Search Results (1,661)

Corrosion damage is an important factor causing structural failure in offshore structures. In order to study the effect of local corrosion on the bearing property of H-shaped steel used in offshore structures, the rig substructure on an offshore platform was taken as the research object. Considering both local damage and the overall structural characteristics of the substructure, the main load-bearing H-shaped steel component was determined through testing. Based on the macroscopic characterization analysis of random pitting corrosion such as morphology, depth, diameter and location, a random pitting damage model was established by combining a regular cylindrical pit model with a MATLAB-based random number generation method. The influence of random pitting position on the bearing property of H-shaped steel is obtained by the simplified beam model and the multi-scale model embedded into the global substructure system respectively. The results show that pitting located at the flange end region produces the most severe stress concentration and results in the greatest degradation in bearing capacity, and corrosion on the upper flange leads to higher equivalent stress levels than that on the lower flange. The research results can provide some reference value for structural safety performance assessment. Full article

In response to corrosion challenges encountered during the gathering and transportation of wet natural gas, this study systematically investigates the corrosion behavior of L245NCS steel in environments containing O₂, H₂S, CO₂ and simulated oilfield-produced water. The research employs a combined approach involving high-pressure autoclave experiments and transparent flow loop simulations. Autoclave tests reproduce gas phase, liquid phase, and gas–liquid interface conditions under a controlled O₂-H₂S-CO₂ mixture, while a visual flow loop equipped with elbows and undulating sections is used to examine liquid accumulation behavior and flow characteristics under dynamic, real-world operating conditions. Results indicate that corrosion is most severe at the gas–liquid interface. H₂S is identified as the primary corrosive agent, exerting a stronger influence than CO₂ or O₂. Liquid accumulation is the main factor leading to non-uniform corrosion distribution, and its formation is influenced by water content, pressure, temperature difference, and pipeline shutdown and restart operations. Critical areas such as low-lying sections, downhill bottoms, and the beginning of uphill sections exhibit localized corrosion rates up to 61.4% higher than areas without liquid accumulation. This integrated methodology bridges mechanistic understanding with engineering practice, providing a basis for corrosion risk assessment, optimal monitoring point placement, and integrity management of wet gas pipelines. Full article

Flow lines in aluminum alloy forgings are not merely post-deformation metallographic features; they are integrated indicators of material transport, microstructural evolution, defect susceptibility, and service performance. This review critically examines the mechanisms controlling flow-line evolution, with emphasis on constitutive flow behavior, dynamic recovery and recrystallization, second-phase redistribution, friction, thermal gradients, and die/preform design. It then evaluates how abnormal flow paths promote key defects, including folding/laps, flow-through discontinuities, vortex-like instability, and exposed flow lines, and distinguishes well-established mechanisms from topics that still rely on indirect evidence. Particular attention is given to the effects of flow-line morphology on anisotropy, notch sensitivity, corrosion-assisted damage, and fatigue life in forged aluminum alloys. Current control strategies, including preform optimization, FE-based backward tracing, multiphysics defect indices, frictional heat management, and isothermal forging, are also assessed. The available literature shows that stable contour-following flow lines are essential for the simultaneous control of defect formation, microstructural homogeneity, and durability, while major research needs remain in in situ validation, quantitative defect criteria, and digitally closed-loop process control. This review is therefore framed as a critical narrative synthesis rather than a formal systematic review; emphasis is placed on forging-centered studies that directly relate flow-path evolution to defect formation, anisotropy, fatigue, and process optimization, while evidence transferred from adjacent processes is treated as mechanistic support rather than equivalent proof. Full article

