Dale E. Newbury, Charles E. Flori, Ryna B. Marinenko, Robert L. Myklebust, Card R. Swytl, and David s.Bright
Microanalysis Research Group Center for Analytical Chemistry National lnstihite of Standards and Technology Gaithersburg. MD 20899
I n this two-part series, Dale E. Newbury and co-workers discuss quantitatiue compositional mapping using the electron probe microanalyzer as a tool for elucidating the chemical microstructure of matter. Part I, which appeared in the November 15 issue (l), discussed the deuelopment of techniques used for wavelength- and energy-dispersiue spectrometries, the general procedure, and correction techniques applied to the mapping of major constituents.
can be successfully mapped. Adequate counts must be accumulated to detect trace levels, and artifacts associated with the spectral background must be corrected to avoid false effects in the apparent contrast of the final compositional map. Counting statistics. The first consideration in quantitative compositional mapping of minor and trace constituents is the optimum level of counting statistics of the X-ray intensity measurement that can be obtained within reasonable instrument parameters. Because thii analysis is for a single-point determination (i.e., the intensity measurement a t a single picture element, or pixel), these statistics refer to the quality of data that can be obtained from a single pixel location arbitrarily selected from the compositional map. The box (above right) shows counting statistics that suggest a relatively poor detection limit for compositional
lNS7RUMENTATION In Part 11, we address the problems involved with counting statistics and background correction for the compositional mapping of minor constituents using wavelength- and energy-dispersive spectrometries. We also discuss the strategy of combining instruments and a few select applications. Compositional mapping ot minor and
trace constituents Corrections for the instrumental artifacts of spectrometer defocusing and collimation effects must be rigorously applied to quantitative compositional mapping of minor and trace constituents. In addition, counting statistia become a significant issue for determining the lowest detection limit that
'
Permanentaddress: Biomedical Engineering Research Branch, NIH, 9wo Roekville Pike, Bethesda. MD 20892-oOOl
mapping. However, the practical detection limits realized are often substantially better than the levels suggested by single pixel counting statistics because the human eye is an outstanding integrator of visual information. If a chemical structure near the detection limit extends over a substantial lateral range of pixels, the analyst may be able to discem it despite the noise that results from statistical fluctuations in the count a t a single pixel. We prefer wavelength-dispersive spectrometry (WDS) over energy-dispersive spectrometry (EDS) for mapping constituents with low detection limits, for several reasons. First, the dead time of WDS is a t least an order of magnitude lower than that for EDS. Second, the limiting count rate in WDS is applied only to the peak of interest; all other X-ray energies are excluded by the diffraction proceea. In EDS, the
ithg slatlstks with 9 accumulation time Relatlve Mandard
devlatlon ( R W
Mass concentrailon (*%)
0.1 (10%) 0.01 (1%)
0.7% 2.2%
0.001 10 1%)
7.1%
limiting count rate is determined by the entire energy range of excited X-rays, extending up to the D u a n e Hunt limit a t the incident beam energy (2). Third,, the signal-to-noise ratio (S/N) of WDS is a t least an order of magnitude greater than that for EDS, a situation that extends the detection limit because the background contribution to the peak energy range is lower. However, a practical consideration when using WDS is the possibility of damage to the specimen that may occur during electron beam bombardment. Radiation damage, including specimen degradation from local heating effects, can occur at the high electron dose rates required to achieve high X-ray count rates with WDS. Loss of mass, migration of certain atomic species, and other forms of degradation are well known in the analysis of biological specimens, and these effects frequently limit the information that can be obtained by electron beam microanalysis (2). The analyst should be aware that even the more robust specimens encountered in materiala science, geology, and other physical sciences may be subject to electron beam-induced degradation, particularly if the specimen has a nonconductive nature. The high electron doses needed to achieve good counting statistics may be incompatible with specimen preservation. Counting Statistics in WDS mapping. Consider the following example (3),in which the beam current is limit-
ANALYTICAL CHEMISTRY, VOL. 62, NO. 24. DECEMBER 15. 1990 * 1246A
ed to 500 nA to retain spatial resolution (which is necessary when relating chemical composition maps to morphological images). It should be possibe to obtain 100 000 countsls from a pure element standard. Such a count rate is within the limit imposed by the dead time of the wavelength-dispersive spectrometer (typically 0.5 ps). If a pixel dwell time of 2 s is chosen (corresponding to 9.1 h of accumulation time for a 128 X 128 map), we can obtain the counting statistics given in the box (previous page) at the indicated concentration levels. These statistics consider only the counts resulting from the peak of interest and ignore any loss of signal from absorption. It is important to note that subtraction of the background, attributable to X-ray bremsstrahlung, from the intensity of the peakof interest willdegrade the counting statistics at the lowest concentrations. Alternatively, if we want to accumulate counting statistics to a relative standard