Abstract
AbstractThe similarity patterns in the physicochemical properties of copper–lead and copper–zinc borate glasses were identified by means of finding similarity within the objects of study using multivariate statistical analysis. As exploratory methods of multivariate analysis, cluster analysis, principal components analysis, and two-way clustering were applied for a set of copper–lead and copper–zinc borate glasses. Specific correlations among the physicochemical properties of copper glasses were interpreted. In particular, the effect of Pb and Zn doping metal ion in copper glasses in the structural and mechanical properties is identified. Interestingly, the degree of lead content determines two kinds of glasses with specific physicochemical properties.
Highlights
Specific changes in the physicochemical parameters of the borate glasses due to changes in their chemical composition could be subject to another experimental data approach, namely, intelligent data analysis [17,18]
Several correlations among glasses and their physicochemical properties based on multivariate analysis techniques as cluster analysis (CA), principal components analysis (PCA), and two-way clustering were obtained
This allows for identifying the effects of Pb and Zn doping metal ions in copper glasses in the structural and mechanical properties
Summary
Specific changes in the physicochemical parameters of the borate glasses due to changes in their chemical composition could be subject to another experimental data approach, namely, intelligent data analysis (exploratory data mining) [17,18] This option is rarely used in the analysis of the properties of noncrystalline materials. Multivariate statistics methods such as cluster analysis (CA) or principal components analysis (PCA) offer a new opportunity for data modeling, classification, and interpretation. They are superior with respect to their information content to the traditional correlation analysis if relationships between physicochemical properties and chemical composition are sought. Relationships for chemical composition versus physicochemical parameters are evaluated by regression analysis
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