Microanalysis: Small amounts, small volumes, or both

MICROCHEMICAL

JOURNAL

Microanalysis:

34, 35-50 (1986)

Small Amounts, DAVID

Department

of Chemistry,

University

Small Volumes,

or Both’

M. HERCULES of Pittsburgh,

Pittsburgh,

Pennsylvania

15260

Received April 2, 1986; accepted April 4, 1986 The word microanalysis originated as a term to describe the analysis of small amounts of materials (small samples) for major components. For example, Benedetti-Pichler developed this form of analysis to a high level of sophistication. Recently, the term microanalysis has been expanded (correctly or incorrectly) to include the measurement of small amounts of material in a large volume (trace analysis) or microamounts of material in small volumes (microprobe analysis). Instrumental capabilities exist (or soon will exist) in which it is possible to measure small volumes for small amounts. The present paper will present a discussion of some of the problems intrinsic in measuring small amounts of materials and some recent examples of how spectroscopic techniques make it possible to perform microanalysis by any definition. 0 1986 Academic Press, Inc.

INTRODUCTION

As part of this symposium, it is appropriate to review the nature of microanalysis to see how it fits into the overall scheme of chemical analysis. Of particular interest will be how the concept of microanalysis has changed in the last several decades and how this change reflects the advancement of analytical chemistry. The present paper will be divided into two parts. First, we will explore the nature of modern microanalysis with regard to parameters, such as sample size, the fraction of the sample consumed, and whether one measures a major or minor component. Second, two examples will be presented to show how the ability to measure small amounts of material can contribute significantly to the analysis of technical materials. Examples will be chosen from the author’s work on surface characterization of catalysts and polymers. DEFINITION

OF MICROANALYSIS

As a point of reference, it is important to see how analytical chemists regarded microanalysis approximately 30 years ago (when the author was a sophomore in college). I have chosen a quote from my undergraduate analytical textbook (Kolthoff and Sandell) which describes the view of microanalysis held at that time (I). In the introductory chapter of the book the authors distinguish between microanalysis and macroanalysis with regard to sample size. “Ordinarily the size of sample taken in a quantitative analysis lies in the range 0.1 to 1 gram. This is a macro sample. It may be advantageous or necessary because of scarcity of mate-

i This paper was presented at the 1985 Eastern Analytical Symposium as part of a symposium honoring Professor L. B. Rogers on receipt of the American Microchemical Society Benedetti-Pichler Award. We offer him our most sincere congratulations. 35 0026-265X/86 $1.50 Copyright 0 1986 by Academic Press. Inc. All rights of reproduction in any form reserved.

36

DAVID M. HERCULES

rial to work with samples about l/100 as large as this; i.e., 1 to 10 milligrams. This is a micro sample, and its use requires the application of micro methods. The difference between macro and micro analysis lies essentially in the scale of operations . . . In trace analysis the object is to determine a trace constituent (less than 0.1%). Such an analysis has the characteristics of both macro and micro analysis. It may require the taking of a large sample, as in macro analysis, when the final determination usually involves quantities smaller than those dealt with in microanalysis.” It is clear from the above that sample size was the primary distinction between macro- and microanalysis, and the percentage of the component sought distinguished between macro-, micro-, and trace analysis. Reflecting on this situation 30 years later it is clear that the definition of microanalysis has changed. In order to characterize types of analysis at the present requires definition of three important parameters. Table 1 summarizes these three parameters: sample size, fraction of the sample consumed, and magnitude of the component sought. Sample size is the classical macro/micro definition. For example, a large sample might be a large piece of material, such as a turning of metal that weighs several grams, while a small sample may be a small amount of a newly synthesized organic or a small particle. If one considers the fraction of the sample consumed in both classical macro- and microanalysis, the entire sample is used. By contrast, in many modern analytical methods only a small fraction of the sample is consumed. Classical microprobe analysis consumes only a small part of the sample, while trace metal analysis frequently uses the entire sample. The third important parameter is the percentage of the component present. A major component analysis would be in the order of 1% or greater, and a minor component analysis below 0.1%. This corresponds better to the classical definition of trace analysis. By considering all possible combinations of the above three parameters one can generate a matrix of eight different types of analytical methods depending on the relationship of the three parameters described. It is instructive to look at these

