Plasma resistance of Bi-Al-Si-O and Bi-Al-Si-O-F glass coating film (Bi-Al-Si-O와 Bi-Al-Si-O-F 유리 코팅막의 플라즈마 저항성)
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- Journal of the Korean Crystal Growth and Crystal Technology
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- v.34 no.4
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- pp.131-138
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- 2024
In this study, the microstructure and plasma resistance characteristics of 35Bi2O3-15Al2O3-50SiO2 (BiAl SiO) and 35Bi2O3-7.5Al2O3-50SiO2-7.5AlF3 (BiAlSiOF) glass layers coated on sintered alumina substrates were investigated according to the sintering conditions. The coated layers were formed using the bar coating method and then sintered at a temperature in the range of 700~900℃, which corresponds to the temperature before and after the hemisphere forming temperature, after a debinding process. The plasma resistance of the two coated glasses was approximately 2~3 times higher than that of the quartz glass, and in particular, the BiAlSiOF glass film with F added showed higher plasma resistance than BiAlSiO. It is thought to be due to the effect of suppressing the reaction with fluorine gas by adding fluorine to the glass. When the sintering time was increased at 700℃ and 800℃, the plasma resistance of both glasses improved, but when the sintering temperature was increased to 900℃, the plasma resistance decreased again (i.e., the etching rate increased). This phenomenon is thought to be related to the crystallization behavior of both glasses. The change in plasma resistance depending on the sintering conditions is thought to be related to the appearance of Al and Bi-rich phases.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (