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Digital Image Processing in Ion Microscope Analysis: Study of Crystal Structure Effects in Secondary Ion Mass Spectrometry J. D. Fassett and G. H. Morrison* Deparfment of Chemistry, Cornel1 University, Ithaca, New York 14853
Methodology is developed to make multielement, multifeature measurements from secondary ion images by digital image processing. Microfeatures within the image field are identified by thresholding or edge detection. Related images are registered such that ion intensity measurements can be made from comparative regions. Correlations are dlsplayed by the construction of ion ratlo images. The methodology is applied to the analysis of polycrystalline iron. The effect of fundamental instrument parameters on the production of intensity contrast from the crystal grain structure is investigated.
Secondary Ion Mass Spectrometry is an extremely powerful analytical technique because of its high elemental sensitivity, its micrometer sampling scale, and its ability to provide compositional morphological information from solid samples (I). T h e ion microscope is a secondary ion mass spectrometer of specialized design ( 2 )t h a t is capable of imaging the secondary ions of a particular mass emitted from a region of the surface of a specimen of less than 0.05 mm2 with one-micrometer point-to-point resolution. Photographic recording of t h e image results in a spatial analysis of a surface region of a specimen for a single element. A series of ion images spanning differing ion species results in a compositional spatial analysis of t h e surface of the sample. T h e power of ion microscopy rests in its ability to simultaneously collect chemical information from a field of a solid sample which may contain a number of component microstructures. T h e identities and composition of microstructures can be ascertained in an ion microscopic analysis as well as t h e relationships between microstructures within the image field. An ion microscopic analysis produces a large amount of multidimensional information, both spatial and elemental. This large amount of information can be only qualitatively assimilated by visual interpretation. Quantitative evaluation of ion images requires the ability t o quantitatively evaluate each image picture element and to combine and reduce the sum of image information to its basic components. By means of a microphotodensitometry and digital image processing system t h a t has been previously described ( 3 ) ,such quantitative evaluations are now possible. Digital image processing is now routinely used in satellite reconnaissance, light microscopy, electron microscopy, astronomy, as well as many other fields that produce large amounts of photographic information (4-6). In chemistry, digital image processing has been successfully applied to electron density mapping in X-ray crystal structure determinations (7). Another recently reported example describes t h e production and manipulation of images produced by a video fluorimeter where t h e images display t h e “excitation-emission matrix” fluorescence spectra of multicomponent samples produced at different excitation energies (8). Digital image processing shares many of the characteristics of pattern recogition (9),which has been applied to a large number of multivariant problems in analytical chemistry (IO, 1 2 ) . The four elements of pattern recognition: 0003-2700/78/0350-1861$01.00/0
display, preprocessing, supervised learning, and unsupervised learning are all contained in image processing methods. Digital image analysis, per se, has yet to be applied to compositional morphological information that is produced by ion microscopy or that can be produced by the related scanning techniques of X-ray microprobe, Auger microprobe, and ion microprobe. Methodology is described in this study that utilizes image processing techniques to handle the chemical information contained within ion micrographs. Processing methodology in image analysis as developed and applied in other fields is, in general, not directly applicable to the analysis of compositional morphology. A primary concern of image processing with the “quality” of an image where quality is subjectively defined and includes a priori information about the image is only of peripheral intereat here. The primary concern with the image processing of the spatial compositional information in ion micrographs is the correlation of structure and composition. Thus, t h e image analysis methodology is more concerned with the identification of microfeatures within t h e image field and the absolute measurements of ion intensities of t h e microfeatures. As an exemplary demonstration of the image analysis methodology t h a t has been developed, a specific problem is studied: the effect of crystal structure in secondary ion microanalysis. Crystalline effects in ion sputtering have been observed for some time (12-14) with ion sputtering also being aptly described as ion etching. Qualitatively, ion images of polycrystalline samples have shown significant intensity contrast between grains (15, 16). Studies of single crystals have demonstrated that this contrast effect is due to the relative orientation of a crystal face to the ion beam (16-19). Both secondary ionization yield and sputtering yield are dependent upon the relative orientat ion of a crystal grain. This effect has obvious adverse ramifications for achieving quantitative analysis of polycrystalline material. T h e amelioration of the effect has been reported by the surface flooding of the sample with oxygen during sputtering (15-20). Ion microscopic imaging of a polycrystalline sample allows the simultaneous observation of differently oriented grains and the effect of experimental conditions on the secondary ion emission from these grains. In this study the experimental conditions varied were relative sample orientation, surface flooding of the sample by oxygen during the analysis, primary ion gas species, and primary ion gas polarity. Previous studies of crystal effects have been of Cu (13,16), Cr (20), A1 (16, 17,19), Ge and InSb (;?I), a Cr-Ni alloy (13), and a Pt-Rh-W alloy (22). This study was made with high purity 5-9 s Fe.
