Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-27T06:23:44.832Z Has data issue: false hasContentIssue false
  • English
  • Français

Mineral identification by elemental composition: a new tool within PDF-4 databases

Published online by Cambridge University Press:  29 June 2018

T. G. Fawcett ,
J. R. Blanton ,
S. N. Kabekkodu  and
T. N. Blanton
T. G. Fawcett*
Affiliation:
International Centre for Diffraction Data, 12 Campus Blvd., Newtown Square, Pennsylvania 19073, USA
J. R. Blanton
Affiliation:
International Centre for Diffraction Data, 12 Campus Blvd., Newtown Square, Pennsylvania 19073, USA
S. N. Kabekkodu
Affiliation:
International Centre for Diffraction Data, 12 Campus Blvd., Newtown Square, Pennsylvania 19073, USA
T. N. Blanton
Affiliation:
International Centre for Diffraction Data, 12 Campus Blvd., Newtown Square, Pennsylvania 19073, USA
*
a)Author to whom correspondence should be addressed. Electronic mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The ICDD has developed a microanalysis tool to help scientists identify minerals from their elemental analyses, most typically micro-XRF or a microprobe analysis. Many minerals have characteristic elemental profiles that can often distinguish the mineral from others by their composition differences. In Release 2016 ICDD® PDF-4 databases 20 670 unique compositions have been identified out of 45 497 mineral and mineral-related entries. The application utilizes several common features of PDF® databases to enhance correct identification, most notably those formulas are expressed in weight and atomic percent, data sets are classified by mineral nomenclature and structural classifications, and most minerals have associated atomic and molecular structures. These crystal structures are very useful in determining compositional variants and solid solutions. The ICDD has developed algorithms that are analogous to the search/match processes used for powder diffraction identification. Data can be input as either the element or common oxide. To test the algorithm and graphics interfaces we compared results from the microanalysis module to published data from the Smithsonian Microbeam reference mineral collection. The software correctly identified 24/28 minerals by the highest merit score in the algorithm. In two cases, an isoelemental mineral was identified and in two other cases, the specimens had more elements than the reference standards hindering positive phase identification.

Keywords

PDF-4 databasesmineral identificationmicroanalysisEDSXRF
Type
Technical Article
Information
Powder Diffraction , Volume 33 , Issue 2 , June 2018 , pp. 156 - 161
Copyright
Copyright © International Centre for Diffraction Data 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Faber, J., and Fawcett, T. G. (2002). “The powder diffraction file: present and future”, Acta Crystallogr. B: Struct. Sci. 58(3), 325–332.Google Scholar
Faber, J., Weth, C. A., and Bridge, J. (2004). “A plug-in program to perform hanawalt or fink search-indexing using organics entries in the ICDD PDF-4/organics 2003 database,” Adv. X-ray Anal. 51, 183–189.Google Scholar
Fawcett, T. G., Crowder, C. E., Kabekkodu, S. N., and Kaduk, J. A. (2011). “Improved material identification methods through targeted data mining,” Adv. X-ray Anal. 54, 149–161.Google Scholar
Fawcett, T. G., Kabekkodu, S. N., Blanton, J. R., and Blanton, T. N. (2017). “Chemical analysis by diffraction: the powder diffraction file™”, Powder Diffr. 32(2), 63–71.Google Scholar