Structure Analysis from Powder Diffraction Data: Rietveld Refinement in Excel

Autor/inn/en	Evans, John S. O.; Evans, Ivana Radosavljevic
Titel	Structure Analysis from Powder Diffraction Data: Rietveld Refinement in Excel
Quelle	In: Journal of Chemical Education, 98 (2021) 2, S.495-505 (11 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Evans, John S. O.) ORCID (Evans, Ivana Radosavljevic)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0021-9584
Schlagwörter	College Science; Science Instruction; Spreadsheets; Computer Uses in Education; Chemistry; Physical Sciences; Least Squares Statistics; Equations (Mathematics); Instructional Materials; Data Analysis + Suchen Sie Ihr Suchwort? Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Spread sheet; Spredsheets; Spreadsheet; Tabellenkalkulation; Computernutzung; Chemie; Natural sciences; Naturwissenschaften; Naturwissenschaft; Equations; Mathematics; Gleichungslehre; Lehrmaterial; Lehrmittel; Unterrichtsmedien; Auswertung
Abstract	Powder diffraction is one of the most widely used analytical techniques for characterizing solid state materials. It can be used for phase or polymorph identification, quantitative analysis, cell parameter determination, or even full crystal structure analysis using the powerful Rietveld refinement method. As with much of modern crystallography, the software used for Rietveld refinement is frequently treated as a "black box" that produces often poorly understood outputs. This paper shows how it is possible for students to perform a full Rietveld refinement against experimental powder diffraction data from scratch using a simple spreadsheet like Excel. It starts by reviewing the basic ideas of least-squares fitting a straight line, develops these into fitting simple functions to peaks in simulated experimental data, and then combines these ideas with crystallographic equations to enable Rietveld refinement of the structure of an inorganic material (rutile, TiO2). At each stage, students can self-learn different fundamental aspects and pitfalls of data analysis that are widely reapplicable. The ideas can be taught as an online learning exercise or could be incorporated in a laboratory class where students collect and analyze their own experimental data. (As Provided).
Anmerkungen	Division of Chemical Education, Inc. and ACS Publications Division of the American Chemical Society. 1155 Sixteenth Street NW, Washington, DC 20036. Tel: 800-227-5558; Tel: 202-872-4600; e-mail: eic@jce.acs.org; Web site: http://pubs.acs.org/jchemeduc
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/1/01