EXPERIMENTAL Instrumental. A CAMECA IMS-300 Ion Microscope of a design originally developed by Castaing and Slodzian ( 2 ) and previously described in the literature ( I ) was utilized. The primary Ar+, or 0- with primary ion energy of 5.5, 5.5, ion beam was 02+, and 14.5 keV, respectively. The primary ion current was less than 1.0 FA rastered over a 300 pm X 300 Fm area. The instrument vacuum was typically lo-‘ Torr. 0 1978 American Chemical Society
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ANALYTICAL CHEMISTRY, VOL. 50, NO 13, NOVEMBER 1978
The ion microscope has a field of view of 260 pm or less with a spatial point-to-point resolution of about 1 km. The image spatial information is simultaneously produced, energy and mass filtered, and simultaneously recorded. Detection is achieved by conversion of the secondary ion image to an equivalent secondary electron image which is recorded on electron-sensitive 35-mm film. This film is normally processed. Digital Image Processing. The digital imaging system and computer software and hardware have been described previously (3). Ion images produced are digitized using a Photomation Mark I1 scanning microphotodensitometer into 256 X 256 point densit! arrays. These digital images in optical density space are converted into ion intensity space by the determination of the characteristic curve of the film. All image processing was done on a PDP-11/20 computer and involved manipulations of the 65 536 data points within each image file. Experimental Procedure. The procedure applied in each experiment was similar. A specific area of the specimen was imaged. The area was randomly chosen and differed from experiment to experiment. The sample was presputtered to remove the immediate surface and polishing artifacts. A series of images was taken, typically spanning several masses of interest, for the experimental parameter varied. In each case the experiment was completed by the imaging of the specimen under the original conditions. Since ion sputtering is a dynamic destructive process, this step ensured the continuity between the initial and final states of the specimen.
RESULTS AND D I S C U S S I O N M u l t i f e a t u r e M e a s u r e m e n t s . T h e correlation of structure and composition requires the identification of structures within the image field in order that measurements of ion intensity can be made for those features. Structure, or the presence of microfeatures within the image field, may be determined compositionally from ion microscopy alone, or by alternative microscopic techniques that highlight this structure. Ion microscopy can initiate form and function analyses by the identification of compositional structure or ion microscopy can provide compositional analysis of known structures. T h e ability to differentiate a feature prior to ion image analysis represents a n example of supervised learning. A feature which is defined by its form and possible function can be specifically ion microscopically analyzed. The identification of the feature or features in the ion micrograph is supervised by the presumptive knowledge about the specimen. T h e ion images can be correlated with light microscopic or other microscopic images. T h e alternative type of feature analysis of ion images is representative of unsupervised learning. Here t h e features are defined in the ion microscope analysis by the intensity contrast in a single ion micrograph, or by the combination of information from several ion micrographs of a single field region of the specimen. The analysis is nonpresumptive and decisions concerning t h e form and function of the features can only be made a posteriori the ion microscopic feature analysis. The advantage of the ion microscope relative to the ion microprobe is this ability to make a posteriori analyses through ion imaging since presumptive knowledge in the positioning of the probe is requisite in any probe analysis. A previous study on the characterization of the heterogeneity of microstandards (15) illustrates an important application of this type in imaging analysis. A typical ion microscopic analysis consists of a series of related images from t h e same area of t h e surface of the specimen. The identification of the features within the image area field is the first step in the analysis and is done with only the ion images or with the ion images and supplementary information about the region of t h e sample. A feature map characteristic of the image series is constructed where the feature map is an artificial image in which the features are differentiated and arbitrarily labeled. T h e characterization
Figure 1. Edge map for image series depicted in Figure 2
