A non-destructive x-ray scatter (XRS) approach has been developed, along with a rapid atomic scatter algorithm for the detection and analysis of low atomic-numbered elements in solids, powders, and liquids. The present method of energy dispersive x-ray fluorescence spectroscopy (EDXRF) makes the analysis of light elements (i.e., less than sodium; less than 11) extremely difficult. Detection and measurement become progressively worse as atomic numbers become smaller, due to a competing process called 'Auger Emission', which reduces fluorescent intensity, coupled with the high mass absorption coefficients exhibited by low energy x-rays, the detection and determination of low atomic-numbered elements by x-ray spectrometry is limited. However, an indirect approach based on the intensity ratio of Compton and Rayleigh scattered has been used to define light element components in alloys, plastics and other materials. This XRS technique provides qualitative and quantitative information about the overall constituents of a variety of samples

Ross, H. Richard

English

NASA Technical Reports Server

AN X-RAY SCATTER APPROACH FOR NON-DESTRUCTIVE CHEMICAL
ANALYSIS OF LOW ATOMIC NUMBERED ELEMENTS
H. Richard Ross
Sverdrup Technology, Inc.
NASA John C. Stennis Space Center, MS 39529
ABSTRACT
A non-destructive x-ray scatter (XRS) approach has been developed, along with a rapid atomic scatter
algorithm for the detection and analysis of low atomic-numbered elements in solids, powders and liquids. The
present method of energy dispersive x-ray fluorescence spectroscopy (EDXRF) makes the analysis of light elements
(i.e. less than sodium; < 11) extremely difficult. Detection and measurement become progressively worse as
atomic numbers become smaller, due to a competing process called "Auger Emission', which reduces fluorescent
intensity, coupled with the high mass absorption coefficients exhibited by low energy x-rays, the detection and
determination of low atomic-numbered elements by x-ray spectrometry is limited. However, an indirect approach
based on the intensity ratio of Compton and Rayleigh scatter has been used to define light element components in
alloys, plastics and other materials. This XRS technique provides qualitative and quantitative information about the
overall constituents of a variety of samples.
INTRODUCTION
Qualitative and quantitative determination of the elemental content of a wide variety of samples is vital in
many fields of science, technology, and industry. Energy dispersive x-ray fluorescence spectroscopy (EDXRF) is
the best known rapid, sensitive, and non-destructive technique for identifying unknown elements in solids, powders,
and liquids. Samples that contain high levels of light elements are not amenable to EDXRF detection. Therefore,
this method is not capable of providing reliable estimates of elemental concentrations for low atomic number based
materials. Corrosion and contamination products are often compounds containing low atomic numbered elements
(i.e. oxides, carbonates, nitrates and hydrates), which are costly to detect and time consuming. EDXRF analysis
reveals only heavy elements, therefore failing to represent the bulk composition of the contaminants.
Recently, a non-destructive x-ray scatter (XRS) approach has been developed at the NASA John C. Stennis
Space Center. XRS can provide qualitative and quantitative information about the overall constituents of a variety
of samples. The experimental results presented in this paper, show the versatility and importance of XRS in
analyzing complex low atomic numbered materials.
EXPERIMENTAL CONDITIONS
System
A schematic of the Spectrace Corporation (Mountain View, California) EDXRF system geometry and
components is shown in Figure 1. Developments in the system's hardware, the technique itself, the theory, and
the performance characteristics are mentioned in the literature [1-2].
Operating Parameters
A complete list of the experimental conditions is provided in Table 1. The spectrometer used in this study
was a Spectrace 440 EDXRF Analyzer equipped with a molybdenum target x-ray tube, which operates at 50W of
power. All scatter measurements were made with a 0.0625 inch diameter collimator and were taken for 90 seconds
per sample. In all tests a minimum of 20,000 counts were acquired for each sample, and the counting procedure
was repeated four times. The percent RSD was less than 3 %.
