Peter L Warren, Pamela Y Shadforth ICI Technology, Wilton, Middlesbrough, U.K.

Transcription

1 783 SCOPE AND LIMITATIONS XRF ANALYSIS FOR SEMI-QUANTITATIVE Introduction Peter L Warren, Pamela Y Shadforth ICI Technology, Wilton, Middlesbrough, U.K. 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

2 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 ICDD Website -

3 784 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.

4 785 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 % Fe Cr cu Al Mn Ni Si Ca P MO K S 0 Elements present Calculated as elements Elements present Calculated with oxygen Converted to Oxides Quantitative determination Total % 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 Si Ni Ba S

5 786 Zn Cl Br Al Ti P Ca Fe cu Zr K C polyprop. (CHZ) Total % remainder 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

6 Copyright (C) JCPDS-International Centre for Diffraction Data 1999 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.