of the features in each ion image is accomplished by the registration of the feature map with each image and the analysis of corresponding regions. T h e method of construction of t h e feature map and identification of the features is dependent upon the nature of the field-feature interaction. Intensit,y, shape, size, and contiguity are ail programmable characteristics which may he used to identify and define features. The most trivial case of feature analysis consists of a simple background - foreground thresholding decision. where features are dispersed within a homogeneous background phase. T h e identification of inclusions within steel is readily accomplished by this method (231. The delineation of the ion image within the digital image file is also simply accomplished by this method. A more complex method is required for homogeneous features that do not have a constant background. The differentiation of the crystal grains, the features of interest in the analysis of polycrystalline iron, is an example of this case. The crystal grains can be bounded by grains of lesser, great>er, or equal intensity. The method developed to differentiate the grains, identified the grain boundary regions. or edge regions, which surrounded each grain. T h e grain boundary regions are characterized both as regions of changing intensity between two grains and as regions of topographical enhancements. The features were identified as the contiguous regions defined by the determined grain boundaries. The determination of the grain boundaries is accomplished by the application of a particular image analysis edge detection routine (24) on one of the images in the series. Edge detection is a well studied problem in image processing (25) and the routine used is but one example. T h e method involves the convolution of an image with a series of compass gradient masks which perform a two-dimensional discrete differentiation of the image. For each point within the image, the direction (within the eight compass directions) and magnitude of maximum change are determined. A point is defined as an edge point if it satisfies both gradient thresholding and connectivity criteria. T h e connectivity criterion limits edge points t o those with a t least two neighboring, directionally related edge points. T h e thresholding criterion requires a minimum magnitude gradient to be surpassed for a point to be defined as an edge point. T h e gradient threshold is established by the experimenter who strives to produce the most, proper edge point image. T h e result of edge detection is the construction of a bit map which defined each point in the original image as an edge point or not. In this particular study: the image used to produce the edge map was chosen as the one with the most obvious contrast. The digital image was smoothed prior to edge processing by two-dimensional box-car averaging. The edge map produced was also smoothed by the rejection of noncontiguous edge regions. Figure 1 illustrates the edge map produced for the
ANALYTICAL CHEMISTRY, VOL. 50, NO. 13, NOVEMBER 1978
r
te
RESIDUAL PRES SURE 8.x10-8 torr
1863
-
Fe2
Figure 3. Feature maps for image series in Figure 4: unrotated and rotated files. Density is scaled lightest to darkest for features which are arbitrarily labeled 1 to 6
Figure 2. Effect of leaking oxygen into sputtering chamber on "Fe' and 112[Fe2]+ion images. Pressures represent no leak and maximum practical leak conditions. Times of exposure are denoted in seconds
image series displayed in Figure 2. T h e actual feature map is constructed from the edge map by the identification and labeling of the contiguous non-edge regions. If the grain boundary regions were identified as the feature of interest, the edge map would suffice as the feature map. Registration. T h e comparison of the ion images with the feature map or with each other requires registration or correlation of the image fields. There are three aspects of the registration problem: (1)registration of images taken of the same area of the sample, (2) registration of images taken after movement or reorientation of the sample, and (3) registration of images of the same area of the sample taken a t different magnifications. All three registration problems were encountered in this study. T h e registration of images taken of the same area of the sample is required because the images can be expected t o be slightly translationally offset relative to each other within the image file because of the manual positioning of the microphotodensitometer during acquisition. This offset is calculated by the determination of the centers of the circular ion images within the square image files. These centers define coincident points for the images. If the sample is moved, the centers of the ion image fields within the square image files will not define coincident points. Coincident regions between images can be rotationally offset as well as translationally offset within the circular image field. One experiment done in this study investigated the effect of orientation of a polycrystalline region of the iron specimen relative to the primary beam. T o compare these images before and after rotation required the determination of both t h e rotational and translational offset. The rotational and translational offsets can be analytically determined by applying the cross-correlation function to similar images (3,26). Accurate determination of offsets can be accomplished by cross-correlating multiple subsections of one image with subsections of t h e other image. Each cross-correlation is an iterative routine that converges to the region with the best correlation. Convergence requires that
Figure 4. "Fe' and 112[Fe2]*ion images at two orientations: top is orientation I, bottom is orientation 11. Times of exposure: "Fe', 20 s; 112[Fe2]+,50 s
