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Deconvolution–convolution treatment on powder diffraction data collected with Cu X-ray and Ni filter

Published online by Cambridge University Press:  29 June 2018

Takashi Ida*
Affiliation:
Advanced Ceramics Research Center, Nagoya Institute of Technology, Asahigaoka, Tajimi, Gifu 507-0071, Japan
Shoki Ono
Affiliation:
Advanced Ceramics Research Center, Nagoya Institute of Technology, Asahigaoka, Tajimi, Gifu 507-0071, Japan
Daiki Hattan
Affiliation:
Advanced Ceramics Research Center, Nagoya Institute of Technology, Asahigaoka, Tajimi, Gifu 507-0071, Japan
Takehiro Yoshida
Affiliation:
Advanced Ceramics Research Center, Nagoya Institute of Technology, Asahigaoka, Tajimi, Gifu 507-0071, Japan
Yoshinobu Takatsu
Affiliation:
Advanced Ceramics Research Center, Nagoya Institute of Technology, Asahigaoka, Tajimi, Gifu 507-0071, Japan
Katsuhiro Nomura
Affiliation:
Inorganic Functional Materials Research Institute, National Institute of Advanced Industrial Science and Technology, Anagahora, Shimoshidami, Moriyama, Nagoya, Aichi 463-8560, Japan
*
a)Author to whom correspondence should be addressed. Electronic mail: ida.takashi@nitech.ac.jp

Abstract

A method to remove small Cu peaks and step structures caused by NiK-edge absorption as well as Cu2 sub-peaks from powder diffraction intensity data measured with Cu-target X-ray source and Ni-foil filter is proposed. The method is based on deconvolution–convolution treatment applying scale transform of abscissa, Fourier transform, and a realistic spectroscopic model for the source X-ray. The validity of the method has been tested by analysis of the powder diffraction data of a standard LaB6 powder (NIST SRM660a) sample, collected with the combination of Cu X-ray source, Ni-foil filter, flat powder specimen and one-dimensional Si strip detector. The diffraction intensity data treated with the method have certainly shown background intensity profile without Cu peaks and NiK-edge step structures.

Type
Technical Article
Copyright
Copyright © International Centre for Diffraction Data 2018 

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