deviation (RSD) equal to 3%, for example, the time necessary to achieve that statistic, as a function of the concentration, is shown in the box (above center). Modem electron probe microanalyzers are equipped with beam current stabilization systems that allow continuous operation in the mapping mode for long periods of time. Mapping is typically done at night, limiting the time available to 12-16 h. Such times are consistent with mapping levels as low as a few hundred parts per million, under favorable circumstances, in images with proper background correction, as described below. Considering that multiple arrays of data fully characterizing a region of a specimen can be collected in this length of time, the investment is worthwhile. The development of a pixel sampling strategy is an important consideration in compositional mapping (4, 5). The analyst has control over the digital density of the data record, typically 64 X 64,128 X 128,256 X 256, and 81 on. Even if data storage space is unlim ited, the number of pixels should be selected carefully. First, the minimum number of counts that should be accumulated in each pixel must be considered. To perform mathematical operations on adjacent pixels (i.e., determining a statistically valid count above zero) or image-processing calculations &e., determining the image gradient), the number of counts in each pixel should be at least eight (4). If a finite number of X-ray counts is accumulated in a large digital array so that a significant number of pixels have less than eight counts, then the array 1246A
.
ume are added in quadrature to give the effective extent of the lateral sampling of the measurement. To determine maximum magnification, first multiply the lateral sampling dimension by the number of pixels in a vector of the scan matrix. Then divide this dimension into the width of the image display. Table I gives an example of the maximum magnification calculation for two different pixel densities using this procedure. Counting statistice! in ELM m a p ping. For EDS, the limiting count rate depends on the energy resolution which, in tum, depends on the pulse processing time (2). For modern EDS systems, the maximum count rate, achieved at the poorest resolution, is approximately 25 000 counts/s. This limiting count rate, however, applies to the entire X-ray spectrum, from the lowest X-ray energy penetrating through the detector window up to the energy of the incident beam, including all characteristic peaks and the bremsstrahlung background. Therefore, it is difficult to make quantitative statements about detection limits in mapping unless a specific mixture of elements is considered. Generally, the count rate that can be obtained from the integrated peak of a minor constituent at 1 wt %, in the presence of large peaks from major constituents, is likely to be only a few hundred m u d s . The RSD for such a peak, even with a 2 s per pixel integration time, will be 5% or higher. The detection of trace constituents in the mapping mode is obviously not practical with EDS. Background correction. In a wellshielded wavelength- or energy-dispersive X-ray spectrometer, the principal source of background noise is the X-ray bremsstrahlung. The bremsstrahlung, or braking radiation, forms a continuum extending from the lowest X-ray energies of interest up to the incident beam energy, and bas an energy and atomic number dependence given by
should be compressed, using nearestneighbor averaging, to obtain an array with at least eight counts per pixel. Second,the relationship between adjacent pixels must be considered. Because of the finite size of the beamand more importantly, the size of the beam interaction volume within the specimen caused by elastic and inelastic scattering-adjacent pixels may not be independently measured for all choices of specimen composition, beam energy, magnification,and digital pixel density. The maximum magnification for independent pixels can he estimated as follows. Modem electron microprobe optics are such that a beam of 0.25-pm diameter should be able to contain 500 nA of electron current at a beam energy of 20 keV (2).We can estimate the lateral size of the interaction volume from an electron and X-ray range equation, such as that of Andersen and Hasler (6)
R (pm) = (O.O64/p) (E;=
- E:
68)
(1)
where R is the range, p is the specimen density, EOis the incident beam energy in keV, and E, is the critical excitation energy for the characteristic X-ray energy of interest. Note that the range, measured perpendicular to the surface, is a conservative estimate of the lateral extent of the interaction and X-ray sampling volumes for most situations. The beam size and the interaction vol-
I Table 1. pixels'
Calculation of maximum magniflcation for independent
Matrlx element (analyle and X-ray noted)
Ranpa (pm)
C (AI-K) AI (AI-K) Cu (CU-K) AU (Au-M)
4.2 3.6 0.81 0.50
ANALYTICAL CHEMISTRY, VOL. 62, NO. 24, DECEMBER 15, 1990
Lateral
Maxlmum
Maxhum
4.2 3.6 0.85 0.55
186 217 919 1420
47 55
230 355
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T
P
Kramers’ equation (7)