This study investigates the antioxidant and anticorrosive properties of Mentha pulegium L. essential oil (MP EO) as a sustainable and eco-friendly alternative to synthetic oxidation inhibitors. The antioxidant activity of MP EO was evaluated using the ferric reducing antioxidant power (FRAP) assay, which demonstrated a strong electron-donating capacity and effective reduction of ferric ions, indicating promising antioxidant potential. The anticorrosive performance was assessed on mild steel in 0.5 M H₂SO₄ using potentiodynamic polarization and electrochemical impedance spectroscopy (EIS). The results showed inhibition efficiencies of up to 75.8% at a concentration of 2 g/L. Molecular docking simulations revealed favorable binding interactions between the key oil components (pulegone and menthone) and the ROS-generating enzyme model (PDB ID: 2CDU), providing complementary mechanistic insight into their potential role in oxidative stress modulation. Additionally, quantum chemical calculations highlighted electronic properties favoring adsorption on metallic surfaces. Surface morphology analysis using SEM/EDX confirmed the formation of a protective film on steel in the presence of MP EO. These combined findings position Mentha pulegium essential oil as a potent, biodegradable candidate for both antioxidant applications and corrosion prevention in acidic environments. Full article

The objective of this study is to develop and assess the feasibility of utilizing lotus (Nelumbo nucifera Gaertn.) leaf extract as a green corrosion inhibitor for copper in a sulfuric acid environment. The inhibitory efficacy was comprehensively evaluated using a multi-technique approach, incorporating electrochemical measurements, weight loss analysis, theoretical analysis, and surface morphological characterization. The experimental results demonstrate that the lotus leaf extract functions as an efficient corrosion inhibitor for copper, achieving an inhibition efficiency of 88.07% at 700 mg/L by effectively suppressing both cathodic and anodic corrosion processes. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) confirmed the protective effect, whereas X-ray photoelectron spectroscopy (XPS) and Fourier-transform infrared spectroscopy (FTIR) identified functional groups and surface interaction between metal and inhibitor. Theoretical calculations further confirmed the involvement of nitrogen (N) and oxygen (O) as the key active sites. Adsorption behavior adheres to the Langmuir isotherm model, involving both physical and chemical adsorption processes that inhibit the Cu⁺→Cu²⁺ oxidation reaction. This study demonstrates acid-resistant protection of copper using lotus leaf extract. Full article

Accurate detection and severity estimation of corrosion on metallic surfaces is essential for maintaining material integrity and ensuring operational safety in industrial systems. To address limitations in manual inspection methods, this study presents a two-stage deep learning pipeline tailored for high-resolution scanning electron microscopy images. The framework combines instance-level corrosion segmentation using the YOLOv8-seg architecture with subsequent severity classification performed by EfficientNet-B0 and ResNet18. In the segmentation stage, models are trained using both manually annotated and automatically generated binary masks, enabling robust instance mask prediction through prototype-based mask decoding. The classification stage assesses the severity of corrosion by analyzing localized regions based on morphological features, leveraging convolutional neural networks optimized for binary output. The experimental results demonstrate strong performance: the segmentation model trained on manual annotations achieves a Mean Intersection over Union (mIoU) of 89.91, a mask mAP@50 of 98.6, and an ROC-AUC of 94.69. For severity classification, EfficientNet-B0 achieves an accuracy of 93.75% and an F1-score of 93.29, outperforming ResNet18. The proposed framework connects advanced SEM with state-of-the-art machine learning. It provides a scalable, annotation-efficient way to use intelligent and automated corrosion characterization in materials science and industrial applications. Full article