the initial estimate for the offset of the two regions be within a minimum distance of the actual offset. T h e result of each cross-correlation is two coordinate pairs: the coordinate pair of t h e best matching subsection in one image and the coordinate pair of the reference subsection of the second image being matched. Determination of t h e translational and rotational vectors to orient the two images can be done by an iterative least-squares routine utilizing the coordinate pairs of the multiple cross-correlations. In actual practice, this method of determining the rotational and translational offsets is extremely time consuming and requires a relatively accurate initial input of the offsets. Thus, for the experimental images in this study, the offsets were determined by the overlaying of transparencies of contour maps of the related images and measurement with protractor and ruler. Once t h e rotational and translational offsets are known, registration is achieved by transposing one image d a t a file relative t o the other data file by the proper rotational offset, creating a rotated image file. This step is necessary because of t h e structure of the image data file as records, each con-
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ANALYTICAL CHEMISTRY, VOL. 50, NO. 13, NOVEMBER 1978
Figure 5. Image data file demagnified (0.85X) and magnified (1.15X): file sizes 256 X 256; negative contrast. Original image data file is of the 112[Fe2]+ image in Figure 4. T h e digitized image data are scaled from an ion intensity of 0.5 (darkest output) to an ion intensity of 0.1 (lightest output)
taining a line of data. Translational offset does not require this step because the offset can be made without crossing record line boundaries. Figure 3 illustrates the effect of rotation on the feature map file or the image series of Figure 4. T h e third type of registration problem results from a change in magnification in t h e microscopy of an area of the sample. In a typical ion image analysis, the microscopic conditions are not changed and this problem does not occur. However, if correlation is desired with light or electron micrographs, such a registration problem will be bound t o occur. In this study, t h e microscopic conditions were changed in one ion imaging experiment which resulted in a change of magnification for a particular region of the sample. A region of the polycrystalline iron was imaged using both positive and negative primary ion beam while collecting positive secondary ions. For t h e CAMECA instrument, a change in magnification results from t h e switch in polarity of the primary ions. Magnification of an image file is accomplished by eliminating edge points and expanding t h e information in the remaining points. Thus, one picture element is expanded into M2 picture elements, where M is the magnification factor. The amount of information lost from the original image file is (1 - 1,'W). Demagnification of a n image file is accomplished by contracting the information in the original file and adding background edge points. Potential information is lost in the contraction process by t h e loss of point-to-point resolution. Figure 5 illustrates t h e result of magnification and demagnification of the Fez+ image in Figure 4. Feature Analysis. Once a feature is identified, relevant descriptors of that feature are calculable. The most important descriptor in ion image analysis is average feature intensity and this descriptor is calculated by the determination of the integrated intensity and integrated area for the feature. There exist other descriptors of features that are potentially useful in certain analyses, such as shape and form descriptors, gradient descriptors, and noise or texture descriptors. These descriptors, once software defined, can be calculated in a feature analysis. A second aspect of feature analysis is field analysis which explores the interrelationships between features in the image field. Automatic image analysis is both a commercially and scientifically established field that has developed its own mathematical and statistical measures for evaluating microstructures in microscopic fields (27,28). These measures are also derivable from ion micrographs with the inclusion of t h e appropriate software procedures. I m a g e Ratioing. T h e visual display of correlation or noncorrelation between similar images is possible by the creation of artificial ratio images where each data point in one
Figure 6. Ratioed image data files, different ion images of the same sample area: (left)72[Fe0]+/56Fe+, scaled from 0.03 (darkest output) to 0.01 (lightest output); (right) 112[Fe2] '/j6Fe+ scaled from 0.5 (darkest output) to 0.1 (lightest output). 56Fe' and 112[Fe2]'images are pictured in Figure 4; 72[FeO]fimage is also in this series
..I
i
. L -
Figure 7. Ratioed image data files, same ion image at two different Images orientations: (left)56Fe'/56Fe+;(right) 112[Fe2]'/1,12[Fep]'. are pictured in Figure 4; ratioed images are of orientation I1 divided by orientation I, scaled from 1.5 (darkest output) to 0.5 (lightest output) image is ratioed to a corresponding data point in a second image. If the two images have strong correlation, the amount of displayed contrast will be reduced; for noncorrelation, the amount of contrast will be increased. Contrast caused by topographical enhancements can be markedly reduced in the ratioing of correlated ion micrographs. Illustrative examples are shown of the polycrystalline iron experiments in Figures 6 and 7. T h e display output of digital image files, whether primary data files or artificially constructed data files, is by means of an electrostatic plotter (Model 900A, Versatec) programmed to display half-tone images, 256 X 256 in 16 gray levels, or 128 X 128 in 64 gray levels. This display mode allows the immediate display of the results and immediate feedback for the optimization of any image processing routine.
