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DISVAR93: A software package for determining systematic effects in X-ray powder diffractometry

Published online by Cambridge University Press:  10 January 2013

G. Berti ,
S. Giubbili  and
E. Tognoni
G. Berti
Affiliation:
Dipartimento di Scienze delta Terra, Università di Pisa, Via S. Maria 53, I-56126 Pisa, Italy
S. Giubbili
Affiliation:
Dipartimento di Scienze delta Terra, Università di Pisa, Via S. Maria 53, I-56126 Pisa, Italy
E. Tognoni
Affiliation:
Dipartimento di Scienze delta Terra, Università di Pisa, Via S. Maria 53, I-56126 Pisa, Italy
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Abstract

DISVAR93 is a collection of programs devised to process XRPD patterns with the aim of determining the parameters of systematic instrumentation and sample effects. These effects have an influence on data uncertainty and also accuracy of the adopted models describing diffraction phenomena. Such modeling is carried out through the mathematical X-ray powder-diffraction theory, while parameter optimization is achieved by using the additive property of X2 and constraining the models to converge simultaneously to the same minimum in a restrained Hilbert's space. The package has been designed to allow both user interaction as well as automatic linking of programs managed by one main menu and offer several options to satisfy individual user requirements.

Type
Research Article
Information
Powder Diffraction , Volume 10 , Issue 2 , June 1995 , pp. 104 - 111
Copyright
Copyright © Cambridge University Press 1995

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References

Appleman, D.E., and Evans, H.T. Jr. (1973). “Indexing and least-squares refinement of powder diffraction data,” U.S. Geol. Comp. Centr.Google Scholar
Berti, G., (1994). “Microcrystalline properties of quartz by means of XRPD measures,” Adv. X-Ray Anal. 37, 359–366.Google Scholar
Berti, G., (1993). “Variance and centroid optimization in X-ray powder diffraction analysis,” Powder Diffr. 8, 89–97.CrossRefGoogle Scholar
Berti, G., Di Guglielmo, G., and Marzoni Fecia di Cossato, Y. (1990). “Interpretation of Powder Diffraction Patterns by Numerical and Computer Graphics System,” J. Appl. Cryst. 23, 6–10.CrossRefGoogle Scholar
Berti, G., and Enea, A. (1992). “Disvar: un pacchetto per il calcolo degli effetti sistematici che alterano la posizione del centroide e la varianza dei picchi in diffrattometria di polveri a raggi X. Regole di installazione e d'uso,” Quaderni di Software n° 4, 3–10.Google Scholar
Berti, G., Enea, A. (1991). Book of Abs., 13 E.C.M. Trieste, 26–30 August.Google Scholar
Berti, G., and Palamidese, P. (1990). “Analysis of the CuKß3 x-ray diffraction pattern of YAG (Yttrium Aluminium Garnet), by numerical and computer graphic techniques,” Powder Diffr. 5, 186–191.CrossRefGoogle Scholar
Bevington, P.R. (1969). Data Reduction and Error Analysis for the Physical Sciences (McGraw-Hill, New York).Google Scholar
Delhez, R., and Mittemeijer, E.J. (1975). “An improved a 2 elimination,” J. Appl. Cryst. 8, 609–611.CrossRefGoogle Scholar
Fletcher, R., (1970). “A new approach to variable metric algorithms,” Comput. J. 13, 317.CrossRefGoogle Scholar
Howard, S.A., and Preston, K.D. (1989). “Profile fitting of Powder Diffraction Patterns,” in Modern Powder Diffraction of Reviews in Mineralogy, Vol. 20, pp. 217–272.CrossRefGoogle Scholar
James, F. (1970). “Montecarlo for Particle Physicists,” in Methods in Subnuclear Physics, edited by Nikolic, M. (Gordon and Breach, New York), Vol. IV, part 3, Sec. 6.1.Google Scholar
Jenkins, R. (1989). “Instrumentation,” in Modern Powder Diffraction of Reviews in Mineralogy, Vol. 20, pp. 19–43.CrossRefGoogle Scholar
Langford, J. I. (1992). “The Use of the Voigt Function in Determining Microstructural Properties from Diffraction Data by means of Potteru Decomposition,” NIST (Gaithersburg, MD), Spec. Pub. 846, 110–126.Google Scholar
Langford, J.I. (1982). “The variance as a measure of line broadening: corrections for truncation, curvature and instrumental effects,” J. Appl. Cryst. 15, 315–322.CrossRefGoogle Scholar
Langford, J. I. and Wilson, A. J. C. (1963). “On Variance as a Measure of Line Broadening in Diffractometry: Some Preliminary Measurements on Annealed Aluminium and Nickel and on Cold-worked Nickel,” in Crystallography and Crystal Perfection, edited by Ramachandran, C. N. (Academic, New York), pp. 207–222.Google Scholar
Louer, D., Auffredic, J.P., Langford, J.I., Ciosmack, D., and Niepce, J.C., (1983). “A precise determination of the shape, size and distribution of size of crystallite in zinc oxide by X-ray line-broadening analysis,” J. Appl. Cryst. 16, 183–191.CrossRefGoogle Scholar
Nelder, J. A., Mead, R. (1965). “A simplex method for function simulation,” Comput. J. 7, 308.CrossRefGoogle Scholar
Wilson, A.J.C. (1963). The Mathematical Theory of Powder Diffractometry (Philips Techn. Lib., Eindhoven, The Netherlands).Google Scholar