where I , is the intensity for a given bremsstrahlung energy E., k is a constant, 2 is the atomic number, and Eo is the incident beam energy. For targets of mixed composition, the bremsstrahlung intensity varies with the concentration-weighted average atomic number. Small et al. have developed an expression for the background (8). The background attributable to the bremsstrahlung sets the ultimate limit on the minimum detectable concentration (2).Because the maximum S I N for EDS is -501 and the maximum SIN for WDS is-5001, the background hai a significant impact on minor anc~ trace-level analysis. Detailed treatments of background subtraction have been developed for conventional analysis to achieve quantitation for minor and trace constituents (2). The wavelength-dispersive spectrometer can be mechanically tuned away from the peak position to measure the background on either side, and the background a t the peak position can then be estimated by interpolation. Alternatively, we can measure the background at the peak position without altering the spectrometer setting by measuring the intensity of a peak on a different element not present in the unknown or standard. The atomic number dependence of Kramers’ equation can then he used to scale this measured hackground to the estimated composition of the unknown, as part of the iteration procedure in quantitativt analysis. EDS has an inherent advantage in the way it treats background noise because the entire spectrum is recorded in every measurement. Background modeling and filtering are two popular methods used for background subtraction. Background modeling makes use of Kramers’ equation to estimate the hackground at the position of any peak of interest from the background measured a t selected energies in the lowand high-energy regions of the spectrum (9).Background filtering considers the spectrum as a frequency distribution in which the characteristic peaks are contained in the midfrequency region. The slowly changing background is in the low-frequency region, and the noise is a t all frequencies but is dominant in the high-frequency region (IO).The original spectrum is modified by a mathematical process (such as the digital “top hat” function) to suppress the low- and bigh-frequency components (the background) while allowing the peaks (the midfrequency 1248A
component) to pass (10). Background correction in quantitative compositional mapping is based on the procedures for conventional single-point analysis, but because of the large volume of data to be processed, there are additional considerations. WDS. Background correction for WDS can be performed by methods that mimic conventional correction by detuning (11).After the X-ray intensity maps of the peak of interest are collected, the spectrometer(s) can be detuned and the scan repeated to collect background intensity maps. However, this procedure is time consuming.
combination
of WDS and
EDS provides the most flexibility in implementing quantitative electron probe compositional mappinr
When considering background collection for trace and minor elements, 10 h or more must he allotted to accumulate data for mapping a t this sensitivity. Clearly, a similar amount of time per pixel must be spent counting with the spectrometers detuned to achieve adequate precision. If only one background measurement is made per analytical point, the mapping time is doubled, and if the spectrometers are detuned above and below the peak, the time is tripled. These time requirements are usually unacceptable. An alternative procedure, when multiple spectrometers are available, is to dedicate one spectrometer to background measurement while the others measure peaks of interest (12). Background values for the spectrometers tuned to peak positions are then obtained by scaling the value measured on the background spectrometer with efficiency factors determined by independent measurements on pure ele-
ANALYTICAL CHEMISTRY, VOL. 62, NO. 24. DECEMBER 15, 1990
ment targets. Although such a procedure avoids the time problems of the previous technique, it still occupies a valuable wavelength spectrometer solely for background measurement. In many situations, the analyst wishes to use all available spectrometers for measuring elemental constituents of interest. Indeed, losing the use of a spectrometer to background measurement may mean having to repeat the scan to measure a remaining constituent. When an irregular surface is mapped, the I d topography can substantially modify the absorption pathlength in different directions, so that the intensity emitted varies according to the spectrometer position. In such a case. transferring the background measurement from one spectrometer to another may not he possible. A third method, based on the compositional relationship described by Kramers’ equation, entirely avoids measuring the background during mapping (13, 14). The atomic number dependence of Kramers’ equation indicates that the background will vary with the composition of the specimen, which can change from one pixel to another. When we consider the variation in intensity of characteristic peaks measured during mapping, we note that the intensity a t a given point can vary because of the local composition and spectrometer defocusing described above. One wonders whether the background also varies because of spectrometer defocusing. Except for the energy region near an absorption edge, the background is a slowly varying function of energy, as compared to a characteristic peak, and the resulting defocusing effect for hackground radiation should be negligible. Experiments have been performed to compare the amount of dkfocusing between a characteristic peak (TiKa) and a nearby background region obtained by detuning the spectrometer on the lowenergy side of the TiKa peak. The intensity map of the characteristic peak showed a significant defocusing artifact, whereas the intensity map of the background showed a negligible loss of intensity from defocusing, amounting to less than 2% across the entire scan field (13). We can correct for the local variation in the background caused by changes in the composition by using the following procedure based on conventional fixedpoint analysis. With this method, an element not present in the unknown is used to measure the background, and Kramers’ equation is used to scale it to the estimated composition of the unknown (13,14).All of the major constit.
