Quantitative phase analysis of challenging samples using neutron powder diffraction. Sample #4 from the CPD QPA round robin revisited

Abstract

Quantitative phase analysis (QPA) using neutron powder diffraction more often than not involves non-ambient studies where no sample preparation is possible. The larger samples and penetration of neutrons versus X-rays makes neutron diffraction less susceptible to inhomogeneity and large grain sizes, but most well-characterized QPA standard samples do not have these characteristics. Sample #4 from the International Union of Crystallography Commission on Powder Diffraction QPA round robin was one such sample. Data were collected using the POWGEN time-of-flight (TOF) neutron powder diffractometer and analysed together with historical data from the C2 diffractometer at Chalk River. The presence of magnetic reflections from Fe 3 O 4 (magnetite) in the sample was an additional consideration, and given the frequency at which iron-containing and other magnetic compounds are present during in-operando studies their possible impact on the accuracy of QPA is of interest. Additionally, scattering from thermal diffuse scattering in the high-Q region (<0.6 Å) accessible with TOF data could impact QPA results during least-squares because of the extreme peak overlaps present in this region. Refinement of POWGEN data was largely insensitive to the modification of longer d-spacing reflections by magnetic contributions, but the constant-wavelength data were adversely impacted if the magnetic structure was not included. A robust refinement weighting was found to be effective in reducing quantification errors using the constant-wavelength neutron data both where intensities from magnetic reflections were ignored and included. Results from the TOF data were very sensitive to inadequate modelling of the high-Q (low d-spacing) background using simple polynomials.

Preparation of pyrite concentrate powder from the Thackaringa mine for quantitative phase analysis using X-ray diffraction

“…An overestimation of heavier elements may point to uncorrected microabsorption, despite empirical calibration being used in the XRF analysis, perhaps resulting from a different particle size distribution in the ore sample compared with purchased synthetic standards. Although particle size reduction decreases the effect of microabsorption, and samples were ball milled identically in preparations for both XRD and XRF analyses, microabsorption may still be non-negligible (Scarlett et al, 2002;Whitfield, 2016) and the degree of microabsorption may differ between characterization methods considering the different wavelengths used.…”

The effect of data quality and model parameters on the quantitative phase analysis of X-ray diffraction data by the Rietveld method

“…Microabsorption can only be known to be an issue through detailed prior knowledge of the constituent phases (Scarlett & Madsen, 2018). It can sometimes be mitigated by grinding, by choosing an X-ray wavelength which is equally absorbed by all phases or by using neutron diffraction (Scarlett et al, 2002;Whitfield, 2016). There exists the Brindley (1945) correction, but this should not be used unless the specimen consists of monodisperse, polycrystalline, spherical particles in a size range appropriate for the correction (Cline & Snyder, 1982).…”

“…If no, or only partial, structural information is available, then an alternative process must be sought, such as calibrated hkl files (Taylor & Rui, 1992;Scarlett & Madsen, 2006) or the direct-derivation method (Toraya, 2016). QPA by the Rietveld method has been the focus of many round robins studying Portland cements (Leó n-Reina et al, 2009), minerals (Raven & Birch, 2017;Madsen et al, 2001;Scarlett et al, 2002), clays (Raven & Self, 2017), ceramics (Toraya et al, 1999) and pharmaceuticals (Fawcett et al, 2010;Scarlett et al, 2002), as well as studies investigating the effect of radiation type (Leó n-Reina et al, 2016) and the outcomes of round robins (Peplinski et al, 2004;Whitfield, 2016). There have only been a few studies looking at the effect of data quality on QPA results (Madsen et al, 2013;Uvarov, 2019) and structural refinement results (Hill & Madsen, 1984, 1986.…”

The human factor: results of a small-angle scattering data analysis round robin

“…Such focus allows us to pinpoint the larger contributors to interlaboratory variability, with the eventual goal of eliminating or minimizing such dependencies. Notable examples of such studies have been carried out in fields such as atom probe tomography (Dong et al, 2019); X-ray diffraction (Madsen et al, 2001;Scarlett et al, 2002); neutron scattering (Rennie et al, 2013); neutron powder diffraction (Whitfield, 2016); biology-specific small-angle X-ray scattering (Bio-SAXS) (Trewhella et al, 2022); and surface-area determination following the Brunauer, Emmett and Teller method (Osterrieth et al, 2022).…”