Published December 31, 2019 | Version v1
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Evaluation of crystallographic ordering degree of magnetically active ions in Sr2FeMoO6-δ by means of the (101) X-ray peak intensity

  • 1. SSPA «Scientific-Practical Materials Research Centre of NAS of Belarus», Minsk, Belarus
  • 2. Technische Universität Dresden, Dresden, Germany

Description

Strontium ferromolybdate double perovskite is a promising candidate for room-temperature spintronic applications. Nevertheless, SFMO has not yet found wide application in spintronics. This is attributed to the low reproducibility of its magnetic properties which partially originates from their strong dependence on the ordering degree of Fe and Mo ions in the Bʹ and Bʺ sublattices of double perovskite A2BʹBʺO6.

In this work, we have considered an express method of determining the degree of disorder in strontium ferromolybdate. The sublattice occupation with Fe and Mo ions has been estimated for stoichiometric and nonstoichiometric Sr2FeMoO6-δ with a 5% Fe and Mo excess, respectively. We have calculated the intensity ratio between the superstructure (101) XRD peak and the most intense (112 + 200) peak. The calculated curves have been fitted to an analytical expression of a similar case known from literature. The calculation results obtained using the proposed method are within a ± 25 % agreement with Rietveld analysis of experimental data. Thus, this method can be used as an alternative to Rietveld analysis if the exposure time during X-ray diffraction experiment was insufficient. We have discussed the dependence of the I (101)/I (112 + 200) peak intensity ratio on various factors including instrumental broadening of diffraction peaks, peak twinning due to grain size reduction, thin film lattice parameter variation due to substrate lattice mismatch and lattice parameter variation due to oxygen vacancies.

The relevance of the method is the evaluation of the degree of superstructure ordering in Sr2FeMoO6-δ without large time consumption for X-ray diffraction pattern recording and Rietveld data processing which may be essential when dealing with large amounts of experimental data.

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