.
x.
c 1 *_.
Figure 1. Background correction using WDS. Specimen is AI-CU eutectic alloy. (a) AI compositional map. (b) Cu compositional msp. (c)Sc artifact distribution wim constant background conection. and (d) elimination Of false SC contrast Wldl locBl pixel compositiondependent background correction. Contrast chosen fa each image is such that black-White corresponds to the range 0-maximum concenhation. Image field width is 50 pm.(Adapted from Reference 13.)
uenta, which have the dominant influence in defining the average atomic number, must he measured in this procedure. First, tune the wavelength spectrometers to the characteristic peak positions of all the elements, Ei,in the unknown. All the major constituents, and preferably the minor constituents as well, must be included. Second, with the spectrometers tuned to the peak positions, make a background measurement, Bb, on another element, Ea, with atomic number Za, which is not present in the unknown. Third, record characteristic intensity maps for the unknown on each spectrometer. Fourth, as a first estimate of the background, subtract the single background value, Bb. measured for each spectrometer on element Ea, from the measured characteristic intensity a t each pixel. Apply one of the defocus correction methods to eliminate defocusing and calculate a k-value map with appropriate standardization. The set of k-value maps is used to calculate a first set of compositional maps through application of an appropriate matrix correction procedure: ZAF, @(pr),or inter-
element factors. Fifth, calculate a map of the concentration-weighted average atomic number Z., from the compositional maps. Sixth, calculate a background map for each spectrometer using Bk measured on the reference element Ek, and scaled to find the appropriate background value, Bpix, for the local composition at each pixel. This scaling is done using the multiplicative factor Z.,/Z,, derived from the atomic number dependence of Kramers' equation Bpix = Bb(Z,,/za)
(3)
Repeat the quantitation step using the background appropriate for each pixel. This procedure can be applied iteratively, but in practice the first calculation is sufficiently accurate because the influence of background on major element intensities is relatively small and the value of Z., is not significantly modified after the first calculation. Figure 1illustrates a test of this procedure, using a series of elemental maps for an aligned A1-Cu eutectic. The compositional maps for AI (Figure la) and Cu (Figure l b ) reveal the alter-
nating AI-rich and Cu-rich phases in a lath-like structure. A compositional map a t the wavelength position of Sc, prepared with a constant background correction a t each pixel, shows an apparent Sc component segregated from the Cu-rich phase (Figure IC). Sc is not present in the sample, however, and the apparent contrast is simply an artifact of the atomic number dependence of the background. When the procedure described previously is followed with a compositionally dependent background correction applied a t each pixel, the false Sc contrast is eliminated (Figure Id). EDS. The background correction problem is more significant for EDS because the S/N is a t least a factor of 10 poorer compared with WDS. Therefore, the analyst will encounter serious artifacts, even a t the level of minor constituents, if a proper background correction is not made. Figure 2 shows compositional maps of the same AI-Cu eutectic described above obtained using EDS. An apparent Sc concentration of 1%is observed segregated from the Cu-rich phase. Background correction in EDS can be performed either by background modeling or background filtering. Both techniques produce satisfactory results. Background filtering is preferred for those specimens where the very nature of the measured X-ray spectrum may change with electron dose. Biological specimens, particularly those in the frozen hydrated state, may undergo significant loss of mass during electron irradiation (15). Such loss affects the absolute intensity of the background and may alter the shape of the background spectrum in a way that is difficult to predict and model. Background filtering can be readily applied because the details of the shape of the spectrum are irrelevant. However, a computationally intensive algorithm must be used during the collection of data, or extensive portions of the spectrum must be saved on a channel-by-channel basis for subsequent processing, requiring large data storage. For robust specimens that do not undergo significant damage during electron bombardment (such as those encountered in materials science), the background modeling procedure can be applied. Background modeling requires only that X-ray counts from low- and high-energy background windows in the spectrum be recorded in addition to the regions for the peaks of interest, thus minimizing data storage. The data for each pixel, consisting of the peak intensities and the two background windows, are processed with the FRAME C algorithm to produce
ANALYTICAL CHEMISTRY, VOL. 62, NO, 24, DECEMBER 15. 1990
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Figure 2. Background correction using EDS.