TABLE 1 Parameters for Definition of Types of Analysis SAMPLE

-1 g sample

cl mg sample

Large piece of metal

Small particle microsynthesis result

SIZE

-Milliliters Grams FRACTION SAMPLE

OF

CONSUMED

MAGNITUDE COMPONENT

Microliters Micrograms

OF SOUGHT

Total sample consumed

Partial sample consumption

Major component

Minor component
21%

MICROANALYSIS:

SMALL

AMOUNTS/VOLUMES

37

OR BOTH

eight possibilities and to correlate them with known analytical methodologies. Such a comparison is presented in Table 2. If one considers a large sample which is completely consumed for major component analysis, one is dealing with the classical Kolthoff and Sandell definition of macroanalysis. An example might be the analysis of copper in brass by a titrimetric method. The brass sample is completely dissolved, the copper is a major component, and the entire sample is consumed. Similarly, using a large sample which is completely consumed, but analyzing for a small component, would be conventional trace analysis. An example might be an analysis of a priority pollutant by high-performance liquid chromatography, such as PAHs in water. Continuing the analogy, if one uses a small sample size and the sample is completely consumed in analysis for a major component, one has the definition of classical microanalysis. The example here would be the classical micro-Kjeldahl determination of nitrogen in an organic compound. If one now considers analysis on a small sample in which the sample is completely consumed for analysis of a minor component, one enters the realm which has frequently been described as ultratrace analysis or microtrace analysis. Traditional clinical analysis operates in this domain; namely, small amounts of materal in a small sample. For example, LDH, glucose, or BUN in blood serum. The sample is small, the percentages of components are small, and the entire sample is consumed. TABLE 2 among Analytical

Relationships

VOLUME

Methodologies

ANALYZED SMALL

LARGE SAMPLE LARGE CLASSICAL MACROANALYSIS

t

SIZE

SAMPLE

SMALL

LARGE

CLASSICAL MICROANALYSIS

SIZE SMALL

TRADITIONAL

PARTICLE

MICROPROBE

ANALYSIS

ANALYSIS

,ARGE

_________________.____________ __________._________-------.

Cu in brass by titrimetry

CONVENTIONAL

MALL

__-.__________________------~

Kjeldahl N, determination in organic compounds

Ni inclusion in Al by SEMiEDX

TRADITIONAL

SURFACE

TRACE

CLINICAL

MICROPROBE

ANALYSIS

ANALYSIS

ANALYSIS

------------------____________

Pollutant analysis by HPLC

------_--.__________________

LDH, glucose, or BUN in blood serum

._ P in grain boundary by Auger Spectroscopy

Measurement of - 100 A particles by AEM

38

DAVID M. HERCULES

The above discussion indicates that if one considers only the percentage composition and the sample size, one can generate the three classical concepts of analysis, along with a fourth trace analysis on small samples (ultratrace analysis). The real change in analytical thinking with regard to microanalysis, therefore, comes when one superimposes on this matrix the possibility of analyzing only a small fraction of the sample. Here then one generates methods which represent the frontier in microchemistry, the microprobe methods. Consider a large sample in which there is a small inclusion and one is seeking the major element composition of the inclusion. Immediately one is dealing with the area of traditional microprobe analysis (Table 2). An example might be the analysis of a nickel inclusion in aluminum by the combination of scanning electron microscopy and nondispersive X-ray analysis (SEM/EDX). Similarly, if one has a large sample, but one is looking for a fraction of the total sample which contains a small component, one leaves the area of traditional microprobe analysis and enters the area of surface microprobe analysis. Here one might seek the composition of a particle, in which the element sought may be present only on the surface, and this represents a very small percentage of the total inclusion. A typical example would be the analysis of phosphorus in a grain boundary by Auger spectroscopy, where frequently the percentage of phosphorus lies below 1%. If TABLE 3 Comparison of Analytical Methods Based on Number of Atoms (or Molecules) Measured VOLUME

I

LARGE SAMPLE

l--l= LARGE

Major component % of 1 g sample

P

E

E

LARGE

SMALL SIZE

SAMPLE

f SMALL

I SIZE

I

LARGE

Microanalysis 10% of a 0.1 mg inclusion in a micrometer-size particle

Microprobe analysis 10% of a lo-pm

102i Atoms

lOi Atoms

lo* Atoms

lo6 Atoms

Trace analysis 10 ppb of 100 ml sample

Analysis of a I-ppb component in l-p1 sample particle

Auger analysis of 50-A deep spot 20 urn diameter

1 ppm of an inclusion in a micrometer-size particle

1OL6Atoms

lo9 Atoms

lo5 Atoms

1 Atom

R

Cl

ANALYZED

I

Measurement of a submicrometer

cube

N T

I

I

M P 0 S I

SMALL

i 1

1

I

MICROANALYSIS:

SMALL AMOUNTS/VOLUMES

OR BOTH

39

one stretches the above definition even further, into the realm of seeking a major component in a small sample but looking only at a fraction of the total sample, one is involved with the gross analysis of particles. A good example is measuring the composition of a 100-A particle by analytical electron microscopy. Finally, one encounters the problem of analyzing a trace amount of material in a very small sample in which one consumes only a fraction of the sample; here it is difficult to find an analytical method which fits the requirement. Thus, for the moment, we will leave a question mark in that particular box in Table 2. Having constructed the above matrix, it is interesting to consider a similar matrix based on the number of atoms or molecules measured in an analysis. Table 3 presents the same matrix as Table 2, except each box reflects the number of atoms or molecules actually measured in each analytical situation (calculated as atoms in the table). For example, consider a major component of a large sample which is completely consumed-analyzing a component that constitutes 10% of a l-g sample measures approximately 1021atoms. The major difference between macroanalysis and trace analysis therefore is the number of particles measured. For example, consuming the entire sample to measure 10 ppb of an element in a IOO-ml sample would yield lOi6 atoms for measurement, a reduction of five orders of magnitude. The important relationship between classical microanalysis and classical trace analysis is seen in Table 3. if one analyzes a small sample for a major component; i.e., 10% of a O.l-mg sample, one is measuring lOi atoms-exactly the same as the number measured for 10 ppb in a loo-ml sample. Thus, classical microanalysis and classical trace analysis really measure the same amount of material but using vastly different sample sizes. By contrast, the analysis of a 1-ppb component of a l-k,1 sample makes available only IO9atoms or molecules. This type of sample (clinical analysis) represents a different scale from microanalysis or trace. analysis because of the small number of atoms available for measurement. If we now look at the microprobe methods, outlined in Table 3, a similar analogy can be developed. For example, if one considers analysis for a component constituting 10% of a IO-p.rn cube, one would have available lo8 atoms or TABLE 4 Real vs Contrived Problems Consider a Pt particle on a catalyst: Particle size-cube 20 A on a side Atomic radius Pt = 1.4 A Atomic volume Pt = 30 A3/atom Particle contains Radius calculation

Volume calculation

-700 atoms

-250 atoms Key Question:

In a bimetallic catalyst, is this an alloy, and what is its stoichiometry?

40

DAVID M. HERCULES

molecules for measurement. Such an analysis would correspond to a IO-pm inclusion in a large sample in which one would consume the entire occlusion during analysis. This represents traditional microprobe analysis; note the similarity in amount of material to that of clinical analysis. For surface analysis, in which one is looking at a surface inclusion, the number of atoms or molecules available decreases. For example, in the Auger microprobe analysis of an inclusion 20 pm in diameter, one is dealing with a very small percentage composition of the particle because of the Auger electron escape depth (50 A) which limits the amount of material to lo5 atoms; a factor of IO3below traditional microprobe analysis. To extend the analogy even further, consider measurement of a submicrometer inclusion in a micrometer-sized particle corresponding to particle analysis in Table 2. Under these circumstances, assuming a major component, one would have approximately IO6atoms for measurement. This is analogous to the amount of material available for detection in the Auger analysis discussed above. If one now considers ppm analysis of an inclusion in a micrometer-sized particle, one enters the box containing the question mark in Table 2. However, for such an analysis, one would have available only 1 atom or molecule! Thus, one understands why it is difficult to select an example of an existing method of analysis for Table 2, since relatively few methods exist that can detect only one atom or molecule. Although the above is an interesting (and hopefully fascinating) intellectual exercise, the question arises as to whether this is a real problem or simply one that is contrived. I submit that it is a real problem. Consider analysis of a metal particle on a bimetallic supported catalyst in the form of a cube approximately 20 A on a side. Data for this are shown in Table 4. Let us assume that the catalyst contains platinum and rhenium (reforming catalyst) and that the catalyst functions better with both platinum and rhenium present than with either alone. A key question in the characterization of such a catalyst is “In a bimetallic catalyst do the particles represent an alloy, and if so, what is the stoichiometry?” Given that the atomic radius of platinum is 1.4 A (2) and that the atomic volume of platinum is 30 A3 (3) per atom, one calculates that a 20-A cube contains somewhere be-

868

864 Binding

860

856

852

Enerw[eV]

FIG. 1. Ni 2p parameters used in this study. &, binding energy; FWHM, peak width at half maximum; a, asymmetry factor (4).