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https://ntrs.nasa.gov/search.jsp?R=19930012995 2020-03-17T06:34:57+00:00Z
Spectrometer Spectrace 440 Energy Dispersive Analyzer
X-ray Tube Molybdenum Anode Target
Excitation Conditions 50 kV, 0.1 mA, Direct Excitation
Count Cycle 90 Seconds Real Time
Dead Time 48 %
Collimator Diameter 0.0625 inches
Pulse Processor Time Constant 12.5 bts
X-ray Path Air
Table 1: Experimental Conditions
THEORY
The presence of Compton (incoherent) and Ra_ieigh (coherent) scatter peaks are routinely observed in x-ray
fluorescence spectra and are considered a nuisance. In fact, scatter is a major source of background which limits
analytical sensitivity and may cause spectral interference [3]. Figure 2 displays the output of a Rhodium x-ray tube
operated at 30kV scattered from a polyester plug. The intense continuum output of the tube scattered from the
polyester plug is apparent. Rhodium characteristic lines appear in the form of the Compton and Rayleigh scatter
peaks. Rayleigh scatter is indicated by characteristic x-rays from the anode of the x-ray tube scattered to the
detector without a change in energy, and Compton scatter is characterized by an energy loss that occurs in the
sample.
When x-ray photons strike a collection of atoms, the photons may interact with electrons of the target atoms
resulting in the scatter of the x-ray photons as illustrated in Figure 3. The scatter of the x-ray photons is caused
mainly by outer, weakly-held electrons. If the collisions are elastic, scatter occurs with no loss of photon energy
and is known as Rayleigh scatter. If the photon loses energy, causing the ejection of an electron, the scatter is
inelastic and results in Compton scatter [ 1,4].
The energy loss associated with Compton scatter results in a predictable change in wavelength of the
radiation given by Eq. 1:
AX = 0.243 (1 - cos ¢), (i)
because most x-ray spectrometers have a primary beam-sample-detector angle of 90 °, 4_ = 90 and cos 4' = 0.
Therefore, the Compton wavelength shift, which is shown in Eq. 2, is known as the Compton Wavelength;
AX =0.0243. (2)
In energy dispersive systems the Compton shift may be more effectively expressed as shown in Eq. 3:
E = 12.396 + 0.0243, when bE = 0.510, (3)
where E is in kV and X is in Angstroms. For example, a molybdenum Compton scatter signal has an observed
energy peak at 16.968 kV, as shown in Eq. 4:
17.478 kV - 0.510 kV = 16.968 kV. (4)
There are two important points to recognize concerning the application of x-ray scatter intensities for
331
measuring light element contributions of a sample; a larger observed Compton scatter is seen from samples with
low atomic number matrices because there is less absorption by the sample; and the ratio of Compton-to-Rayleigh
scatter intensity increases as the average atomic number of the sample decreases, as shown in Figure 4 [6].
Therefore, x-ray fluorescence scatter can provide qualitative and quantitative information about a variety of samples.
Most significantly, the x-ray scatter spectrum signal yields information about the overall sample constituents and
exploits the sensitivity of scatter parameters by combining the estimates of light elements with EDXRF measurement
of the remaining elements. The x-ray scatter system helps discriminate light elements in metal alloys, and also
determines the percentage of each component by using the XRS signal to compute the Atomic Scatter Factor (ASF)
as shown in Eq. 5:
ASF--_(wO(ZO. <5)
i
The ASF includes the following properties: the ASF of an element is the same as the atomic (Z) number of the
element, and the ASF of a compound or a mixture of elements is the mass sum fraction(s) of element i(wi)
multiplied by the Z number of element i, where N=number of elements. The XRS algorithms contain their own
calibration curves, relating the Compton/Rayleigh scatter signal ratio to atomic number and for calculating a given
chemical formula.
EXPERIMENTAL RESULTS
Corrosion and Contamination
Corrosion and contamination often represent critical problems for government agencies as well as industry.
XRS analysis can effectively solve these problems, since compounds containing light elements are often associated
with corrosion and contamination products. EDXRF analysis of contaminant spots on a low-alloy steel housing
reveals iron as the only detectable heavy element. XRS analysis indicate that these spots have an ASF of 20.6.