Accurate prediction of corrosion-fatigue crack growth rate in aluminum alloys is critical for the safety assessment of aerospace structures. Conventional empirical fracture-mechanic models often struggle to capture multiphysics coupling effects, whereas purely data-driven machine-learning models may lack physical interpretability and generalize poorly beyond the training distribution. To address this challenge, this study proposes a physics-guided knowledge-based XGBoost (KBXGB) model. Based on a comprehensive dataset comprising 2786 experimental records, Permutation Feature Importance was utilized to identify 11 key features, including the stress intensity factor range, stress ratio, frequency, and environmental parameters. The KBXGB framework learns the residual between physics-based empirical models (e.g., the Paris and Walker laws) and measured experimental data, recasting the complex nonlinear mapping into a correction of the systematic deviations of the physical models, thereby achieving deep integration of domain knowledge and data-driven learning. Test results demonstrate that the KBXGB model achieves a coefficient of determination (R²) of 0.9545 and a reduced Mean Relative Error (MRE) of 1.61% on the test set, outperforming standard XGBoost and traditional regression models. Crucially, in independent extrapolation validation, the standard XGBoost model failed (R² = 0.2858) with non-physical staircase artifacts, whereas the KBXGB model maintained high predictive fidelity (R² = 0.8646) and successfully reproduced physical crack growth trends. The proposed approach effectively mitigates the “black-box” limitations of machine learning in sparse data regions, offering a high-precision and physically robust tool for corrosion fatigue-life prediction under complex service conditions. Full article

This study developed a machine learning approach to detect carbide precipitation in the microstructure of austenitic stainless steel, specifically grade 316, using a convolutional neural network (CNN). Microstructural images were prepared and classified into three categories: as-received, heat-treated without carbide precipitation, and heat-treated with carbide precipitation. A CNN was trained and validated using two separate datasets of varying sizes to assess the impact of data quantity on classification performance. This automated microstructure recognition system offers potential benefits for additive manufacturing (AM) by enabling real-time quality assessment and feedback control, particularly for avoiding undesirable carbide formation during metal 3D printing. By linking microstructural analysis to processing conditions, this approach supports the development of defect-free, corrosion-resistant components and contributes to the integration of intelligent monitoring within digital manufacturing workflows. Full article

Successive ionic layer adsorption reaction (SILAR) was used to deposit Cu₂O nanosheets on anodized TiO₂ nanotubes at different deposition cycles (4, 8, 15, and 20). Compared to the bare TiO₂ nanotubes, these coatings were investigated for their tribological behavior (friction, wear and energy loss), scanning and transmission electron microscopy (SEM, TEM), X-ray Diffraction (XRD) was used to characterize Cu₂O/TiO₂ coatings to study the effect of number of cycles on the morphological and structural properties of the samples; these characteristics engage in determining the wear mechanisms. The assessment of the coating’s adhesion was determined by the obtained critical loads from the scratch test; the 15 cycles Cu₂O/TiO₂ exhibited higher critical loads, which corresponds to improved adhesion. This sample also showed a low wear volume of 7.5 × 10⁶ µm³ compared to other samples but higher energy loss due to the low shear strength of copper oxide. The friction coefficient, however, decreased from 0.7 for bare TiO₂ nanotubes to 0.48 for 20 cycles Cu₂O/TiO₂ coatings at higher loads, which proves the wear resistance enhancement. Since these coatings will be manufactured for orthopedic and dental implant applications, the corrosion resistance was tested, and the 15 cycles Cu₂O-NPs/TiO₂-NTs where these coatings exhibited the most favorable combination of a low corrosion current density (1.9 × 10⁻⁴ A/cm²) and a noble corrosion potential (−0.3 V/SCE); furthermore, there was a polarization resistance of 2.4 × 10⁴ Ω·cm² and a protection efficiency of 96.7%, indicating significantly enhanced corrosion resistance as opposed to the other samples. Full article

Composite specimens of normal concrete (NC) and ultra-high performance concrete (UHPC) in marine tidal zones are susceptible to coupled physico-chemical degradation under seawater wet–dry cycling; however, the microscopic damage-evolution mechanisms within the NC/overlay transition zone (OTZ)/UHPC three-phase region remain unclear. In this study, accelerated erosion was conducted using 10-fold concentrated artificial seawater under 0, 30, 60, and 90 wet–dry cycles. The X-ray computed tomography, mercury intrusion porosimetry, backscattered electron imaging coupled with energy dispersive X-ray spectroscopy and slant shear tests were employed to systematically investigate the macroscopic bonding performance and microscopic structural damage of NC-UHPC composites. The results show that the interfacial bond strength initially increases and then declines, exhibiting a 13.53% improvement after 30 wet–dry cycles and a sharp 41.55% decrease after 90 cycles compared with that after 60 cycles. The damage severity was the highest in NC, intermediate in OTZ, and lowest in UHPC. The gas-rich pore region within the OTZ provides a stress-buffering effect during the early stage of corrosion. After 90 wet–dry cycles, the total porosity increased by 0.14%, with external porosity increasing by 0.21% and internal porosity decreasing by 0.07%, indicating a pore-structure reconfiguration characterized by micropore coalescence and an increased proportion of macropores. These findings clarify the damage process associated with seawater erosion, pore expansion, and interfacial failure, providing theoretical support for the repair design and durability assessment of marine concrete structures. Full article

