Determination of spatial distribution of alloying and impurity elements in zircaloy using imaging secondary ion mass spectrometry and principal component analysis. (February 2017)

• Record Type: Journal Article
• Authors: Karki, V.; Agarwal, R.; Alamelu, D.
• Is Part Of: Vacuum. Volume 136(2017)
• Page Start: 1
• Page End: 9
• Publication Date: 2017-02
• Subjects: Zirconium alloys -- SIMS -- Microstructure -- Depth distribution -- Principal component analysis; Vacuum -- Periodicals; 621.55
• DOI: 10.1016/j.vacuum.2016.11.015
• Languages: English
• ISSNs: 0042-207X

Abstract

Abstract: SIMS (Secondary Ion Mass Spectrometry) was successfully utilized for investigating the surface microstructure and depth distribution analysis of alloying and impurity elements in unirradiated zirconium alloy, Zr X-868 sample. Oxygen (O2 + ) and Cesium (Cs + ) primary ion beams with positive and negative secondary ions detection mode respectively was selected for the present analyses. It was concluded from the surface ion distribution images that the elements O, Zr, Nb, Sn, Hf and Ta were homogenously distributed while C, Na, Mg, Si, Ca, V, Cr, Fe, Co and Cu were non homogenously distributed laterally. Multivariate technique called Principal Component Analysis (PCA) was selected for studying the correlation between surface distribution pattern of different elements. The scores plot between PC2 and PC1 showed that the groups of elements (Mg, Si, Fe), (Ca, Co, Cu) and (V, Cr) have similar distribution patterns while C and Na which doesn't belong to any of the groups, showed unique distribution patterns. Depth distribution analyses were also carried out which revealed uniform depth distribution of all the elements within the analysis area of 250 × 250 μm 2 . Graphical abstract: Highlights: SIMS was used for investigating the surface microstructure and depth distribution analysis of elements in zircaloy sample. It was concluded that the elements O, Zr, Nb, Sn, Hf and Ta were homogenously distributed. The surface ion distribution patterns for elements C, Na, Mg, Si, Ca, V, Cr, Fe, Co and Co were non homogenous. Principal Component Analysis (PCA) was selected for studying the correlation between the distribution patterns of elements. Similar distribution pattern for the groups of elements (Mg, Si, Fe), (Ca, Co, Cu) and (V, Cr) were obtained.