New insights into the effect of Tris-HCl and Tris on corrosion of magnesium alloy in presence of bicarbonate,sulfate,hydrogen phosphate and dihydrogen phosphate ions

In vitro degradation is an important approach to screening appropriate biomedical magnesium（Mg） alloys at low cost. However, corrosion products deposited on Mg alloys exert a critical impact on corrosion resistance. There are no acceptable criteria on the evaluation on degradation rate of Mg alloys. Understanding the degradation behavior of Mg alloys in presence of Tris buffer is necessary. An investigation was made to compare the influence of Tris-HCl and Tris on the corrosion behavior of Mg alloy AZ31 in the presence of various anions of simulated body fluids via hydrogen evolution, p H value and electrochemical tests.The results demonstrated that the Tris-HCl buffer resulted in general corrosion due to the inhibition of the formation of corrosion products and thus increased the corrosion rate of the AZ31 alloy. Whereas Tris gave rise to pitting corrosion or general corrosion due to the fact that the hydrolysis of the amino-group of Tris led to an increase in solution p H value, and promoted the formation of corrosion products and thus a significant reduction in corrosion rate. In addition, the corrosion mechanisms in the presence of Tris-HCl and Tris were proposed. Tris-HCl as a buffer prevented the formation of precipitates of HCO;, SO;,HPO;and H;PO;ions during the corrosion of the AZ31 alloy due to its lower buffering p H value（x.x）.Thus, both the hydrogen evolution rate and corrosion current density of the alloy were approximately one order of magnitude higher in presence of Tris-HCl than Tris and Tris-free saline solutions.

A deep learning-based method for segmentation and quantitative characterization of microstructures in weathering steel from sequential scanning electron microscope images

Microstructural classification is typically done manually by human experts,which gives rise to uncertainties due to subjectivity and reduces the overall efficiency.A high-throughput characterization is proposed based on deep learning,rapid acquisition technology,and mathematical statistics for the recognition,segmentation,and quantification of microstructure in weathering steel.The segmentation results showed that this method was accurate and efficient,and the segmentation of inclusions and pearlite phase achieved accuracy of 89.95%and 90.86%,respectively.The time required for batch processing by MIPAR software involving thresholding segmentation,morphological processing,and small area deletion was 1.05 s for a single image.By comparison,our system required only 0.102 s,which is ten times faster than the commercial software.The quantification results were extracted from large volumes of sequential image data(150 mm^(2),62,216 images,1024×1024 pixels),which ensure comprehensive statistics.Microstructure information,such as three-dimensional density distribution and the frequency of the minimum spatial distance of inclusions on the sample surface of 150 mm^(2),were quantified by extracting the coordinates and sizes of individual features.A refined characterization method for two-dimensional structures and spatial information that is unattainable when performing manually or with software is provided.That will be useful for understanding properties or behaviors of weathering steel,and reducing the resort to physical testing.