Copper and its alloys are used widely in marine environments, and anisotropic corrosion influences the corrosion kinetics of copper. Corrosion of copper in an electrolyte containing Cl− is described as a dissolution–deposition process, which is a prolonged process. Therefore, it is laborious to clarify the corrosion anisotropy in different stages. In this paper, electrochemical impedance spectroscopy (EIS) following elapsed open circuit potential (OCP) test with 0 h (0H), 24 h (24H) and 10 days (10D) was adopted. To exclude interruptions such as grain boundary and neighbor effect, single crystal (SC) Cu(100) and Cu(111) were employed. After 10D OCP, cross-sectional slices were cut and picked up by a focused ion beam (FIB). The results showed that the deposited oxide was Cu2O and Cu(100)/Cu(111) experienced different corrosion behaviors. In general, Cu(100) showed more excellent corrosion resistance. Combined with equivalent electrical circuit (EEC) diagrams, the corrosion mechanism of Cu(100)/Cu(111) in different stages was proposed. In the initial stage, a smaller capacitive loop of Cu(111) suggested preferential adsorption of Cl− on air-formed oxide film on Cu(111). Deposited oxide and exposed bare metals also played an important role in corrosion resistance. Rectangle indentations and pyramidal structures formed on Cu(100)/Cu(111), respectively. Finally, a perfect interface on Cu(100) explained the tremendous capacitive loop and higher impedance (14,274 Ω·cm2). Moreover, defects in the oxides on Cu(111) provided channels for the penetration of electrolyte, leading to a lower impedance (9423 Ω·cm2) after 10D corrosion.
High-dimensional of image data is an obstacle for clustering. One of methods to solve it is feature representation learning. However, if the image is distorted or suffers from the influence of noise, the extraction of effective features may be difficult. In this paper, an end-to-end feature learning model is proposed to extract denoising low-dimensional representations from distorted images, and these denoising features are evaluated by comparing with several feature representation methods in clustering task. First, some related works about classical feature learning are introduced. Then the architecture and working mechanism of denoising feature learning model are presented. As the structural characteristics of this model, it can obtain essential information from image to decrease reconstruction error. When facing with corrupted data, it also runs a robust clustering result. Finally, compared to other unsupervised feature learning methods, extensive experiments demonstrate that the obtained feature representations by proposed model run a competitive clustering performance. The low-dimensional representations can replace the original datasets primely.
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