Morphology and mechanical characteristics of monolayer and multilayer Ir coating by double glow plasma

Wu, Wangping; Jiang, Jin-Wu; Chen, Zhaofeng; Jiang, Peng; Wang, Zhizhi

doi:10.1007/s11595-017-1579-9

Cited by 11 publications

(2 citation statements)

References 39 publications

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“…In 2018, Wu Jizhong et al addressed the parameter estimation problem for obtaining soil moisture content by using GPS-IR (GPS-interferometric reflectometry). They proposed an improved method for estimating reflection signal parameters and studied the process of establishing soil moisture inversion models [22]. With the development of computer technology, scholars began to use deep learning techniques for soil moisture inversion.…”

Section: Introductionmentioning

confidence: 99%

Research on Soil Moisture Inversion Method for Canal Slope of the Middle Route Project of the South to North Water Transfer Based on GNSS-R and Deep Learning

Hu,

Li,

Liu

et al. 2023

Remote Sensing

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The soil moisture from the South-to-North Water Diversion Middle Route Project is assessed in this study. Complex and variable geological conditions complicate the prediction of soil moisture in the study area. To achieve this aim, we carried out research on soil moisture inversion methods for channel slopes in the study area using massive monitoring data from multiple GNSS observatories on channel slopes, incorporating GNSS-R techniques and deep learning algorithms. To address the issue of low accuracy in linear inversion when using a single satellite, this study proposes a multi-satellite and multi-frequency data fusion technique. Furthermore, three soil moisture inversion models, namely, the linear model, BP neural network model, and GA-BP neural network model, are established by incorporating deep learning techniques. In comparison with single-satellite data inversion, with the data fusion technique proposed in this study, the correlation is improved by 12.7%, the root mean square error is reduced by 0.217, the mean square error is decreased by 0.884, and the mean absolute error is decreased by 0.243 with the linear model. With the BP neural network model, the correlation is increased by 15.4%, the root mean square error is decreased by 0.395, the mean square error is decreased by 0.465, and the mean absolute error is reduced by 0.353. Moreover, with the GA-BP neural network model, the correlation is improved by 6.3%, the root mean square error is decreased by 1.207, the mean square error is decreased by 0.196, and the mean absolute error is reduced by 0.155. The results indicate that performing data fusion by using multiple satellites and multi-frequency bands is a feasible approach for improving the accuracy of soil moisture inversion. These research findings provide new technical means for the risk analysis of deformation disasters in the expansive soil channel slopes of the South-to-North Water Diversion Middle Route Project.

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Section: Introductionmentioning

confidence: 99%

Research on Soil Moisture Inversion Method for Canal Slope of the Middle Route Project of the South to North Water Transfer Based on GNSS-R and Deep Learning

Hu,

Li,

Liu

et al. 2023

Remote Sensing

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“…4,5,[8][9][10][11][12][13] Ir is a promising candidate for a wide range of applications due to its high melting point, low oxygen permeability, high chemical stability, superior oxidation resistance and electrocatalytic activity performance. [14][15][16][17] Therefore, Ir doped Ni may lead to potential wide use, especially in catalytic and anti-corrosive environment applications. 18,19 Ir-Ni alloy is of particular interest because of the completely miscibility of Ir and Ni and the absence of intermetallic compounds in Ir-Ni isomorphous phase diagram.…”

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confidence: 99%

Effect of Electroplating Variables on Electrodeposition of Ni Rich Ni-Ir Alloys from Citrate Aqueous Solutions

Wang

Jiang

et al. 2017

J. Electrochem. Soc.

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Nickel–iridium (Ni–Ir) alloy system is of great interest for catalytic and corrosive environment applications. In this study, Ni-Ir alloys were galvanostatically electrodeposited on copper substrates from citrate aqueous solutions in a three-electrode cell system. The effects of deposition variables, including pH, temperature and current density, on the faradaic efficiency (FE), chemical composition, surface morphology and crystallographic structure of the films were studied. The electrochemical behaviors of Ir, Ni and co-deposited alloys were investigated using cyclic voltammetric, chronopotentiometric and chronoamperometric techniques. The results show that Ni–Ir alloys as thick as 25.7 μm with Ir–content as high as 15 at% and the FE as high as 36% were obtained. By adjusting these deposition parameters, the optimum pH, temperature and current density for deposition of Ni-Ir alloys were 5.0, 70°C and 60 mA·cm−2, respectively. As the deposition variables were varied, the abundant of Ni-content in the deposit was obtained. The alloy was composed of nanocrystalline phase. The deposition of Ni ions was favored, and the co-deposition of Ni-Ir alloy was a normal deposition. The chronoamperometric transient nucleation at the potentials of −0.25 and −3.0 V were consistent with the model of diffusion-controlled 3D nucleation.

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The influence of shot peening on mechanical properties, wear, and corrosion resistance of 42CrMo steel

Sun,

2024

Int J Adv Manuf Technol

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Morphology and mechanical characteristics of monolayer and multilayer Ir coating by double glow plasma

Cited by 11 publications

References 39 publications

Research on Soil Moisture Inversion Method for Canal Slope of the Middle Route Project of the South to North Water Transfer Based on GNSS-R and Deep Learning

Research on Soil Moisture Inversion Method for Canal Slope of the Middle Route Project of the South to North Water Transfer Based on GNSS-R and Deep Learning

Effect of Electroplating Variables on Electrodeposition of Ni Rich Ni-Ir Alloys from Citrate Aqueous Solutions

The influence of shot peening on mechanical properties, wear, and corrosion resistance of 42CrMo steel

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