POLYCRYSTALLINE IRON IMAGING Elemental Correlations, A series of ion images was taken which spanned the major secondary positive ion species produced from a particular area of the surface of the iron specimen a t two orientations relative to a positive oxygen primary beam. This experiment investigated the effect of both ion species and orientation on the ion intensity contrast produced from the randomly oriented crystal grains in the field of view. The images of jGFe+and "'[Fe2]' at the two different orientations are pictured in Figure 4. The feature map derived for this series of images is in Figure 3 and Table I summarizes t h e results of the feature analysis. T h e correlation of molecular ion species' images with the elemental iron ion image indicates that correlation exists between "Fe+ and "[FeO]+ images and between 11z[Fe2]+ and
ANALYTICAL CHEMISTRY, VOL. 50, NO. 13, NOVEMBER 1978
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Table I. Feature Mass Analysis
feature exp. mass ' 6 0
'"Fe ',[FeO]
I
orientation 300 I (s)
20
200
[Fe, 1
50
26[Fe,0]
50
i I
~
I I1 I
I1 I I1 I I1
Characteristic curve: Dc = 0.896
t
_
intensities
_
I
I1
0.68 94.
0.52 78. 86.
_
_
I11 0.82 120.
~
1.8
1.8
2.1
0.39 96. 96. 1.7
1.6
1.6 34. 29.
1.8
1.7
21. 19. 15. 9.3
14. 14. 9.0 7.7
105.
30. 24. 17. 9.9
118.
16.
12.
1.033 ( t . r / l O O ) . I(Fe,,)
'2S[Fe,0]+ but there is anomalous noncorrelation between monomer and dimer iron images. This anomalous behavior is contrary to published results for aluminum and copper sputtered with an Ar+ primary beam where atomic and diatomic species correlate closely (16). If the ion intensities of the iron image are examined in light of the qualitative sputtering yield information available from topography in the image, it is seen that the ion intensity is inversely related to sputtering yield. This result is in agreement with the analyses of Bernheim and Slodzian (29) who maintain that the ionization yields under oxygen bombardment depend mainly on the chemical effect induced by the oxygen concentration built u p in the sample by the implantation of the bombarding ions. Since t h e higher is t h e sputtering yield, the lower is the implanted oxygen concentration, and the lower is the ion intensity. Thus, the anomalous lack of correlation between j6Fe+ and 112[Fe2]+ images is explainable by the differences in ionization mechanisms: Fet emission is controlled by the chemical effect, Fe2+emission is not. T h e variations in the measured Fey+intensities are closely related to the variations in sputtering yields. Visual display of ratioing t h e "[FeO]+ and 112[Fe2]+ images to t h e j6Fet image, examples of correlation and noncorrelation, respectively, is illustrated in Figure 6. T h e second part of the experiment investigating the effect of rotating the sample and thus reorienting the randomly oriented grains relative to the primary beam caused little effect on the relative contrast on the 56Fet image. However, the "*[Fe2]+image a t one orientation did not correlate with the 112[Fe2]timage a t the second orientation. Visual display of the results of this orientation change is illustrated by j6Fet and ""[Fe2]+ ratioed images in Figure 7 . Oxygen Flooding. Oxygen flooding has heen shown to reduce or eliminate the contrast produced by crystalline samples (15-20). It has been hypothesized that t h e oxygen produces a n amorphous compound on the surface while sputtering is taking place ( 2 7 ) . T o investigate the effect of oxygen flooding on the relative contrast in a polycrystalline specimen, a series of images of 56Fe+and "*[Fe2]+was taken a t different partial pressures of oxygen leaked into the sputtering chamber. Representative images obtained a t a residual pressure in the sputtering chamber S X Torr, and a t 1 X 1U Torr of oxygen in the sputtering chamber are illustrated in Figure 2. T h e relative effect of differing oxygen pressures on two representative crystal grains is graphically presented in Figure 8. Qualitatively the variation of secondary ion intensities for both %Fe' and "'[Fey]+ is in good agreement with the results of Macnonald and Martin (20) and Benninghoven and Muller (30)who studied Cr. 'The reduction in contrast is more rapid for t h e 56Fe+ signal relative to the 112[Fe2]+ signal, again indicative of the importance of the chemical effect on Fe+ ionization. T h e reduction and homogenization of the Fe2+
=
-
IV
V 0.67
78. 96. 1.7 1.7
21. 33. 12. 15.
