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SCOPE AND LIMITATIONS FOR SEMI-QUANTITATIVE

XRF ANALYSIS

Peter L Warren, Pamela Y Shadforth

ICI Technology, Wilton, Middlesbrough, U.K.

Introduction

Historically x-ray fluorescence spectrometry has been used for elemental analysis in two modes, quantitative and qualitative. The former category was normally the most important to the analyst, and represented the main justification for the considerable expenditure in the technique. However, as XRF is a relative rather than absolute technique, quantitative determinations need matrix matched standards, or suitable reference materials. If these are not available, or if the analytical requirement is limited to identifying the type of sample under investigation, then a qualitative scan is sometimes sufficient. Qualitative scans require a suitably experienced analyst to properly interpret the spectra and identify the fluorescent lines.

Some XRF users developed systems to examine qualitative scans and categorise elements present at major, minor or trace concentrations. However manual interpretation is often slow, inaccurate and person dependent. What was needed was speed and consistency, so it was the advent of powerful personal computers that took this type of analysis one stage further. In ICI various in-house programs had been developed which automated the scanning routines and provided clients with approximate figures for completely unknown samples.

Software Development

In the last few years commercial packages have become available that can be truly described as semi-quantitative. They have become popular for the identification of “one-off’ samples, material classification (eg metal alloy typing), and preliminary screening, where the results can be used to make decisions on further analytical testing. During this time we have established what are the important features to make an SQ program function satisfactorily.

CRITERIA FOR SEMI-QUANT. SOFTWARE

reliable algorithm for element identification

accurate quantification of elements present

range of sample forms eg beads, powders, liquids

good limits of detection

realistic values for “not detected” elements

extend to low Z elements

interactive or automatic modes

organic + inorganic matrices

Copyright (C) JCPDS International Centre for Diffraction Data 1999ISSN 1097-0002, Advances in X-ray Analysis, Volume 41

This document was presented at the Denver X-ray Conference (DXC) on Applications of X-ray Analysis. Sponsored by the International Centre for Diffraction Data (ICDD). This document is provided by ICDD in cooperation with the authors and presenters of the DXC for the express purpose of educating the scientific community. All copyrights for the document are retained by ICDD. Usage is restricted for the purposes of education and scientific research. DXC Website – www.dxcicdd.com

ICDD Website - www.icdd.com

ISSN 1097-0002, Advances in X-ray Analysis, Volume 41

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Experimental Results

The Siemens SSQ program is a typical example of software which has been progressively developed in recent years. It is based on a series of spectral scans, which are optimised for spectral resolution (by choice of crystal, collimator ) rather than sensitivity. Element peaks are identified and background count-rates subtracted automatically. The program applies the theoretical approach of “fundamental parameters” using data from x-ray physics to calculate individual “alphas” (matrix corrections) for each element detected in the sample. The procedure follows an iterative process which finally produces element concentration. Calibration is a “once-off’ procedure, normally performed by the manufacturers.

FEATURES OF S.S.Q.

optimised spectral scans

alternative lines for most elements

interactive - user interrogation of data

background modelling and subtraction

individual “alpha” corrections (Fundamental Parameters.)

range of print-out options

one initial calibration

variants for different modes / sizes

Our experience with this package covers a wide range of sample types and matrices. From a qualitative standpoint, we have found very few false positives (elements detected that are not really present) or negatives (elements missed). The only grey area is near the detection limit, when differentiating between a small peak and detector noise. A threshold based on concentration and/or count statistics distinguishes elements that can assumed to be definitely present, from those below the detection limit.

The best results ie those agreeing most closely with values from reference samples, come from samples whose composition can be fully determined by XRF. That is, composed of elements from F (Z=9 ) to U (2=92) in the periodic table. Typically metally alloys, for instance ,

produce concentrations within 5-10% of the true figure, and total close to 100%. However materials that contain elements not measured quantitatively by XRF eg oxides, carbonates or polymers, need more careful consideration to obtain accurate results. The FF calculations depend on input for the total elemental composition of the material. If a large percentage of oxygen is introduced into the equation, the average atomic number is consequently reduced, which alters the absorption / enhancement characteristics of the sample. Thus if a metal is present as an oxide or carbonate, the calculations will differ from those of the element alone.

