Application of Multivariate Data Analysis Techniques in Modeling Structure–Property Relationships of Some Superconductive Cuprates

✍ Scribed by K. Lehmus; M. Karppinen

• Publisher: Elsevier Science
• Year: 2001
• Tongue: English
• Weight: 401 KB
• Volume: 162
• Category: Article
• ISSN: 0022-4596
• DOI: 10.1006/jssc.2001.9283

✦ Synopsis

Multivariate analysis methods are used for the examination of the 5ne structure and superconductivity properties of the 1212type superconductive copper oxides with the stoichiometry of MA 2 QCu 2 O 6؉z (M ‫؍‬ Cu, Hg, Tl/Pb; A ‫؍‬ Ba, Sr; Q ‫؍‬ rare-earth element, Ca; z ‫؍‬ 0 +1). PCA (principal component analysis) is used for the qualitative evaluation of relations between structural variables such as the cation}oxygen bond lengths, bond angles, and oxygen content z, and the superconductivity transition temperature T C , adopted from a number of neutron di4raction studies published for MA 2 QCu 2 O 6؉z samples with various compositions. The di4erent ways of doping positive holes in the superconductive CuO 2 plane are discussed on the basis of the PCA results. Quantitative modeling of the value of T C in the CuA 2 QCu 2 O 6؉z system is successfully performed by PLS (projections to latent structures by means of partial least squares), resulting in a model with predictive power of & &93%. The present study demonstrates the potential of multivariate analysis methods in studying structure+property relations of inorganic materials with ionic structure.