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Specimen is Ai-CU eutectic aiioy. (a) Ai comporitionai map, (b) Cu compositional map. ( c )Sc artifact distribution with constant background correction. and (d) elimination of false Sc Contrast with formal background correction. image fieid width is 44 pm. (Adapted from Reference 13.)
quantitative maps (16).Figure 2 illustrates this approach for the AI-Cu eutectic sample ( 1 7 ) .The apparent level of the Sc component in the map prepared without proper background correction is approximately 1%in the Curich phase, as shown in Figure 2c. With background correction, the apparent Sc component is eliminated, as shown in Figure 2d. Only randomly scattered bright points are observed with contrast expansion. Instrumental strategy for compositional mapping
Because the strengths of one complement the weaknesses of the other, there are several advantaees to emdovine an instrumental strategy that i s i s Goth WDS and EDS (18). WDS offers the advantages of high
sensitivity because of a high S/N and a high limiting count rate. The wavelength-dispersive spectrometer also provides high resolution to eliminate spectral overlap, which is particularly important when a minor or trace constituent is to he measured in the spectral region of a major constituent. The main problem of WDS is usually the limited number of spectrometers available to measure the constituents of a complex specimen. This limitation becomes important when an accurate background correction is necessary for quantitative determination of minor or trace constituents in a complex specimen. Because all of the major constituents, and preferably the minor constituents as well, must be measured to calculate an accurate background correction, having an adequate number of spectrometers to measure all trace, minor, and major constituents simultaneously is often impossible. When only WDS is available, repeated image scans are often required to measure all constituents of interest. EDS has the potential to detect all constituents simultaneously, hut its poor SIN and low limiting count rate on minor and trace peaks result in a poor detection limit in the mapping mode. Interferences are also a major problem with EDS because of its poor spectral resolution, which can cause worse detection limits when a minor peak is located near a major peak. This is especially true in mapping, where the maximum time available per pixel is a few seconds, resulting in relatively poor counting statistics. Despite these limitations, EDS can he used with WDS in a combined mapping strategy. EDS can simultaneously provide measurements of the major constituents because an adequate counting rate can usually be obtained
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Specimen is @met. (a) Ca. EDS: Ib) Mn. WDS: ( c )Ti. WDS; id) Fe. EDS; (e) background channel. EDS: (1) Ai, ED% (g) Mg. WDS; and (hi Si EDS. Contrast Chosen lor each image is such that black-white coreSponds to the range 0-maximum concentration. image fieid width is 125 pm.
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ANALYTICAL CHEMISTRY. VOL. 62. NO. 24. DECEMBER 15, 1990
Flgure 4. Compositional maps of heterogeneous region in a bulk YBaf&Oi superconductor. (a) Cu. (bi Ba. lci Y. and id) IoUII. Image lield width is 223 pm. Speclmn MUIBSY 01 J. BlWdell. NIST.
and the background correction can be done with sufficient accuracy. In favorable situations, without spectral interference, minor constituents can be measured. Wavelength-dispersive spectrometers are thus freed for measurement of trace and minor constituents, especially those for which interferences exist. The major element information obtained from the energydispersive spectrum is used to make the background correction for minor and trace elements measured by the wavelength-dispersive spectrometer following Kramers’ equation method described above. An example of combined WDS and EDS mapping is shown in Figure 3. A complex multielement garnet is mapped with various elements measured simultaneously by one energy-dispersive and three wavelength-dispersive spectrometers.
the ideal 1-2-3 composition dominates the images, sharp deviations due to other phases are observed locally. Because all of the elements are measured separately, or in the case of 0, calculated by assumed stoichiometry, the total a t each pixel also conveys information. Figure 4d shows theanalytical total, including the 0 constituent calculated by assumed stoichiometry. Theanalytical total compositional map shows the existence of voids in the structure. Note that in the case of the 1-2-3 superconductor, the concentration of 0 is of special interest, because variations in 0 have a profound effect on superconducting properties. It is possible to measure 0 directly by electron-excited X-ray analysis, hut significant uncertainties arise because of the strong absorption of the low-energy 0-K X-rays. For the example given, the contrast of the voids against the solid material is much greater than the contrast that might be generated because of actual differences in the 0 content. Compositional modification of grain boundaries. When diffusion occurs along grain boundaries in polycrystalline materials, the passage of the solute element can create positional instability in the boundary, causing it to migrate into the adjacent grains. As the boundary migrates, compositional changes occur, transferring the solute atoms in the bulk of the grains a t rates that greatly exceed the rate of bulk diffusion. This phenomenon, known as
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mapping Microstructure of a high T. superconductor. The new class of high Tc ceramic superconductor materials initially generated great excitement. That excitement has been tempered because the limiting critical supercurrent densities that can be achieved are relatively modest compared with conventional metallic superconductors, at least for ceramic materials in the bulk form. Compositional mapping has characterized the microstructure, directly revealing at least part of the source of these limitations (19). Figure 4a-c shows a series of maps for the three principal cation components of the “1-2-3” superconductor YBaaCuaO;. The heterogeneous nature of the microstructure is easily seen in the chemical contrast in these maps. Although
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Figure 5. Compositional map of Zn in polycrystalline Cu-Zn alloy illustrating diffusion-induced grain boundary migration.