Copyright (C) JCPDS International Centre for Diffraction Data 1999ISSN 1097-0002, Advances in X-ray Analysis, Volume 41

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Table 1 shows the effect on the concentrations of an iron oxide sample. The second column shows the initial estimates assuming XFW elements alone are present. In the middle column, 30% oxygen ( the stoichiometric amount necessary) is included in the calculations, and the concentrations of the elements drop considerably. These elements are normally reported as the oxides (column 4) and are in good agreement with the quantitative determination of the major elements (column 5).

IRON OXIDE SAMPLE

%

Fe

Cr

cu

Al

Mn

Ni

Si

Ca

P

MO

K

S

0

Elements present Calculated as

elements

81

7.3

3.1

0.8

0.7

0.4

0.25

0.17

0.08

0.06

0.02

0.02

Elements present Calculated with

oxygen

61

5.5

2.2

0.7

0.5

0.3

0.22

0.14

0.07

0.05

0.02

0.02

30.6

Converted to Oxides

Quantitative determination

87 86

8 8.4

2.8 2.5

1.3 2.2

0.8 0.9

0.5

0.4

0.19

0.15

0.07

0.02

0.04

Total % 94.2 100.8 100.8

Table 1.

The difference made by the “light” elements is quite dramatic when the bulk of the material is organic eg plastics. Information on the non-measured elements is essential for this, and

sometimes other techniques (eg combustion for C/H/N) are needed to give a clearer picture. Again, elements such as C, H, N, 0 are critical to the Fp calculations.

POLYPROPYLENE Elements present Elements present calculated as Accepted SAMPLE Calculated as including carbon polypropylene

Value elements IEltliX

% % PPm PPm

Mg 0.22 0.21 1700 2000

Si 0.18 0.11 670 800

Ni 1.2 0.29 460 280

Ba 0.76 0.19 360 430

S 0.13 0.05 230 250

Copyright (C) JCPDS International Centre for Diffraction Data 1999ISSN 1097-0002, Advances in X-ray Analysis, Volume 41

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Zn

Cl

Br

Al

Ti

P

Ca

Fe

cu

Zr

K

C

polyprop. (CHZ) Total %

0.73

0.07

0.02

0.01

0.06

0.02

0.04

0.01

0.01

0.01

0.01

3.5 78.9

0.18

0.03

0.01

0.01

0.02

0.01

0.01

0.01

0.01

0.01

0.01

77.8

250 250

110 150

70 100

70 100

60 65

40 45

50 40

30 20

30 20

20 50

20 20

remainder 100.00

Table 2.

Results from a doped polypropylene material are shown in Table 2. The second column indicates the values achieved assuming no organic matter present. The middle column illustrates the recalculated figures by including the approximate carbon content (as determined by the XRF SQ program). A final calculation based on a polypropylene matrix (-CH2-) is shown in the fourth column.

The intensity of the Compton scattered tube lines (Rh kal, kbl) give a valuable guide into the validity of these calculations. It provides an indication of the scattering power of the matrix (roughly in proportion to the average atomic number of the sample), and can be compared with the theoretical figure computed by SSQ. Powders and liquids can be analysed with this program, with determinations from sodium up.

ICI EXPERIENCE

* operation is fast, simple

* SQ figures are impressive

* few false positives, negatives

* interactive evaluation for best results

* trade-off with speed v. sensitivity

* suits range of sample types

* need better physical data

* calibration is not a user task

* clients understand results

Copyright (C) JCPDS International Centre for Diffraction Data 1999ISSN 1097-0002, Advances in X-ray Analysis, Volume 41

The software takes into account the nature and thickness of the supporting f&n. Small and thin samples are also catered for, where the amount of material available is insuI%cient to reach the critical thickness. We have achieved good figures with as little as a few mg, albeit with reduced sensitivity and accuracy.

Conclusions

We conclude that the SSQ computer package for semi quantitative XRF analysis is a powerful additional tool for the estimation of elemental composition. Good results have been attained with a wide variety of sample types. The computerised data requires careful interaction with an experienced analyst who can provide additional data and scientific understanding, in order to achieve the best results. The speed with which this multi-element analysis can be produced (normally 20 minutes) is appreciated by our customers. However we have found it necessary to educate our customers so that the numbers produced are not used out of context, or confused with regular quantitative data. It is important that the client is clear exactly what the SQ figures mean.

Copyright (C) JCPDS-International Centre for Diffraction Data 1999ISSN 1097-0002, Advances in X-ray Analysis, Volume 41