Anal. Chem. 1991, 63, 2232-2237
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(9) W i c k , J. L.; Taylor, L. T. J . H@h ResoM. Chrometog. 1990, 73, 312-316. 110) Snider. L. R.: Kirklend. J. J. In lnboductkn t0 MoaKn L&& Chroma-
(12) The Mer& Index, 11th ed.; Budavari, S.,Ed.; Mer& and CO: way, KI, 1989.
Rah-
Automated Scanning Electron Microscopy for Atmospheric Particle Analysis Mark S. Germani*Jand Peter R. Buseck Departments of Chemistry and Geology, Arizona State University, Tempe, Arizona 85287
A procedure Is deecrlbed for the analysls of lndlvldual atmorpherlc partkles Wng an automated analytical scanning electron microscope (AASEM). The procedure Incorporates a novel automatk vldeo threrhddlng method that io essential for long-term unattended analysis of particles as small as 1 pm In dlameter. The procedure Is evaluated by udng particle dlsperdons prepared from USGS standard rock powders. Mlneral partlde types were ldentllkd by ushg duster analysts. Typlcal preclslon of f10% for malor mbreral types and f20 % for mlnor mineral types were determlned from repllcate analyses. The results of a chemlcal mass balance b e tween lndlvldual particle analyses and bulk chemical compodtlons of the standard rock powders lndlcate a preclslon of better than f15% for most elements. The accuracy of the procedure, when m d l z e d to SO, range8 from 1% to more than a factor of 3. Large dlscrepancles for some elements are due to errors In particle vokmo calculatkrw and p " 8 assoclated wlth analyzlng minor mineral types.
INTRODUCTION Several microanalytical instruments such as electron microscopes and electron, ion, and laser microprobes are available to study individual atmospheric particles. The analytical scanning electron microscope (ASEM) is among the most versatile instruments because it can provide both chemical and morphological data for particles as small as 0.1 pm in diameter. Despite this versatility the ASEM has been used sparingly in atmospheric particle research. A major drawback has been the amount of time needed for an operator to analyze a statistically significant number of particles to characterize an atmospheric particle sample. The introduction of automated ASEMs (AASEM) has greatly reduced the time required to analyze large numbers of particles (1-10). Although AASEMs have been used to analyze atmospheric particle samples and other particulate material, little information is available to evaluate the precision and accuracy of this technique. Johnson e t al. (9) compared individual particle analysis with bulk X-ray fluorescence data for the coarse fraction of atmospheric particle samples collected with a virtual impactor. The authors report automated analysis results of individual particles 2.5 to 15 pm in diameter on Teflon filters. The filters had particle mass loadings of 50-100 pg cm-2. 'Present address: McCrone Aesoc, 850 Paaquinelli Dr, Westmont, IL 60559. 0003-2700/91/0363-2232$02.50/0
In this paper we report the results of an evaluation of an AASEM procedure for individual particle analysis. The procedure is specifically designed for the analysis of particles as small as 1pm in diameter on Nuclepore filters. The filters have particle mass loadings of 5-10 ng cm-2. The procedure was developed for use in an individual particle study of the Phoenix urban aero801 (5). Particle dispersions prepared from U. S. Geological Survey (USGS) standard granite (G-2) and basalt (BHVO-1) rock powders were used to develop and evaluate the procedure. USGS standards were chosen because they are chemically well characterized and are similar in size distribution and chemical composition to the coarse size fraction (diameter >1 pm) of atmospheric particles in the Phoenix aerosol. Data obtained in this study were also used to develop a cluster analysis statistical program for automated individual particle analysis (AIPA) data reduction, which is described elsewhere (11).
EXPERIMENTAL SECTION AASEM. A JEOL JSM-35 ASEM is used for automated particle analysis. The instrument is equipped with an energydispersive X-ray (EDX) spectrometer (Princeton Gamma Tech, Princeton, NJ), automation system (TN2000, Tracor Northern, Madison, WI), and a custom-designed back-scattered electron detector (BSED). The automation system includes a digital electron beam control, sample stage positioning, video signal monitoring, and EDX analysis. The BSED is an annular lightpipescintillator design (M. E. Taylor Engineering,Wheaton, MD) that replaces the secondary electron detector in the ASEM. Standard Particle Dispersions. To simulate Phoenix urban aerosol samples it was necessary to prepare filter samples with particle loadings of 100 ng/filter. Sucrose ie used as a solid diluent to facilitate weighing, handling,and filteringsuch a small mass of mineral powder. Reagent grade sucrose is purified to remove any particles >1 pm in diameter by recrystallizing a saturated aqueous solution after filtering through a 0.45 pm pore diameter membrane filter. The recrystallized sucrose is ground with an agate mortar and pestle and sieved through a 63-pm mesh screen. A 50-mg sample of granite powder and a 100-mgsample of basalt powder are each made up to 1g with sucrose in 12." X 50-mm polystyrene mixing vials. A 12-mm-diameter plastic ball is added to each vial, and each sample is mixed for 4 h (Spex Industries, Mixer Mill, Model SOOO). This process is repeated twice, each time diluting 100-mg aliquota from each of the two sucrose mixtures in 1g of sucrose. A 100-mgaliquot of the final mixture is added to a filtration apparatus containing 100 mL of ultrapure water over a aT-mm-diameter, 0.4 pm pore diameter Nuclepore filter. The sample is dissolved for 2 h to remove the sucrose before filtering. Three replicate filter samples were prepared from the final granite and basalt mixtures. The particle mass on the granite and basalt filter samples is 50 and 100 ng, respectively.