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The compositionalrange hom 0-10 wi % Zn has been assigned to me lull gray scale range (blackwhite). The compositionalong the v ~ c t o rAE (line at the top of the figure) Is pion& on the superimposed graph. where the scale 0-400 Units corresponds to 0-4 wi % Zn and me hOtiz~ntalaxis is the distance hom A to B. Imam field width is
ANALYTICAL CHEMISTRY.
VOL.
62. NO. 24. DECEMBER 15. 1990
1251 A
INSTRUMENTATION
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Liquid Chromatography/ Mass
Flgure 6. Compositional map of Zn in polycrystalline Fe-Zn alloy illustrating diffusion-induced grain boundary migration. Cross-sectional Specimen. la1 Zn map with trace along llm vMtor AB (line at mter of tlgue) crosslr$ the central dark graln. Scale 0- 1500 CDrleSpOndS I O 0-15 W % Zn and th8 halzantal axis is lhe distance trom A to 0. lbl Zn map 01 a different area with the compositional conlra61displayed wiIh mi1Iciat expan. sion to reveal m n e Sloqhtly ~ delicient in Zn. lmaqe lleld wldm Is 50 pm. Specimen counesy 01 C. Handwerker. NIST.
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diffusion-induced grain boundary migration, is of interest because of the possibilities it presents for lowtemperature alloying. Compositional mapping is especially effective for the study of diffusioninduced grain boundary migration, because materials scientists are interested in observing small changes in composition that occur over small distances (20). Figure 5 shows a map of the Zn constituent at a grain boundary in an alloy of Cu and Zn following diffusioninduced grain boundary migration. The compositional structure shows evidence of a discontinuous and periodic boundary migration process, rather than a smoothly operating one (i.e., the composition along vector AB on the map parallel to the direction of motion shows periodic modulation). It is ohvious that the complexity of the structure resulting from diffusion-induced grain boundary migration is such that a limited set of quantitative line traces (which might he the result of a conventional analytical approach) would not adequately describe the process. The compositional map permits the analyst tovisualize the true nature of the structure and select points or vectors to plot numerical concentration values. The value of image presentation of compositional information is further demonstrated in Figure 6. It shows a cross section of an Fe foil after undergoing diffusion-induced grain boundary migration from the migration of Zn (which had been evaporatedon thesurface of the foil) down the Fe grain boundaries. The Zn has locally penetrated several grains completely, except for the large grain at the center of the field, which appears as a black, hexagonally shaped hole. A line trace NO. 24. DECEMBER 15. 1990
across this grain (Figure 6a) shows that, within the central grain, the Zn has been quantitatively rejected, with the modulation resulting only from the counting statistics of the Zn spectrometer background in Fe. In adjacent grains, Zn concentrations as high as 9 wt % have accumulated. The compwitional map reveals that the deposits of Zn on both surfaces of the hexagonal grain are intact. Enhancement of the compositional contrast a t another location in the foil cross section, shown in Figure 6b, shows an interesting decrease and increase (dark-bright) variation of the Zn concentration a t a boundary that crosses the center of the foil.
Flgure 7. Compositional map of Fe In the interaction zone between an AI wlre and an Fe screw in an electrical junction. VariWS z m s are obsewed carapondlng to the nearly pure Fe. Fe-AI IntemXttallic compounds. and nearly pwe AI. The CompOSItim along lhe Y B C ~ MAB Is ploned on me Superimposed graph where the Scale Irom 3000 I O 5000 corresponds to 30-50 W % Fe and the b i z o n t a t mi* is the distance hom A to 8. Image lield width is 500 pm. (Adapted Irom Reference 22.)
