Copyright (C) JCPDS International Centre for Diffraction Data 1999
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
783
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
Copyright (C) JCPDS International Centre for Diffraction Data 1999
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.
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Copyright (C) JCPDS International Centre for Diffraction Data 1999
785
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
%
Elements present Elements present
Calculated as
Calculated with
elements
oxygen
Converted to
Oxides
Quantitative
determination
Fe
81
61
87
86
Cr
7.3
3.1
8
2.8
8.4
cu
Al
5.5
2.2
0.8
0.7
1.3
2.2
Mn
Ni
0.7
0.4
0.5
0.9
Si
Ca
0.25
0.3
0.22
0.8
0.5
0.4
0.17
0.14
0.19
P
0.08
0.06
0.07
0.05
0.15
0.02
0.02
0.02
0.02
30.6
94.2
100.8
MO
K
S
0
Total %
2.5
0.07
0.02
0.04
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
SAMPLE
calculated as
polypropylene
Accepted
Elements present
Calculated as
elements
Elements present
including carbon
%
%
PPm
PPm
Mg
0.22
0.21
Si
0.18
0.11
1700
670
2000
800
Ni
S
0.13
0.29
0.19
0.05
460
360
230
280
Ba
1.2
0.76
IEltliX
Value
430
250
Copyright (C) JCPDS International Centre for Diffraction Data 1999
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Zn
0.73
0.18
250
250
Cl
0.07
0.03
110
150
Br
0.02
0.01
70
100
Al
0.01
0.01
70
100
Ti
0.06
0.02
60
65
P
0.02
0.01
40
45
Ca
0.04
0.01
50
40
Fe
0.01
0.01
30
20
cu
0.01
0.01
30
20
Zr
0.01
0.01
20
50
K
0.01
0.01
20
20
C
77.8
polyprop. (CHZ)
Total %
3.5
78.9
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 1999
The software takes into account the nature and thicknessof the supporting f&n. Small and
thin samplesare also cateredfor, where the amount of material availableis insuI%cientto reach
the critical thickness. We have achievedgood figures with as little as a few mg, albeit with
reduced sensitivity and accuracy.
Conclusions
We conclude that the SSQ computer packagefor semi quantitative XRF analysisis a powerful
additional tool for the estimation of elementalcomposition. Good results have beenattained
with a wide variety of sampletypes. The computeriseddata requires careful interaction with
an experiencedanalyst who can provide additional data and scientific understanding,in order
to achievethe best results. The speedwith which this multi-element analysiscan be produced
(normally 20 minutes) is appreciatedby our customers. However we have found it necessary
to educateour customersso that the numbersproduced are not used out of context, or
confusedwith regular quantitative data. It is important that the client is clear exactly what the
SQ figures mean.