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Phase Composition Analysis of the NIST Reference Clinkers by Optical Microscopy and X-Ray Powder Diffraction.


pdf icon Phase Composition Analysis of the NIST Reference Clinkers by Optical Microscopy and X-Ray Powder Diffraction. (2020 K)
Stutzman, P. E.; Leigh, S.

NIST TN 1441; 49 p. September 2002.

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Keywords:

cements; clinker; composition; optical microscopy; reference material; Rietveld method; x-ray powder diffraction

Abstract:

Certification of the phase compositions of the three NIST Reference Clinkers will be based upon more than one independent method. The current reference values were established using an optical microscope examination, with additional optical microscope data taken from an ASTM C 1356 round robin. The present X-ray powder diffraction (XRD) study provides a second, independent estimate of the phase abundance. Reitveld refinement of the powder diffraction data allowed calculation of a set of best-fit reference patterns and their scale factors. Because of significant contrast in the linear absorption coefficients of ferrite and periclase relative to the estimated mean matrix linear absorption coefficient, the scale factors were adjusted for microabsorption effects. The XRD data generally agree with the optical data with the exception of aluminate. This disagreement may reflect the difficulty in resolving this fine-sized phase using the optical microscope. The XRD data show greater precision than replicate measurements by microscopy. Measurements from different sources, laboratories, instruments, and methods can exhibit significant between-method variability, as well as distinct within-method variances. The data sets were analyzed using both unweighted and weighted schemes to establish optimal consensus values and to provide meaningful consensus uncertainties. While the consensus mean values of individual phase abundance do not vary significantly across methods of combining data sources, the associated uncertainty values do. The Mandel-Paule-Vangel-Rukhin maximum likelihood method of combining the data sets is favored as this method produces a weighted mean whose weighting scheme does not necessarily skew the consensus value in the direction of the large number of XRD values, and it takes between- as well as within-method variation explicitly into account.