Flgure 8. Compositional maps of Ca and AI In neurons (a1 Ca (maximum brlghlness = 7200 ppm) and (b) AI (mxlmum brrghlnass = 500 ppm) occur ~nthe cell body and axonal process 01 neurons Image lleld vidlh 15 250 firn
Structure of intermetallic compound layers i n electrical connections. Electrical connections involving AI wires and steel screws have been observed to undergo catastrophic breakdown in service. The origin of this failure occurs during arcing between loose connectors and is caused by the formation of layers of intermetallic compounds such as FeAh and Fe2Als. These compounds have much higher resistivity than the AI or steel (21.22). The resulting high-resistance junction causes positive feedback, which tends to produce a runaway heating effect. Figure I shows a compositional map of the Fe constituent in the intermetallic zone between the wire (black region) and the screw (white region) of one of these failed electrical junctions. The gray scale image presentation of the Fe compositional map reveals two different intermetallic layers as zones of different Fe content. This observation is confirmed by the quantitative trace across the structure. Trace element distributions i n neurons. Trace levels of Ca and AI appear in neurofibrillary tangle bearing hippocampal neurons of residents of Guam, who suffer from parkinsonian dementia. These trace elements have been revealed by electron probe compositional mapping (23.24). as shown in Figures Ea and b. The localization of Ca (highest brightness is 7200 ppm by weight) and AI (highest brightness is 500 ppm by weight) occurs in the cell body and axonal process of the neurons. Proper correction of the background was critical in mapping these trace concentration levels. Biological specimens present special problems for electron probe analysis because the average atomic number of the material is low and even small contributions of brems-
strahlung from elements heavier than the carbonaceous matrix (e.g., AI, Ca, Fe) can significantly influence the measured background. If not properly corrected, such atomic number effects can manifest themselves as image artifacts of apparent concentrations when indeed the element is not present in the specimen. Biological specimens are also susceptible to significant loss of mass because of electron beam damage. Such loss would also affect the measured background, leading to an incorrect estimate of trace Constituents. Because of these factors, it is not possible to use the background correction procedure based solely on Kramers’ equation, as the sample mass may vary locally and irregularly over the scanned area depending on the electron dose. Moreover, because the absorption pathlength through the specimen depends on relative orientation of the spectrometer, the same spectrometer must be used for both peak and background measurements. The background was therefore corrected by repeating the scan with the spectrometers detuned from the peak positions to measure the background at each image pixel. Although this procedure doubles the analysis time, it provides the most accurate and unambiguous results for trace constituents in the special circumstances that beam-sensitive specimens create. Carclusion
Quantitative compositional mapping with the electron probe microanalyzer involves performing a rigorous elemental analysis at each location of a matrix scan. T o use quantitative compositional mapping, special corrections must be made for instrumental effects and for the specimen-dependent bremsstrah-
lung background. The defocusing of the wavelength dispersive X-ray spectrometer during a scan of the beam can be corrected by stage scanning with a static beam to avoid the defocusing; synchronized spectrometer scanning with the beam scan on the specimen to continually restore spectrometer focus; defocus mapping, in which a homogeneous standard is mapped to provide a direct spatial map of the defocus, which can then be used to create an intensity ratio map; and defocus modeling, which uses a mathematical model of the relationship between a two-dimensional defocus map and a one-dimensional spectrometer peak profile to construct a standard map. The collimation effect of the energydispersive X-ray spectrometer, which occurs at extremely low magnification and large beam deflection, can be corrected by stage scanning with a static beam to avoid the effect, and by collimation effect mapping, in which a map is created by scanning the beam on a homogeneous standard to develop a model of the intensity loss caused by the effect. The background attributable to X-ray bremsstrahlung is dependent on the average atomic number of the target. Strategies for correction of the background during mapping depend on the type of spectrometry that is used. For WDS, the spectrometer measures only a single, narrow-energy channel that contains both characteristic and bremsstrahlung radiation. In the mapping mode of operation, background correction can be performed by instrument-intensive procedures that involve mechanical detuning and repeated synchronized scans. Alternatively, a procedure that uses the known physical dependence of the bremsstrahlung on composition can be used to model the locally varying background in a map from that measured on a standard. A combination of WDS for sensitivity and EDS for elemental coverage provides the most flexibility in implementing quantitative electron probe compositional mapping. With all corrections applied, mapping to levels of 100 ppm can be achieved in the most favorable cases, while mapping of concentrations a t loo0 DDm is achievable in most instances.
ReferNewbury, D. E.; Fiori. C. E.; Marinenko,R.B.;Myklebust.R.L.;Swyt.C.R.; Bripht. D.S. Anal. Chem. 1990. 62.
