Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-28T01:38:42.050Z Has data issue: false hasContentIssue false

An Iterative Qualitative–Quantitative Sequential Analysis Strategy for Electron-Excited X-ray Microanalysis with Energy Dispersive Spectrometry: Finding the Unexpected Needles in the Peak Overlap Haystack

Published online by Cambridge University Press:  03 September 2018

Dale E. Newbury
Affiliation:
National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Nicholas W. M. Ritchie*
Affiliation:
National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
*
*Author for correspondence: Nicholas W. M. Ritchie, E-mail: nicholas.ritchie@nist.gov
Get access

Abstract

When analyzing an unknown by electron-excited energy dispersive X-ray spectrometry, with the entire periodic table possibly in play, how does the analyst discover minor and trace constituents when their peaks are overwhelmed by the intensity of an interfering peak(s) from a major constituent? In this paper, we advocate for and demonstrate an iterative analytical approach, alternating qualitative analysis (peak identification) and standards-based quantitative analysis with peak fitting. This method employs two “tools”: (1) monitoring of the “raw analytical total,” which is the sum of all measured constituents as well as any such as oxygen calculated by the method of assumed stoichiometry, and (2) careful inspection of the “peak fitting residual spectrum” that is constructed as part of the quantitative analysis procedure in the software engine DTSA-II (a pseudo-acronym) from the National Institute of Standards and Technology. Elements newly recognized after each round are incorporated into the next round of quantitative analysis until the limits of detection are reached, as defined by the total spectrum counts.

Type
Materials Science Applications
Copyright
© Microscopy Society of America 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

Armstrong, J (2011) Low voltage and low overvoltage X-ray nanoanalysis with field emission electron microprobes and SEMs: Problems in quantitation for first-row transition elements. 2011 AGU Fall Meeting, San Francisco, CA.Google Scholar
Castaing, R (1951) Application of electron probes to local chemical and crystallographic analysis. PhD Thesis, University of Paris, Paris, France.Google Scholar
Goldstein, JI, Newbury, DE, Michael, JR, Ritchie, NWM, Scott, JH Joy, DC (2018) Scanning Electron Microscopy and X-ray Microanalysis, 4th ed. New York: Springer.Google Scholar
Lifshin, E (1974) The use of solid state X-ray detectors for obtaining fundamental X-ray data. Tutorial and Proceedings of the Ninth Annual Microbeam Analysis Society, Ottawa, ON, Canada.Google Scholar
Llovet, X, Pinard, PT, Heikinheimo, E, Louhenkilpi, S Richter, S (2016) Electron probe microanalysis of Ni silicides using Ni-L X-ray lines. Microsc Microanal 22, 12331243.Google Scholar
McCarthy, JJ Schamber, FH (1981) Least-squares fit with digital filter: a status report. In: Energy Dispersive X-Ray Spectrometry, Heinrich KFJ, Newbury DE & Mylebust RL (Eds.), pp. 273–296. Gaithersburg, MD:National Bureau of Standards.Google Scholar
Mengason, M Ritchie, N (2017) Overcoming peak overlaps in titanium- and vanadium-bearing materials with multiple linear least squares fitting. Microsc Microanal 23(3), 491500.Google Scholar
Newbury, D (2005) Misidentification of major constituents by automatic qualitative energy dispersive x-ray microanalysis: A problem that threatens the credibility of the analytical community. Microsc Microanal 11, 545561.Google Scholar
Newbury, D (2009) Mistakes encountered during automatic peak identification of minor and trace constituents in electron excited energy dispersive X-ray microanalysis. SCANNING 31, 91101.Google Scholar
Newbury, DE Ritchie, NWM (2015) Performing elemental microanalysis with high accuracy and high precision by scanning electron microscopy/silicon drift detector energy-dispersive X-ray spectrometry (SEM/SDD-EDS). J Mat Sci 50, 493518.Google Scholar
Newbury, DE Ritchie, NWM (2016) Electron-excited x-ray microanalysis at low beam energy: Almost always an adventure!. Microsc Microanal 22, 735753.Google Scholar
Pouchou, J-L Pichoir, F (1991) Quantitative analysis of homogeneous or stratified microvolumes applying the model PAP. In Electron Probe Quantitation, Heinrich KFJ and Newbury DE (Eds.), p. 31. New York, NY: Plenum.Google Scholar
Reed, SJB (1975) The shape of the continuous X-ray spectrum and background corrections for energy-dispersive electron microprobe analysis. X-Ray Spectrometry 4, 1417.Google Scholar
Ritchie, NWM (2014) Optimizing the dose for energy dispersive electron probe x-ray microanalysis measurements. Microsc Microanal 20(S3), 746747.Google Scholar
Ritchie, NWM Newbury, DE (2013) Designing the optimal quantitative electron probe x-ray microanalysis measurement. Microsc Microanal 19(S2), 12481249.Google Scholar
Ritchie, NWM, Mengason, MJ Newbury, DE (2017) Standard bundles simplify standards-based quantification in NIST DTSA-II. Microsc Microanal 23(S1), 220221.Google Scholar
Ritchie, NWM, Newbury, DE Davis, JM (2012) EDS measurements of x-ray intensity at WDS precision and accuracy using a silicon drift detector. Microsc Microanal 18, 892904.Google Scholar