[en] The corrosion behaviour of E690 steel in industrial and non-industrial marine splash environments was studied by environmental testing, morphology analysis, electrochemical measurements, and scanning Kelvin probe microscopy. Chloride and sulphide anions were found to diffuse across the rust layer following the evaporation of seawater splashed on the steel’s surface. The cation-selective permeability of the rust layer resulted in an anion concentration gradient across the rust layer, which was more significant in the presence of sulphur dioxide. In addition, sulphur dioxide enhanced the formation of α-FeOOH, which led to the formation of distinct anode and cathode areas at the rust/steel interface.

[en] Pitting corrosion of high-strength steel 10Ni8CrMoV under square wave polarization (SWP) in simulated deep-sea environment is investigated and the possible mechanism is proposed. The results show that potential perturbation generates periodic intensification effect on both anodic and cathodic processes by frequently breaking the electrode equilibrium state. The intensity of periodic intensification effect essentially depends on the concentration gradient of Fe²⁺ cations at the steel/solution interface which acts as the forced electrochemical oscillator. The concentration gradient and the resulting concentration polarization effect increase periodically with the increase in SWP potential range. The morphology observation of the pitting and electric charge calculation indicate that the periodic intensification effect can promote the initiation and growth of pits by enhancing the anodic dissolution even under cathodic protection, but it is ineffective below the hydrogen evolution potential. The decrease in either upper or lower potential can mitigate anodic dissolution. Through the statistical analysis of pitting size, it is found that the wide potential range tends to activate the metastable pitting formed under hydrostatic pressure, forming densely distributed pitting. Meanwhile, it is more favorable to the formation of fully grown pits with high size dispersion degree when the proportion of electric charge in the anodic process is higher.

[en] Highlights: • Sr promotes micro-galvanic corrosion and undermining of Mg₁₇Sr₂ phases. • Sr increases weight loss rate but inhibits localized corrosion of Mg-Zn-Mn alloy. • Corrosion depth/second phase spacing keep constant as added Sr content exceeds 1.0 wt.%. • The optimum Sr content is 1.01.5 wt.% in Mg-1Zn-1 Mn alloy. -- Abstract: Effect of Sr on microstructure and corrosion behavior of biodegradable Mg-1Zn-1Mn-xSr alloy is investigated. Decrease in second phase spacing and linear increase of Mg₁₇Sr₂ fraction are observed with increasing Sr content. Corrosion rate increases linearly with increasing Sr content from 0 to 1.5 wt.% because of the enhanced micro-galvanic corrosion. Undermining of Mg₁₇Sr₂ and α-Mg grains results in the dramatic increase in corrosion rate at a high Sr content (3.0 wt.%). Localized corrosion is inhibited by alloying with Sr, yielding an optimum Sr content of 1.01.5 wt.% with comprehensive consideration of uniform/localized corrosion resistance and biomedical adaptability.

[en] Pitting morphologies of E690 low-alloy steel in industrial and non-industrial marine splash zones are statistically analyzed, including the influence of plastic deformation. Results show that in a marine splash zone, E690 low-alloy steel experiences severe uniform corrosion and pitting corrosion, and the corrosion is more severe in the high-temperature non-industrial environment. The relationship between the pitting area changes and the depth can be well fitted to the Boltzmann cumulative distribution function to investigate the corrosion factors. In non-industrial marine splash zone, high content of chloride ions makes the metal surface have a high roughness and anodic dissolution is then promoted. The pitting depth is strongly promoted by anodic dissolution. In the marine splash zone, plastic deformation aggravates the maximum pitting depth, especially in the initial stage and in SO₂-pollutant environments, although this effect gradually decreases. Pitting on low plastic deformation area in U bend samples may influenced by receiving electrons, which inhibits the protective FeOOH generation.

[en] Motion compensation can eliminate inconsistencies of respiratory movement during image acquisitions for precise vascular reconstruction in the clinical diagnosis of vascular disease from x-ray angiographic image sequences. In x-ray-based vascular interventional therapy, motion modeling can simulate the process of organ deformation driven by motion signals to display a dynamic organ on angiograms without contrast agent injection. Automatic respiratory signal estimation from x-ray angiographic image sequences is essential for motion compensation and modeling. The effects of respiratory motion, cardiac impulses, and tremors on structures in the chest and abdomen bring difficulty in extracting accurate respiratory signals individually. In this study, an end-to-end deep learning framework based on a motion-flow-guided recurrent network is proposed to address the aforementioned problem. The proposed method utilizes a convolutional neural network to learn the spatial features of every single frame, and a recurrent neural network to learn the temporal features of the entire sequence. The combination of the two networks can effectively analyze the image sequence to realize respiratory signal estimation. In addition, the motion-flow between consecutive frames is introduced to provide a dynamic constraint of spatial features, which enables the recurrent network to learn better temporal features from dynamic spatial features than from static spatial features. We demonstrate the advantages of our approach on designed datasets which contain coronary and hepatic angiographic sequences with diaphragm structures, and coronary angiographic sequences without diaphragm structures. Our method improves over state-of-the-art manifold-learning-based methods by 85.7%, 81.5% and 75.3% in respiratory signal accuracy metric on these datasets. The results demonstrate that the proposed method can effectively estimate respiratory signals from multiple motion patterns. (paper)