Correction: Liu et al. Microstructure and Properties of SUS304 Stainless Steel Joints Brazed with Electrodeposited Ni-Cr-P Alloy Coatings. Materials 2021, 14, 4216.

Error in Figure/Table [...].

Microstructure and Properties of SUS304 Stainless Steel Joints Brazed with Electrodeposited Ni-Cr-P Alloy Coatings.

In this study, an amorphous Ni-13.4Cr-11.6P (mass%) alloy coating with a thickness of 30 µm was deposited on the surface of SUS304 stainless steel as a brazing filler metal to conduct brazing. The differential thermal analysis measurements indicate that the electrodeposited Ni-13.4Cr-11.6P alloy has a melting point of approximately 892 °C, which is almost consistent with that of the commercial BNi-7 filler metal. The microstructure, shear strength, and fracture mode of the brazed joint were investigated using an electron probe X-ray microanalyzer, a scanning electron microscope, an optical microscope, and a universal testing machine. The results showed that the brazed filler metal is filled between the SUS304 stainless steel plates without any flaws in the brazed seam. The P-containing phases, i.e., the Cr-P rich phase and the (Ni,Fe)3P phase, were formed in the brazed seam. The shear strength of the brazed joint obtained in this study is 59.0 MPa. The fracture occurs in the brazed filler zone, where the brittle P-containing phases are present. Galvanic current measurement results showed that the brazed Ni-13.4Cr-11.6P alloy coating has a better corrosion resistance than that of the brazed Ni-11P alloy coating, which can be attributed to the formation of a large amount of Ni-Fe solid solution and Cr-P rich phase in the top layer of the brazed Ni-13.4Cr-11.6P alloy coating.

Using Machine Learning and a Combination of Respiratory Flow, Laryngeal Motion, and Swallowing Sounds to Classify Safe and Unsafe Swallowing.

OBJECTIVE: The aim of this research was to develop a swallowing assessment method to help prevent aspiration pneumonia. The method uses simple sensors to monitor swallowing function during an individual's daily life. METHODS: The key characteristics of our proposed method are as follows. First, we assess swallowing function by using respiratory flow, laryngeal motion, and swallowing sound signals recorded by simple sensors. Second, we classify whether the recorded signals correspond to healthy subjects or patients with dysphagia. Finally, we analyze the recorded signals using both a feature extraction method (linear predictive coding) and a machine learning method (support vector machine). RESULTS: Based on our experimental results for 140 healthy subjects (54.5 32.5 years old) and 52 patients with dysphagia (75.5 20.5 years old), our proposed method could achieve 82.4% sensitivity and 86.0% specificity. CONCLUSION: Although 20% of testing sample sets were erroneously classified, we conclude that our proposed method may facilitate screening examinations of swallowing function. SIGNIFICANCE: In combination with the portable sensors, our proposed method is worth utilizing for noninvasive swallowing assessment.