This information was input into the XRS program which generated the formula Fe203 as the only possibility, thus
revealing the contamination to be simple rust spots and not a more complex contaminant.
The XRS identification algorithm is based upon comparison of net intensities of the selected elements in
the unknown sample with those of the known reference samples. First a "Library" of references must be created
using a set of known samples as shown in Table 2.
Substance Atomic Scatter Factor C/R
Fe 26.0 0.58
Fe-3 O4 21.0 0.89
Fe203 20.6 1.18
FeCO3 16.5 1.65
FeN309 " 9H20 10.1 3.71
Table 2: "Library" of Known References
After choosing the elements to be employed in the identification, each reference sample is measured long
enough (usually 90-120 sec) to make the statistical counting error negligible. The net intensities of the selected
elements (Z > 12) and the Compton-Rayleigh backscatter ratios are calculated and stored in the "Scatter Library"
along with their standard deviations and a formula label of the reference sample.
332
After the library of references is complete, a measurement of the unknown sample can be performed. The
net intensities (I.) of the unknown sample are calculated and a statistic (t,) is created for each of the possible K pairs,
where K is the number of references in the library. The statistic (t,) is shown in Eq. 6:
N
_.l o_(I_)÷o2(I_)'
(6)
where Ix and Ik are the net intensities of the i°' element of the unknown and the kU'reference, respectively; air, and
al_, are the standard deviations of these intensities; and N is the number of intensities measured. The statistic (tO
is a squared Euclidean distance coefficient (dxk), between the unknown sample and the k_ reference, weighted with
the variances of measured intensities. During the actual identification, the program sequentially compares intensities
of the unknown with those of the references, (See Figure 5 & 6) calculating for each pair (unknowns) it's t, value,
and selecting the smallest of them. If the smallest t_value is greater than 90% threshold, then the second smallest
t, value is also retrieved and names of both references are displayed along with the message "possible fit'.
However, both t,'s must also be less than the 99.9 % confidence level threshold, or the algorithm will determine that
none of the references match the sample and will display "no match found'.
Solvent Mixtures
The analysis of hydrocarbon components is of critical importance in the assessment of the integrity of clean
systems used in the National Aeronautics and Space Administration (NASA) Space Shuttle Main Engine (SSME)
Testing Program. These analyses are based on the removal of residues from hardware critical surfaces by means
of flushing the surfaces with approved halogenated solvents. Infrared spectroscopy has been widely used in the
detection of these impurities. However, there are limitations with this technique caused by the strong infrared
absorbance band that are characteristic of these halogenated solvents. Consequently, when analyzed by infrared
spectroscopy, the solvent bands are totally absorbing, which makes the detection of several hydrocarbons in the
infrared spectral region impossible.
XRS spectroscopy has proven to be a significant improvement in the speed and accuracy of infrared
measurements. This procedure is based on the application of hydrocarbons expressed as isopropyl alcohol (IPA)
in chlorofluorocarbon (CFC) 113. Scatter rate ratios were obtained from the calibration mixtures and are tabulated
in Table 3.
% CFC 113 % IPA Molecular Wt. % Hydrogen C/R
ii i
100 0 187.37 0.0 0.6673
80 20 161.92 0.51 0. 8584
60 40 136.47 1.2 1.1835
50 50 123.74 1.9 1.2740
40 60 111.01 2.5 1.5656
20 80 85.55 5.3 2.6341
0 100 60.10 13.42 5. I 118
Table 3: Calibration data and results for hydrogen x-ray scatter analysis of IPA in CFC 113.
The calibration curve for hydrogen (IPA) determination is given in Figure 7. A low atomic number element such
as hydrogen produces very strong Compton scattering. The calibration curve was linear for the range of hydrogen
333
as hydrogen produces very strong Compton scattering. The calibration curve was linear for the range of
hydrogen concentrations studied. The relative standard deviation was found to be 2%, which yields a relative
error in the hydrogen determination of -+ 0.15%.
Alloy Identification
XRS can also be effective in the identification of alloys containing low Z elements. An aluminum alloy
was analyzed by EDXRF and XRS. The EDXRF analysis shows aluminum as the only detectable heavy element.