-
-
VI
0.85 130. 147. 2.6 1.8
47. 27. 26. 12.
1.00.
I
L
I 10-7
I
10-6
J
10-5
0, Pressure (torr) Figure 8. Effect of leaking oxygen into sputtering chamber on the measured intensities from two of the crystal grains in the image field of Figure 2 . Partial display of information extracted from the image series by digital image processing. Grains are located at 12 o'clock ( 0 )and 9 o'clock (0).Actual ratio of intensity scales for "Fe+ to 1'2[Fe,]f is 100 to 1
intensities presumably parallels the reduction and homogenization of the sputtering yields from the different grains (17). O t h e r E x p e r i m e n t s . Experiments were also done investigating the polycrystalline contrast produced by a positive argon primary beam, with and without the oxygen leak, and investigating the relative effect primary ion beam polarity has on the analysis. With argon bombardment, the Fe+ and Fez+ intensities were well correlated, in agreement with the previous discussion and the results of Bernheim (16). The effect of the oxygen leak was qualitatively similar to the effect of the oxygen leak with a n oxygen primary beam, (although less severe a t similar pressures. Changing the polarity of the primary ion beam results in a change in the angle of incidence and energy of the primary ion beam at the surface of the specimen (positive primarypositive secondary: 72' and 4.5 keV; negative primary-positive secondary: 36' and 14.5 keV). For the CAMECA instrument, the predominant nature of the primary oxygen beam also changes from the dimer ion in the positive mode to the monomer ion in the negative mode. Bernheim has investigated t h e effect of changing the angle of incidence and energy of a primary argon ion beam without changing the polarity and observed large modifications in the contrast (16). This observation was verified in this experiment. The polycrystalline contrast produced in the negative primary-positive secondary ion images was less severe than in the positive primary-positive
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ANALYTICAL CHEMISTRY, VOL. 50, NO. 13, NOVEMBER 1978
secondary ion images. The relative contrast of the images produced in the two modes showed poor correlation, as expected.
CONCLUSIONS T h e application of digital image analysis to the specific problem of the effect of crystal structure in secondary ion microanalysis is presented in this study. Although this problem has been approached before, ion microscopy provides a unique dimension in its elucidation and interpretation. The simultaneous analysis of a field of microfeatures by means of image processing allows the important comparison of relative effects of experimental conditions on secondary ion yields. Since the goal of ion microscopy is the chemical analysis of unknown microstructures, the elucidation and eventual control of experimental parameters and their effect on image fields are prerequisite. T h e methodology developed in this study to handle ion images is primarily concerned with identification of features and t h e quantitative manipulation of related microscopic images. T h e broader role of digital image processing in the combination and display of information from related images was of secondary concern. T h e production of artificial or amended images is potentially extremely valuable in summarizing image information and displaying elemental interrelationships. T h e potential correction of image ion intensities by calibrating localized image field sputtering conditions through the use of matrix species ion images is also promising, having been successfully applied in microprobe analysis (32). T h e general applicability of digital image analysis of morphological compositional information to other imaging techniques as well as to the wide range of structure-composition problems in biology, geology, and metallurgy is obvious. Features such as grain boundaries, inclusions, interfaces, mineral phases, and biological structures such as cell walls or organelles are all candidates for feature analysis. Investigations are presently underway in several of these areas.
