Healing
by Paula Buzo Frigério, Nathália Dantas Duarte, Mateus Meister Koury, Felipe de Souza Duarte, Roberta Okamoto, Daniela Vieira Buchaim, Carlos Henrique Bertoni Reis, William Saranholi da Silva, Lívia Maluf Menegazzo Bueno, Marcio Cristino Raphael, Rogerio Leone Buchaim and João Paulo Mardegan Issa
Pharmaceutics 2025, 17(9), 1105; https://doi.org/10.3390/pharmaceutics17091105 - 25 Aug 2025
Viewed by 144
Abstract
Background/Objectives: Hydrogels have emerged as strategic biomaterials in bone tissue engineering, especially in the peri-implant context, due to their high biocompatibility, water retention capacity, three-dimensional defect filling, and ability to mimic the extracellular matrix. These properties allow physical support for regeneration and [...] Read more.
Background/Objectives: Hydrogels have emerged as strategic biomaterials in bone tissue engineering, especially in the peri-implant context, due to their high biocompatibility, water retention capacity, three-dimensional defect filling, and ability to mimic the extracellular matrix. These properties allow physical support for regeneration and the incorporation and controlled release of bioactive, immunomodulatory, and osteoinductive agents. Methods: This narrative review aimed to summarize recent advances in developing and applying hydrogels for the repair of peri-implant bone defects. The selection of studies was performed in PubMed, Web of Science, and EBSCO databases, covering the period from 2010 to 2025. Thus, 14 preclinical and clinical studies were included in this review. Results and Conclusions: Hydrogels show great potential for peri-implant bone regeneration due to their biocompatibility and ability to deliver bioactive agents. While preclinical results are promising, clinical validation remains limited. Further studies are needed to confirm their efficacy and ensure the safe translation of these findings into clinical practice. Full article
(This article belongs to the Special Issue Prospects of Hydrogels in Wound Healing)
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21 pages, 7107 KB  
Article
Study on Mesoscopic Evolution Mechanism and Influencing Factors of Concrete Blasting Damage Based on PFC
by Xueying Hu, Shuyang Yu, Yifei Li, Yihan Tang, Ying Sun and Pingping Gu
Buildings 2025, 15(17), 3000; https://doi.org/10.3390/buildings15173000 - 23 Aug 2025
Viewed by 223
Abstract
In urban construction, the efficient demolition of concrete structures imposes high-precision requirements on blasting technology. The mesoscopic evolution mechanism of concrete blasting damage is the key to optimizing blasting parameters. In this study, a numerical model of concrete blasting is established using Particle [...] Read more.
In urban construction, the efficient demolition of concrete structures imposes high-precision requirements on blasting technology. The mesoscopic evolution mechanism of concrete blasting damage is the key to optimizing blasting parameters. In this study, a numerical model of concrete blasting is established using Particle Flow Code (PFC). By comparing it with an experimental model containing a blast hole and a horizontal single fissure, the rationality and reliability of the model in simulating blasting damage evolution are verified. On this basis, four groups of control variable schemes are designed (concrete particle size distribution, aggregate content, prefabricated fissure inclination angle, and fissure length) to systematically explore the effects of mesoscopic structures and macroscopic defects on blasting damage. The results show that larger concrete particles make it easier for damage cracks to avoid large particles, forming sparse and irregular crack networks. A higher aggregate content enhances the “obstruction-guidance” effect of aggregate distribution on damage. When the aggregate content is 40%, the vertical damage expansion is the most prominent, reaching up to 3.05 cm. Fissure inclination angle affects the damage direction by guiding the propagation path of stress waves. Fissures inclined at 30°~60° serve as preferential damage channels, while 90° vertical fissures make vertical damage more significant. An increased fissure length expands the damage range, and the damage degree is the highest for a 40 mm long fissure, being 1.29 times that of a 30 mm fissure. The research results reveal the mesoscopic evolution laws of concrete blasting damage, providing a theoretical basis for the optimization of engineering blasting parameters and safety control. Full article
(This article belongs to the Section Building Structures)
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25 pages, 11036 KB  
Article
Fatigue Performance Analysis of Weathering Steel Bridge Decks Under Residual Stress Conditions
by Wenye Tian, Ran Li, Tao Lan, Ruixiang Gao, Maobei Li and Qinyuan Liu
Materials 2025, 18(17), 3943; https://doi.org/10.3390/ma18173943 - 22 Aug 2025
Viewed by 532
Abstract
The growing use of weathering steel in bridge engineering has highlighted the increasing impact of fatigue damage caused by the combined effects of welding residual stress and vehicular loading. This study investigates the fatigue performance of Q500qENH weathering steel bridge decks by proposing [...] Read more.