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@ 1991 American Chemlcal Society
ANALYTICAL CHEMISTRY, VOL. 63, NO. 20, OCTOBER 15,
1991
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Table I. AASEM Instrumental Operating Conditions electron beam voltage, keV electron beam current, pA working distance, mm magnification small particles" large particlesb digital scan point spacing small particles coarse raster: pm fine raster! pm large particles coarse raster, pm fine raster, pm video dwell time, ps
40 50[
30
20
10
average video signal
-
Ivideo I
-
7
-
I I ......... ;
.I. . . I
500x 200x 0.3 0.1 0.7 0.3 125
"Particle diameter >1 pm. bParticle diameter >3 p m (granite), >4 pm (basalt). 'Used to locate particles. dUaed to size particles.
.I ..I
25 300 35
Table 11. Energy Ranges for X-ray Regions-of-Interest I
Gray Level Flgure 1. Portlon of a BSE vMeo histogram for a Nuclepore filter showing the average substrate vMeo signal and the vMeo threshold
setting.
element Na Mg A1
A 1-cm2section is cut from the center of each filter. Also,two additional sections are cut from one of the granite and basalt filters. One section is taken from an area half way between the center and edge of the filter and the other section is taken near the edge of the filter. The sections are mounted on 12-mm-diameter carbon stubs by using colloidal carbon paint and coated with a 20-nm evaporated carbon film. AIPA. Particles are automatically detected by an increase in the particle's back-scattered electron (BSE) video signal above a preset video threshold. The BSE signal is better for detecting particles on a filter substrate than the secondary electron signal because of its higher atomic number contrast, its lack of topographic information, and its lower susceptibilityto electron beam charging artifacts, which often occur even with a carbon-coated filter sample. The advantage of the BSED used in this study is that a higher contrast, higher resolution BSE image can be obtained at lower beam currents than is possible with commercially available solid-stae BSEDs. In earlier studies, the video threshold has been manually set to eliminate the substrate signal so that the computer only %ees" the particles. A problem with manual thresholding is that it is difficult to reproduce the threshold from sample to sample and operator to operator. In addition,once the threshold is established, the instrumental conditions must remain constant or the video signal will change during analysis. In this study, the threshold is automatically determined from an analysis of the substrate video signal. The BSE video signal (0-255 gray levels) is measured for 5120 points in the SEM field-of-view. The average and standard deviation (a)of the video signal is determined, and those points with video signals greater than *2a from the average (outliers) are rejected. The average and standard deviation are recalculated and outliers rejected until the change in the relative standard deviation (RSD) of the average substrate video signal is less than 0.2. The standard deviation calculation converges in two or three iterations when a value of 0.2 is used for the change in the RSD. The threshold is set at a value of 3a above the substrate's average video signal (Figure 1). The threshold is recalculated after each field of particles is analyzed. Automatic thresholding removes any operator bias, compensates for long-term instrument drift and changes in substrate topography. A particle loading (projected particle area per SEM field-of-view area) of 1 pm were analyzed for all samples. The samples were reanalyzed for particles >3 and >4 pm for the granite and basalt samples, respectively. Two size ranges are
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ANALYTICAL CHEMISTRY, VOL.
63,NO. 20, OCTOBER 15, 1991
Table 111. Chemical Composition and Density of Granite and Basalt Mineral Particle Types mineral type K-feldspar quartz biotite plagioclase-1 chlorite apatite muscovite plagioclase-2
density, g/cm3
NazO
MgO
SiO,
A1203
Granite 2.5 2.6 2.8
2.7
CaO
KzO
17.0
65.8
15.3
9.5
16.2 20.6
8.6 4.2
14.8
21.9
100 43.5 67.7 37.7
6.1
2.9
Pz06
27.2 19.7
2.7
18.9
56.0
44.0 11.1
FeO
24.0
3.2 2.6 2.6
TiOz
53.3 67.8
10.9
63.4
15.6
5.5
Basalt K-feldspar quartz pyroxene-1 plagioclase pyroxene-2 iron oxide glass
ilmenite mixed-ilmenite olivine
2.5
19.5
2.6 3.2 2.9 3.3 5.7 2.9 4.5
100
13.5 5.0 12.6 3.4
3.5 3.3 4.0
6.8 25.1 5.7
66.7
6.0
34.0 40.8
needed to efficiently obtain data from a statistically significant number of particles in the 1-10-pm size range. For example, approximately 85% of the >1-pm-diameter granite and basalt particles fall in the size rangee 1-3 and 1-4 pm, respectively. Also, approximately 80% of the >&pm-diameter granite particles fall in the range 3-10 pm, whereas, approximately 60% of the >4pm-diameter basalt particles are in the 4-10-pm size range. Approximately lo00 particles were analyzed in about 20 h for each sample in each size range. A cluster analysis procedure (11) was used to identify the mineral types in the granite and basalt samples using particle X-ray RI data. A standard ZAF X-ray correction procedure was used to quantitatively analyze several representative particles from each of the mineral types. Larger particles (5-10 pm in diameter) are used to minimize the effect of particle size and geometry on the ZAF correction (14). Table I11 lists the average chemical composition and density of the granite and basalt mineral particle types. The density for mineral type t, P;, is
14.7 11.9 11.6
54.1 50.7
13.9
36.8 100
corunduma a From grinding process.