The XRS routine measured an atomic scatter factor of 12.46, and it is known without using the ASF
identification algorithm that the ASF of pure aluminum is 13.0. The measured ASF of 12.46 is obviously lower
than that of pure aluminum which indicates the presence of a light element. The XRS analysis for low atomic
numbered compounds revealed that the ASF of 12.46 corresponded to the material having a composition of
A1Li0.012, which corresponds to approximately 5% by weight of lithium in the aluminum.
The sample was presented as a flat sheet, therefore no sample preparation was required. The XRS
analysis time was approximately 2 minutes.
DISCUSSION
The determination of light elements by EDXRF analysis is hindered by a number of difficulties. One
of which is the fluorescence yield, which for atomic numbers below 16, does not exceed 0.05. When an electron
transition fills a vacancy in an inner shell there is a certain probability that the emitted x-ray photon will be
absorbed in the atom. That is, the emitted x-ray photon ejects an Auger (secondary) electron in one of the outer
shells. Figure 8 shows a plot of fluorescence yield (_) versus atomic number for K, L, and M x-ray lines. Auger
electron production is a process that is competitive with x-ray photon emission. The Auger yield (1-(o) increases
with decreasing atomic number. Therefore, Auger analysis seems more promising than x-ray analysis, since the
low energy photons can not escape from the sample. Typically, the Auger information comes from a surface
layer approximately 10-20 A in depth. By using the Compton-Rayleigh scatter method with molybdenum k-alpha
as primary x-ray radiation, the critical depth can be as high as in 300 microns in an aluminum matrix. This
critical depth is more representative of the whole sample, and smaller errors will be found when samples are not
perfectly homogenous.
CONCLUSION
X-ray scatter spectroscopy has considerable usage for the detection and analysis of low atomic numbered
elements from hydrogen to sodium. This technique can provide results that are comparable to conventional
infrared and inductively coupled plasma analysis methods.
Covering a wide range of applications, XRS spectroscopy can be used in microelectronics and other
contamination control industries; in disciplines involving natural compounds, such as geology, mineralogy,
archaeology, and biology; in industries using microstructure composites; in art and artifacts fields for
authentication purposes; in material verification; and in environmental problem solving and failure analysis.
Obtaining both, XRS and EDXRF information from a single sample will allow the instrument to provide
simultaneous chemical and mineralogical data that could be used to characterize unknown materials. Utilization
of this information would be indispensable for applications in space exploration such as future missions to the
moon and to Mars. This technique can be used for on-stream analysis, continuous control, improved product
quality, raw materials savings, and automation of industrial processes.
ACKNOWLEDGEMENTS
The author wishes to extend appreciation to Lore' M. Gandy for her editorial assistance and critical
review of this paper. Appreciation is also extended to Chris M. Bourgeois for preparing the samples and for
data analysis support.
334
(1)
(2)
(3)
(4)
(5)
REFERENCES
D. E. Leyden, Fundamentals of X-Ray Spectrometry as Applied to Energy Dispersive Techniques
(Tracor X-ray, Mountain View, California, 1984).
T. B. Johansson, R. E. Van Greiken, J. W. Nelson, and J. W. Winchester, Anal. Chem. 47, 854-
860 (1975).
E. P. Bertin, Principles and Practice of X-Ray Spectrometric Analysis (2_ Ed., Plenum Publishing
Corp., N. Y., New York, 1975).
D. E. Leyden, "Energy Dispersive X-Ray Spectrometry", Spectroscopy 2, 28-36, 1988).
K. K. Nieison, "Application of Direct Peak Analysis to Energy Dispersive X-Ray Fluorescence
Spectra', X-Ray Spectrometry 7, 15-22 (1978).
335
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scattered from a polyester plu¢.
336
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Figure 9:
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Illustration of Campion and Raylei_h scatter.
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RAYLEIGH
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Figure 4: Partitionin_ of Scatter Intensities - The difference between the liEht element
contributions (shaded area) and heavy element contributions (unshaded areas).
337
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339



An x ray scatter approach for non-destructive chemical analysis of low atomic numbered elements

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