LITERATURE CITED G. H. Morrison and G. Slodzian, Anal. Chem.. 47, 932A (1975) R . Castaing and G. Slodzian, J . Microsc. (Parfs), 1, 395 (1962). J. D. Fassett, J. R. Roth, and G. H. Morrison. Anal. Chem.. 49, 2322 (1977). N. M. Short, P. D. Lowman, Jr , S. C. Freden, and W. A Finch, Jr., "Mission to Earth: Landsat Views the World", NASA, Washington, D.C., 1976. R. C Gonzalez and P. Wink, "Digital Image Processing", Addison-Wesley, Reading, Mass., 1977. A. Rosenfeld, Ed., "Digital Picture Analysis", Springer-Verlag, New York, N.Y., 1976. N. Xuong and S. T. Freer, Acta. Crystallogr , Sect. E , 27, 2380, (1971). B. W. Johnson, J. B. Callis, and G. D. Christian, Anal. Chem., 49, 747A (1977). R. 0. Duda and P. E. Hart, "Pattern Classification and Scene Analysis", Wiley-Interscience, New York, N.Y., 1973. B. R. Kowalsky, Ana;. Chem., 47, 1152A (1975). P S. Schoenfeld and J. R . DeVoe, Anal. Chem , 48, 403R (1976) G. D. Magnusson and C. E. Carlston, J . Appl. Phys., 34, 3267 (1963). J. M. Fluit, P. K. Roll, and J. Kistemaker, J . Appl. Phys., 34, 690 (1963). D. Onderdelinden, Appl. Phys. L e t t . 8 , 189 (1966). G. J. Scilla and G. H. Morrison, Anal. Chem., 49, 1529 (1977). M. Bernheim, Radiat. Eff., 18, 231 (1973). M. Bernheim, G. Slodzian, I n t . J . Mass Spectrom. Ion Physics, 12, 93 (1973). M. Bernheim and G. Slodzian, Surf. Sci.. 40, 169 (1973). P. J. Martin and R. J. MacDonaid, Radiat. E f f . , 32, 177 (1977). R. J. MacDonald and P. J. Martin, Surf. Sci.. 67, 237 (1977). E. Zwangobani and R. J. MacDonald, Radiat. E f f . , 20, 81 (1973). W. H. Christie, D. H. Smith, R. E. Eby, and J. A. Carter, A m . Lab., IO. 19 (1978). D. M. Drummer, J. D. Fassett, and G. H. Morrison. Anal. Chim. Acfa, in press. G. S. Robinson, Comp. Graph. Image Proc.. 6 , 492 (1977). L. S. Davis, Comp. Graph. Image Proc., 4, 248 (1975). K. B. Welies, PhD. Thesis, Cornell University, Ithaca, N.Y., 1976. S. Nazar6 and G. Ondracek, Microscope, 22, 39 (1974). E. E. Underwood, Microscope, 22, 69 (1974). M. Bernheim and G. Slodzian, I n t . J . Mass Spectrom. Ion Phys.. 20, 295 (1976). A. Benninghoven and A. Muller, Surf. Sci.. 39, 416 (1975). J. D. Ganjei, D. P. Leta, and G. H. Morrison, Anal. Chem., 50, 285 (1978).
RECEIVED for review May 16,1978. Accepted August 14,1978. Financial support provided by the National Science Foundation under Grant No. CHE77-04405 and through the Cornel1 Materials Science Center, and t h e National Institutes of Health under Grant No. GM24314-01.
Spark Source Mass Spectrometric Procedure Employing Stable Isotopes to Study the Uptake of Copper by Fish from Seawater and Food B. R. Harvey MAFF, Fisheries Radiobiological Laboratory, Hamilton Dock, Lowestoff NR32 IDA, U.K.
Because of the lack of suitable radioisotopes, the use of electromagnetically enriched stable isotopes as a means of studying the uptake of copper by fish (plaice) has been investigated. =Cu (99.81 % enriched) was chosen for the tracer, while the other stable isotope s3Cu(99.88% enriched) was used as a standard spike in the spark source mass spectrometric analysis. An attractive feature of this method is that both the natural copper content of the fish organs and the concentration of added tracer are determined on the same sample by making two measurements of isotopic ratio-one before and one after the addition of the standard spike. The sensitivity of the analysis is such that all but the very smallest organs of a 303 (wet) plaice (e.g., heart, kidney, spleen) may be analyzed individually for both natural copper and the absorbed tracer. A low processing blank was achieved by the use of quartz apparatus and clean room conditions. 0003-2700/78/0350-1866$01 O O / O
Our knowledge of the fate of many elenients during the metabolic processes of living organisms has been greatly enhanced in recent years as a result of the ready availability of suitable radioactive tracers i1 ) . 'There are, however. some important gaps in this study where suitable radioisotopes do not exist or are prohibitively expensive t o manufacture. One such element is copper which is toxicologically important for many species of fish; unfortunately the readily available "4cu ( t I l 2= 12.8 h) decays too rapidly for convenient use. while "Cu (tl12 = 61.6 h) is less readily available, rather expensive, and still too short-lived for lengthy experiments. Roth stable isotopes of copper are however available in a highly enriched state (>99.8%', for example from AERE, Harwell, England) and these isotopes constitute ideal tracers in many ways, being readily available at low cost as well as being free from radiation hazards of any kind. In order t o C 1978 American Chemical Society