50.7
2.5
CPij i ;
a0 -
0 2
RESULTS AND DISCUSSION Standard Particle Dispersions. The USGS particle dispersions were prepared to simulate the coarse particle fraction of samples collected from the Phoenix urban aerosol (5). The Phoenix samples are collected for 8 h a t a flow rate of 10 L min-' with two-etage (8.0 and 0.2 pm pore diameter) Nuclepore filter samplers. Figure 3 contains typical particle size distributions for the granite and basalt particle samples and a coarse particle fraction from a Phoenix aerosol sample. There is generally good agreement among the size distributions for the three types of samples. The average particle diameters for the granite and basalt samples are 2.0 and 2.1 pm, respectively, whereas, the atmospheric particle samples have average particle diameters of -2.5 pm. The granite and basalt dispersions have average particle loadings of 41 and 57 particles per field, respectively, which are in the range of 40-100 particles per field for the coarse particle fraction of the atmospheric samples. The loading of the coarse fraction of the atmospheric samples was kept low to avoid overloading the fine particle fraction that is collected on the 0.2 pm pore diameter Nuclepore filter. Although the
15.3 100 5.3
20.0 17.0
65.0 28.0 16.2
Phoenix sample
70
2
! i
60
-
-
50'
40-
30
-
ill
where pi0 is the density for the oxide of element i, fa is the weight fraction of the oxide of element i in type t, and NE is the number of oxides in mineral type t.
9.8
-
so
NE P;
1.2
4.8
3.0
100
1.6
20
10
ii
i
1
\
2 3 4 5 s
Size (un) Flguo 3. Particle size distributions for granle (@2), basal (WVO-l),
and Phoenix aerosol particles.
average loading was low for the entire coarse particle filter, it was actually much higher if one considers the available filtration area of a large pore size Nuclepore filter. Particles are collected on this type of filter by interception and impaction processes that tend to collect particles near the pores in the filter. An 8 pm pore diameter filter has approximately 40 pores per field-of-view, which translates to about 2 particles per pore for the atmospheric samples. Though this particle loading is light compared to other studies, there are -2000 particles on the filter section for analysis. Nuclepore filters are an excellent substrate for automated particle analysis. Their smooth surface is ideal for automatic video thresholding. One artifact that has to be avoided, in setting the threshold, is the electron-beam-induced charging
ANALYTICAL CHEMISTRY, VOL.
that occurs at pore edges in the 8 pm pore diameter fiiter and is present in the BSE image. This effect can be eliminated by setting the threshold a t 5u above the average substrate signal without affecting the analysis of 1-pm-diameter particles. Mineral Analysis. Cluster analysis was done for one of the basalt and granite samples to identify the mineral types, and discriminant analysis (11)was used to assign particles to the mineral types in the remaining samples. The mineral types identified in the granite sample (Table 111)agree with those found by standard petrographic analysis (15). The identification of the mineral types in the granite is better defined than the basalt because the granite minerals have a larger grain size than the basalt minerals. Consequently, the granite powder contains more single-mineral-phase particles than the basalt powder. For example, the ilmenite in the basalt has a fine dendritic structure and, therefore, many of the particles have a biphasic ilmenite-plagioclase or ilmenite-pyroxene ('mixed" ilmenite) composition. The composition of these biphasic particles is different enough from the single-mineral-phase particles that the cluster analysis programs may identify them as distinct mineral types. A modified x2 distance measure is used in discriminant analysis. Dxn is defined as NE
where RI, is the observed relative intensity for an element in the particle, RI, is the expected relative intensity for the element in the mineral type, and NE is the number of elements with RI, > 0.01 in the mineral type. The RI, values are obtained from cluster analysis centroid compositions (11). Particles are assigned to the mineral type that minimizes Dx2 or as 'unknown" if DXn> 0.03. A value of 0.03 was chosen because that is the distance at which the number of unknown particles began to rapidly increase. Approximately 10% of the particles will be classified as unknown. The average percent number of particles for the granite and basalt mineral types is listed in Table IV. We did not observe any difference between the results for samples cut from the center of the filters and the edge or halfway between the center and edge. Table IV contains average results for five samples. Relative errors of 10% were obtained for the major particle types K-feldspar, quartz, and plagioclase in the granite and pyroxene-1 and plagioclase in the basalt. Relative errors of >30% were obtained for the minor mineral types (