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Castaing. R.;Deschamps, P.; Philibert, J., Eds.; Hermann: Paris, 1966; p. 310. (7) Kramers. H. A,; Phil. Mag. 1923, 48, Plf "I,I,_
(8)Small, J. A.; Leigh, S. D.; Newbury. D. E.; Myklebust. R. L. J . Appl. Phys. 1987.61,45969. (9) Fiori, C.E.; Myklebust. R. L.; Heinrich, K.F.J.; Yakowitr. H. Anol. Ckem. 1976, 4R 177 ..~
(IO) Schamber, F. H. "Curve Fitting Techniques and Their Application to the Analysis of Energy Dispersive Spectra"; In National Bureau of Standards Special Publication 604, Energy Dispersive X-ray Spectrometry, Heinrich. K.F.J.; Newbury. D. E.; Myklebust, R. L.;Fiori. C. E.. Eds.; NBS: Washinpton. DC, 1981; pp. 193-232. (11) Marinenko. R. B.; Myklebust. R. L.;
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R. B.; Myklebust, R. L.; Bright, D. S., unpublished work. (19) Marinenko, R. B.; Newbury, D. E.; Bright, D. S.; Myklebust. R. L.; Blendell, J. E. In Microbeam Analysis--1988; San Francisco .- . Press: San Francisco. 1988: pp, (18) Newbury. D. E.; Marinenko.
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B.; Cahn, J. W.; Manning, J. R.; Newbury, D. E.; Piccone. T. J. In Character of Grain Boundaries. Yan. M. F.: Heuer. A. H.. Eds.: American Ceramics Society: Columbus. OH, 1983; vni fi nn m - 1 7 . 1, rr. (21) Newbury, D. E.; Creenwald, S. Nat. Bur. Stand. J. Res. 1980.85,429-40. (22) Newbury. D. E. Anal Chem. 1982.54. (20) Butrymowicz, D.
Dalr E. Newbur? ( l o p c r n f r r .group) rcceiwd n H..". degree in mrtallurg? and materialsscience from Lehigh Uniwrsityin 196'9and a D . Phil. in melallurgyand materials science from thP Uniuersity of Oxford i n 1972. He has been at the National Institute of Standards and Technology ( N I S T ) since 1972 and is currently group leader for microanalysis research. His interests include materials characterization by microscopy and microanalysis. Charles E. Fiori (top left,group) obtained a B.A. degree in applied math from George Washington University in 1973. I n 1987 he was president of the Microbeam Analysis Society. His research interests include the application of small computers t o electron column instruments and spectral analysis. Ryna B. Marinenko (bottom right, group) received a B.A. degree from Wells College in 1964 and a Ph.D. in analytical chemistry from Georgetown University in 1974. She joined N I S T in 1972 and became a member of the microanalysis research group in 1974. Her research a t N I S T has included characterization of inorganic alloys and glasses with the electron microprobe for certification as standard reference materials. Robert L. Myklebust (top right,group) received a B.A. degree i n chemistry from Augustana College ( S D ) in 1958and a n M.S.degree in physical chemistry from Iowa State University in 1961. He has worked at N I S T since 1968. His research interests include quantitative electron probe microanalysis and computer-aided analysis. Carol R. Swyt (bottom left,group) received a B.A. degree in physics from Case Western Reserve University in 1964 and a n M.S. degree in physics from John Carroll University (OH) i n 1966. She is a physical scientist i n the biological engineering and instrumentation program at N I H , where she has worked on biological imaging and has developed analytical techniques in electron energy loss and X-ray spectroscopy. David S. BriRht (farright) received his Ph.D. in biophysics from Colorado State University in 1975and has been a research chemist at NISTsince 1976. He joined the microanalysis research group in 1984. His research interests include USin# image analysis techniques to automate instruments, identify diffraction patterns, and enhance and correlate X-ray maps. 1254A
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(23) Garruto, R. M.; FukatPu, R.; Yana i ham. R.; Carleton Gajdusek, D.;Hook, Fiori. C. E. Proc. Not. Aeod. Sei. ( U S A ) 19R4,81.1875-79. (24) Garruto. R.M.; Swyt, C.; Yanagihara, R.; Fiori, C. E.; Carleton Gajdusek, D. New Eng. J . Med. 1986,315,711-12.
8,
CORRECTIONS Isothermal Titration Calorimetry: Direct Thermodynamic Characterization of Biological Molecular Interactions. E r nesto Freire, Ohdulio L. Mnyorga, and M a r t i n S t r a u m e (Anal. Chem. 1990,62.950 A-959 A). Acknowledgment of NIH grants RR-04328,GM-37911, and NS-24520, held by Ernest0 Freire, t h e principal investigator for this work. WBS inadvertently omitted from this article.
The New Generation of Measurement. Lockhart B. Rogers (Anal. Chem. 1990. 62, 703 A711 A). On p. 711 A, Reference 48 should read Ackerman, S. Amer. Sei. 1989, 77,19-23.
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