MICROANALYSIS:

SMALL

AMOUNTS/VOLUMES

OR BOTH

41

tween 250 and 700 atoms, depending on whether one uses the atomic radius or atomic volume for calculation. Thus, if the particle stoichiometry corresponds to 10% of one metal and 90% of the other, one is faced with the necessity of detecting fewer than 100 atoms for the minor component and focusing the analytical probe on a 20-A particle. The above example illustrates a very important point. Our concept of how to go about extending microanalysis in the future must change. We have traditionally sought ways to detect fewer atoms or molecules, and undeniably we have been successful. However, conceptually we have begun to reach the limit as to how far we can go by detecting fewer atoms or molecules. Once one achieves single atom (or molecule) detection, conceptually at least, one has reached the limit. Further, in any real microchemical system it is unlikely that one is going to be able to “harvest” all of the atoms or molecules available in any given sample. Therefore. in order to solve increasingly complex (and important) microanalytical problems, we must seek ways to amplify signals produced by a few atoms or molecules as the mode for future development of microanalysis. ESCA Characterization of Nickel-Alumina Catalysts The ESCA spectra of first-row transition metals have been used effectively by many workers to characterize these metals in supported catalysts. Recently we have made use of nonlinear least-squares curve fitting (4) to analyze nickel 2p spectra obtained from Ni-alumina catalysts. It was possible to identify a multiplet splitting peak, in addition to the main peaks, which is useful for catalyst characterization. It was possible to correlate the intensity of a specific multiplet splitting band with the reduction behavior of nickel on Ni-alumina catalysts. r

NIW04

880

872 Binding

864

856

Enerw[eV]

FIG. 2. Curve fitting of the Ni 2p spectra of NiWO,. Bottom: fit with two satellites and main peak (9).

Top: fit with one satellite

and main peak.

42

DAVID M. HERCULES

Ni 2p3,2 ESCA spectra can be defined by three parameters: the binding energy, the asymmetry factor, and the full-width-at-half-maximum (FWHM). These parameters are defined for a Ni 2p s,* spectrum, as shown in Fig. 1. The main Ni 2p,,, peaks for all nickel compounds exhibit peak asymmetry toward the high binding energy side. The degree of asymmetry is similar for most nickel compounds; NiWO, and NiO are exceptions-they have larger asymmetry factors. The asymmetry of Ni 2p3,* peaks of nickel compounds can be correlated with an intense satellite peak 1.9 eV from the main peak, as shown in Fig. 2 (labeled Sl). Other peaks labeled in this spectrum are S2, which is the main shakeup satellite for the nickel peak, and S3, which is another weak shakeup peak. Note the structure is repeated for the Ni 2~,,~ line in the binding energy range of 870 to 890 eV. Although when curve fitting ESCA spectra, one is frequently quoted the proverb of being able to tit an elephant, given a sufficient number of parameters, Fig. 2 shows the Ni 2p spectrum of NiWO, fitted using shakeup and main peaks with and without the multiplet splitting peak at S 1. (The small peak on the lower binding energy of each fitted peak is due to the Kc+,, X-ray satellites.) It is clear from Fig. 2 that including Sl improves the “goodness” of the fit, as indicated by the x2 values given in Fig. 2. Visual examination of the valley between the Ni 2pj12 peak and its satellite for the two spectra clearly indicates that the additional peak . NIB 3% NlO

7X NIO

11% NiO n

880

872 Binding

864 Energy

856

[ev]

FIG. 3. Curve fitting of the Ni 2p spectra of Ni/AI,O, catalysts (4).