TiO₂ nanotubes were synthesized using the anodization method on Ti foils and decorated with PbS nanoparticles by the SILAR method at different cycle numbers (10, 15, 20, 25, and 30). These samples were characterized using SEM, TEM, XRD, and microhardness tests. Morphologically, the PbS nanoparticles were evenly dispersed on TiO₂ nanotubes in the shape of small spheres. With an increase in the number of cycles, the size and shape of the nanoparticles increased. This also affected the structure and crystallinity of the PbS NPs, as the crystallite size of PbS increased. The in-depth analysis of the tribological characteristics of the coatings conducted using the scratch test allowed us to evaluate the adhesion of the coatings, a crucial aspect in determining their effectiveness and durability. Furthermore, we found that the wear resistance of the coatings increased with the number of PbS cycles up to 15 cycles. However, for the samples with higher size distribution and crystallite size, such as those with more than 15 cycles, the microhardness continued to decrease. This indicates that the addition of PbS can improve the durability of TiO₂ coatings, making them a potential candidate for advanced surface coatings in demanding engineering applications. Electrochemical measurements were conducted to assess the corrosion resistance of the samples. The electrochemical impedance spectra (EIS) results revealed that the PbS/TiO₂ coatings with 15 deposition cycles exhibited the most effective corrosion resistance, with a dense and uniform distribution of PbS nanoparticles forming a compact barrier that effectively protects against corrosion. The charge transfer resistance (R_ct) and the absorption capacitance (Q_ab) values were higher for the 15-cycle sample (4.49 Ω·cm² and 0.9 Fsⁿ⁻¹cm⁻², respectively). Full article

Depletion of reserves of rich copper–porphyry ore deposits necessitates the development of highly efficient methods for Mo (VI) extraction from complex, corrosive hydro-metallurgical media. The present study undertakes a comprehensive assessment of sorptive concentration of Mo (VI) from strongly acidic sulfate solutions (120 g/L H₂SO₄) by employing a spectrum of commercially available strong- and weak-base anion-exchange resins. It has been established that the macroporous weak-base anion exchanger Purolite A-100 demonstrates decisive superiority over gel-type analogs (Lewatit M-800, AB-17), facilitating unimpeded intra-gel diffusion of bulky molybdenyl sulfato-complexes anions, thereby circumventing the obstructive “sieve effect.” Thermodynamic and kinetic investigations revealed that the sorption process exhibits pronounced concentration- and pH-dependent characteristics. Peak extraction efficiency (up to 95.91%) is achieved at pH ≈ 1, a finding that correlates with the region of maximal protonation of tertiary amino groups within the resin matrix. Kinetic acceleration of mass transfer upon heating to 80 °C has been experimentally confirmed, yielding 94.6% extraction within 60 min. The obtained results corroborate the prospective integration of macroporous weak-base anion exchangers into operational hydro-metallurgical schemes as an environmentally benign and efficacious alternative to conventional solvent extraction of molybdenum. Full article

