It has been proved that zeolite MFI coatings synthesized by in situ hydrothermal synthesis is corrosion-resistant. Pure silica zeolite MFI (silicalite-1) coatings on aluminum alloy 1060 plates seeded by brush-coating a seed paste and leveling the seed layer with fingers have been synthesized by the seeded growth method in this study. The seed paste is composed of seeds, silica sol, and water. The effects of the seed/sol ratio and the seed crystal size on the properties of silicalite-1 coatings are investigated in detail. The corrosion protection performance of the silicalite-1 coatings on aluminum alloy plates is determined by a direct current polarization technique. The silicalite-1 coatings (∼3 μm thick) obtained by the seeded growth method at 150 °C for 3 h are found to be highly b-oriented and corrosion-resistant in severely corrosive acidic aqueous solution (0.5 M H2SO4) and sodium chloride aqueous solution (0.5 M) exposed to air at room temperature. The optimum seed/sol ratio and the seed crystal size in the seed paste are 6.0 and 500 nm, respectively.
For decades, traditional correlation analysis and regression models have been used in social science research. However, the development of machine learning algorithms makes it possible to apply machine learning techniques for social science research and social issues, which may outperform standard regression methods in some cases. Under the circumstances, this article proposes a methodological workflow for data analysis by machine learning techniques that have the possibility to be widely applied in social issues. Specifically, the workflow tries to uncover the natural mechanisms behind the social issues through a data-driven perspective from feature selection to model building. The advantage of data-driven techniques in feature selection is that the workflow can be built without so much restriction of related knowledge and theory in social science. The advantage of using machine learning techniques in modelling is to uncover non-linear and complex relationships behind social issues. The main purpose of our methodological workflow is to find important fields relevant to the target and provide appropriate predictions. However, to explain the result still needs theory and knowledge from social science. In this paper, we trained a methodological workflow with left-behind children as the social issue case, and all steps and full results are included.
Exploratory analysis is an important way to gain understanding and find unknown relationships from various data sources, especially in the era of big data. Traditional paradigms of social science data analysis follow the steps of feature selection, modeling, and prediction. In this paper, we propose a new paradigm that does not require feature selection so that data can speak for itself without manually picking out features. Besides, we propose using the deep network as a methodology to explore previously unknown relationships and capture complexity and non-linearity between target variables and a large number of input features for big social data. The new paradigm tends to be a relatively generic approach that can be widely used in different scenarios. In order to validate the feasibility of the paradigm, we use country-level indicators forecasting as a case study. The process includes: 1) data collection and preparation and 2) modeling and experiment. The data collection and preparation part builds a data warehouse and conducts the extracttransform-load process to eliminate data format inconsistency. The modeling and experiment part includes model setup and model structures change to achieve relatively high accuracy on prediction results at both model level and case level. We find some patterns about network capacity modification and the influence of time interval difference on the test results, whereas both of them deserve further research.
The radioactive corrosion products 58Co and 60Co in the primary loops of pressurized water reactors (PWRs) are the main sources of radiation doses to which workers in nuclear power plants are exposed. To understand cobalt deposition on 304 stainless steel (304SS), which is the main structural material used in the primary loop, the microstructural characteristics and chemical composition of a 304SS surface layer immersed for 240 h in borated and lithiated high-temperature water containing cobalt were investigated with scanning electron microscopy (SEM), X-ray diffraction (XRD), laser Raman spectroscopy (LRS), X-ray photoelectron spectroscopy (XPS), glow discharge optical emission spectrometry (GD-OES), and inductively coupled plasma emission mass spectrometry (ICP-MS). The results showed that two distinct cobalt deposition layers (an outer layer of CoFe2O4 and an inner layer of CoCr2O4) were formed on the 304SS after 240 h of immersion. Further research showed that CoFe2O4 was formed on the metal surface by coprecipitation of the iron preferentially dissolved from the 304SS surface with cobalt ions from the solution. The CoCr2O4 was formed by ion exchange between the cobalt ions entering the metal inner oxide layer and (Fe, Ni) Cr2O4. These results are useful in understanding cobalt deposition on 304SS and have a certain reference value for exploring the deposition behavior and mechanism of radionuclide cobalt on 304SS in the PWR primary loop water environment.
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