The growing use of weathering steel in bridge engineering has highlighted the increasing impact of fatigue damage caused by the combined effects of welding residual stress and vehicular loading. This study investigates the fatigue performance of Q500qENH weathering steel bridge decks by proposing a coupled analysis method for residual stress and fatigue crack growth, utilizing collaborative simulations with Abaqus 2023 and Franc3D 7.0. An interaction model integrating welding-induced residual stress fields and dynamic vehicular loads is developed to systematically examine crack propagation patterns in critical regions, including the weld toes of the top plate and the weld seams of the U-ribs. The results indicate that the crack propagation rate at the top plate weld toe exhibits the most rapid progression, reaching the critical dimension (two-thirds of plate thickness) at 6.98 million cycles, establishing this location as the most vulnerable failure point. Residual stresses significantly amplify the stress amplitude under tension–compression cyclic loading, with life degradation effects showing 48.9% greater severity compared to pure tensile stress conditions. Furthermore, parametric analysis demonstrates that increasing the top plate thickness to 16 mm effectively retards crack propagation, while wheel load pressures exceeding 1.0 MPa induce nonlinear acceleration of life deterioration. Based on these findings, engineering countermeasures including welding defect control, optimized top plate thickness (≥16 mm), and wheel load pressure limitation (≤1.0 MPa) are proposed, providing theoretical support for fatigue-resistant design and maintenance of weathering steel bridge decks. Full article
(This article belongs to the Section Construction and Building Materials)
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25 pages, 3030 KB  
Review
Lithium Niobate Crystal Preparation, Properties, and Its Application in Electro-Optical Devices
by Yan Zhang, Xuefeng Xiao, Jiayi Chen, Han Zhang, Yan Huang, Jiashun Si, Shuaijie Liang, Qingyan Xu, Huan Zhang, Lingling Ma, Cui Yang and Xuefeng Zhang
Inorganics 2025, 13(9), 278; https://doi.org/10.3390/inorganics13090278 - 22 Aug 2025
Viewed by 164
Abstract
Lithium Niobate (LiNbO3, LN) crystals are multifunctional optical materials with excellent electro-optical, acousto-optical, and nonlinear optical properties, and their broad spectral transparency makes them widely used in electro-optical modulators, tunable filters, and beam deflectors. Near Stoichiometric Lithium Niobate (NSLN) crystals have [...] Read more.
Lithium Niobate (LiNbO3, LN) crystals are multifunctional optical materials with excellent electro-optical, acousto-optical, and nonlinear optical properties, and their broad spectral transparency makes them widely used in electro-optical modulators, tunable filters, and beam deflectors. Near Stoichiometric Lithium Niobate (NSLN) crystals have a lithium to niobium ratio ([Li]/[Nb]) close to 1:1,demonstrate superior performance characteristics compared to composition lithium niobate (Congruent Lithium Niobate (CLN), [Li]/[Nb] = 48.5:51.5) crystals. NSLN crystals have a lower coercive field (~4 kV/mm), higher electro-optic coefficient (γ33 = 38.3 pm/V), and better nonlinear optical properties. This paper systematically reviews the research progress on preparation methods, the physical properties of LN and NSLN crystals, and their applications in devices such as electro-optical modulators, optical micro-ring resonators, and holographic storage. Finally, the future development direction of NSLN crystals in the preparation process (large-size single-crystal growth and defect control) and new electro-optical devices (low voltage deflectors based on domain engineering) is envisioned. Full article
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25 pages, 3791 KB  
Review
A Review of Modification of Carbon-Based Materials Based on Defect Engineering in Capacitive Deionization
by Yubo Zhao, Rupeng Liu, Jinfeng Fang, Feiyong Chen and Silu Huo
Water 2025, 17(16), 2478; https://doi.org/10.3390/w17162478 - 20 Aug 2025
Viewed by 299
Abstract
Capacitive deionization (CDI) is a novel water treatment technology based on the principle of double-electric-layer adsorption, which stores ions in the solution on the surface of electrodes by applying a low potential difference to achieve desalination. CDI has the advantages of low operating [...] Read more.
Capacitive deionization (CDI) is a novel water treatment technology based on the principle of double-electric-layer adsorption, which stores ions in the solution on the surface of electrodes by applying a low potential difference to achieve desalination. CDI has the advantages of low operating voltage (<1.2 V), small equipment footprint, low energy consumption, low cost and environmental friendliness. The performance of CDI is heavily dependent on the electrode materials. Carbon-based materials are widely used in CDI systems because of the large specific surface areas, lower price, and remarkable stability. To improve the CDI performance, extensive research efforts have been made for the modification of carbon-based materials. Defects in carbon-based materials play an important role in electrochemical processes and the introduction of defects is an important method to modify carbon-based materials. However, there is a lack of systematic summary of modification of carbon-based materials through introducing defects in CDI system. Therefore, this study makes the first attempt to review the modification of carbon-based materials of CDI based on defect engineering. The mechanism of enhancing CDI performance of carbon-based materials with the induction of different defects is analyzed and the future research prospects are proposed. Full article
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