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Application of the Modified Snyder's Program for the Data Processing of an Automated X-Ray Powder Diffractometer

Published online by Cambridge University Press:  06 March 2019

G. Platbrood ,
J. M. Quitin  and
H. Barten
G. Platbrood
Affiliation:
LABORELEC (Laboratoire de l'lndustrie Electrique) BP 11 1640 Rhode-St-Genese, Belgium
J. M. Quitin
Affiliation:
LABORELEC (Laboratoire de l'lndustrie Electrique) BP 11 1640 Rhode-St-Genese, Belgium
H. Barten
Affiliation:
LABORELEC (Laboratoire de l'lndustrie Electrique) BP 11 1640 Rhode-St-Genese, Belgium
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Extract

X-ray diffraction analysis is an excellent analytical tool. But if a certain quality of the results is needed or if solutions of analytical problems are to be obtained in a short time period, the X-ray diffractometer must be automated and the spectra reduced with dedicated algorithms.

In LABORELEC, three programs are principally used to solve the problems encountered in the X-ray diffraction analysis: a modified program given by R. L. Snyder; the search/match G. G. Johnson program (last version); and the POWD5 program.

Type
VI. XRD Search/Match Procedures and Automation
Information
Advances in X-Ray Analysis , Volume 25: Thirtieth Annual Conference on Applications of X-ray Analysis, August 3-7, 1981 , 1981 , pp. 261 - 265
Copyright
Copyright © International Centre for Diffraction Data 1981

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References

1. Mallory, Chester L. and Snyder, Robert L., The control and processing of data from an automated X-ray powder diffractometer, in “Advances in X-ray Analysis,” 22:121 (1978).Google Scholar
2. Johnson, G. G., “User Guide, Data Base and Search Manual,” Pub. by JCPDS-International Centre for Diffraction Data, Swarth more, PA.Google Scholar
3. Clark, Connie M., Smith, Deane K. and Johnson, Gerald G., “A FORTRAN 4 program for calculating X-ray powder diffraction patterns.” Version 5, The Pennsylvania State University.Google Scholar
4. Savitzky, A and Golay, M. J. E., Smoothing and differentiation of data by simplified least squares procedures, Anal. Chem. 36:1622 (1964).Google Scholar
5. Ladell, J., Zagofsky, A. and Pearlman, S., Cu Kα2 elimination algorithm, J. Appl. Cryst. 8:499 (1975).Google Scholar
6. Marquardt, D. W., An algorithm for least-square estimation of nonlinear parameters, J. Soc. Ind. Appl. Math. 11:431 (1963).Google Scholar
7. Janssens, N., Identification de parametres, LABORELEC (1980).Google Scholar
8. Daniels, Richard W., “An Introduction to Numerical Methods and Optimization Techniques,” Elsevier North-Holland (1978).Google Scholar
9. Goehner, Raymond P., Specplot-an interactive data reduction and display program for spectral data, in “Advances in X-ray Analysis,” 23:305 (1980).Google Scholar
10. Mallory, Chester L. and Snyder, Robert L., New York College of Ceramics, Technical paper No. 144 (1979).Google Scholar