This research investigates the effects of electrochemical oxidation on surface properties and corrosion performance of the Ti-6Al-4V ELI alloy intended for biomedical applications. Electrochemical anodization is performed in 1 M H₂SO₄ and 1 M H₃PO₄ electrolytes at applied potentials of 200, 250, and 275 V for 1 min. Morphological characteristics and chemical constitution of the oxide films are investigated by SEM-EDS analysis, while surface roughness, wettability, and microhardness are evaluated using profilometry, contact angle measurements, and Vickers microhardness testing. Electrochemical behavior is assessed by monitoring free potential (OCP) and electrochemical impedance spectroscopy in Ringer solution and Ringer solution containing 40 g/L hydrogen peroxide. Among the investigated conditions, anodization at 200 V for 1 min provides the most favorable surface morphology, producing well-defined and uniformly distributed nanopores while maintaining the structural stability of the oxide layer. Oxidation in 1 M H₂SO₄ leads to a more homogeneous nanoporous structure, higher surface roughness, improved hydrophilicity, and increased microhardness compared to 1 M H₃PO₄ treatment. Electrochemical impedance spectroscopy analysis reveals superior corrosion resistance for all oxidized samples in comparison with the untreated alloy. The oxide layers obtained in sulfuric acid exhibit the highest polarization resistance and electrochemical stability in simulated physiological environments. Full article

This study presents two independently optimised Extreme Gradient Boosting (XGBoost) regression models, one for predicting corrosion current density ($i_{corr}$) and one for predicting corrosion potential ($E_{corr}$) parameters of carbon steel rebar embedded in mortar and immersed in simulated pore solution. An experimental dataset consisting of 216 measurements was curated from a systematic potentiodynamic scan study covering six chloride contamination levels, two carbonation states (non-carbonated and carbonated), four moisture conditions for mortar (65%, 85%, 95% relative humidity, and submerged), and three conditioning durations for simulated pore solution (36 h, 72 h and 20 days). Hyperparameters of the XGBoost models were optimised using a Bayesian optimisation framework with the Tree-structured Parzen Estimator (TPE) sampler over 300 trials. Model performance was assessed using 5-fold cross-validation and a random 80:20 train–test split. The optimised models achieved cross-validation $R^2$ scores of 0.936 and 0.953 for $i_{corr}$ and $E_{corr}$, respectively. On the hold-out test set, $R^2$ values of 0.933 and 0.945 were obtained with test RMSE values of 0.2 log₁₀(µA/cm²) and 41.9 mV, respectively. The contribution of each input feature to model predictions was quantified and visualised using the SHapley Additive exPlanations (SHAP) methodology. SHAP analysis reveals that chloride content has the highest impact on $i_{corr}$, followed by carbonation state and the low-humidity condition, while for $E_{corr}$, chloride content and the Submerged condition have the greatest impact. An interactive web application was developed using Streamlit, enabling researchers and practitioners to obtain corrosion parameter predictions. The findings provide data-driven insights into the relative importance of environmental factors governing rebar corrosion, with direct implications for the development of accurate corrosion prediction models for reinforced concrete service life assessment. Full article

This study presents an investigation of the factors (driven by coupled multi-factor corrosion mechanisms) which contribute to the degradation of the spirally wound armored steel wires used to protect core-structured, unarmored optical-fiber submarine cables. The influences of the physical properties of deep-sea sediments on the durability of unarmored cables, as well as the impact of ionizing radiation on optical fibers, are also assessed. The objective of this paper is to establish a scientific basis for cable longevity by integrating theoretical insights with empirical evidence. Although the steel utilized in armored cables is cost-effective and durable, it remains vulnerable to corrosion. Since the inaugural practical deployment of submarine communication cables between the UK and France in the 1850s, only a small number of studies worldwide have examined the corrosion and durability of cable armor. There is also limited literature examining the physical characteristics of the deep-sea surface sediments that directly affect the service life of the cables’ mechanically fragile polyethylene sheathing. An in-depth analysis of the cable damage and environmental conditions observed during maintenance operations provides valuable insights into the key environmental factors that influence armor corrosion and cable longevity. This research aims to guide future design and support strategies to improve the sustainability and durability of cable systems in marine environments. Full article