MICROANALYSIS:

SMALL

AMOUNTS/VOLUMES

OR BOTH

43

is necessary. An important question is, however, whether or not the peak indicated as Sl has any chemical significance. It was found that when the spectra of nickel catalysts supported on alumina were analyzed, the Sl peak also was present and correlated with the level of nickel coverage. Curve fitting results for the Ni 2p spectra of three supported nickel catalysts are shown in Fig. 3. A well-established method for characterizing nickel species on nickel catalysts is the combined use of ESCA and hydrogen reduction for determination measurement of reducible nickel species (5). Generally, as the amount of nickel in the catalyst increases, so does the percentage reducibility of the nickel. A correlation between reducibility of nickel and the Sl satellite-to-main peak intensity ratio for nickel catalysts is shown in Fig. 4. The correlation coefficients for the Ni 2p3,2 and Ni 2p1,2 lines were 0.984 and 0.989, respectively. Nickel-alumina catalysts contain both nonreducible nickel aluminate-like species and a reducible NiO-like species; the percentage reduction of nickel reflects the change in the relative amounts of these two species. Given that NiO has the highest intensity of the Sl satellite peak and nickel aluminate the lowest, it is reasonable that the reducibility which correlates normally with NiO would also correlate with S I. Thus, it is clear that one can correlate the intensity of the nickel satellite peak with the reducibility of nickel on the catalyst. This represents a step toward the prediction of catalytic activity using surface spectroscopic techniques such as ESCA. Secondary Ion Mass Spectrometry

of Polymers

A major challenge for surface analysis is the characterization of a thin polymer layer either on top of another polymer or on a metal. Both ESCA and conventional static SIMS have shown potential for studying polymer layers which are sufficiently thin that they cannot be characterized using conventional polymer

% Reduction

FIG. 4. Correlation of percentage reduction with Ni 2p satellite to main peak intensity ratio (Z,,/Z,,,). (0) Z,,&,, of Ni 2p,,*, correlation coefficient = 0.984; (0) Z,,/Z, of Ni 2~,,~, correlation coefficient = 0.989.

44

DAVID

M. HERCULES

methodology. Traditionally, ESCA has had drawbacks because of its low intrinsic information content. Similarly, quadrupole SIMS or sector SIMS instruments have not permitted characterization of polymers in the mass range above m/z 500. In collaboration with Professor A. Benninghoven (University of Mtinster, West Germany) the time-of-flight SIMS instrument developed in his laboratory has been used successfully to characterize polymers in the static SIMS mode. High mass fragments have been detected which have not been seen in other SIMS instrumentation applied to polymers. Figure 5 shows a schematic diagram of the time-of-flight SIMS instrument. A primary ion source directs a pulse of argon ions toward the sample, striking an area of approximately 0.01 cm*. The width of the ion pulse is about 10 nsec and contains approximately IO3 ions per pulse. Successive pulses are separated by approximately 100 psec to permit recording of the time-of-flight spectrum. After the ion pulse strikes the sample, the secondary ions are accelerated into the timeof-flight analyzer and detected using a secondary electron multiplier. Signal averaging permits integration over a number of pulses. Between 60 and 300 set are required to obtain a spectrum. More details on the instrumentation has been published (6). We will report here results from two studies. First, a series of nylons has been studied to identify changes in the TOF-SIMS spectrum with systematic variation of structure. Second, a broader group of polymers has been studied to show the potential breadth of the technique. Nylons are linear polymers consisting of a carbon-carbon backbone connected by amide groups. To date SIMS and laser mass spectrometry have not been successful in obtaining high-molecular-weight fragments from these materials. However, TOF-SIMS spectra were obtained by using the Mtinster instrument; a typical result is shown in Table 5. The spectra are characterized by excellent signalto-noise ratio in the high mass range; fragments containing up to 24 monomer

TIME-OF-FLIGHT

SIMS

FIG. 5. Schematic diagram of Mtinster time-of-flight

SIMS.

MICROANALYSIS:

SMALL

AMOUNTS/VOLUMES

45

OR BOTH

TABLE 5 Nylons Studied Sample name

Structure

Nylon

m/z repeat unit

AB-aliphatic 0 N.6

6

” ,-W&l, H

N.8

8

113 0

,-&CH,),$-l.

141

AABB-aliphatic

N.66

66

226

N.69

69

268

N.66((u6)

66(cx6)

310

units and peaks as high as m/z 3500 were observed (7). The time-of-flight SIMS spectra of the simple nylons (N6 and NS) show essentially three types of fragment ions. Fragmentation of the backbone produces carbon cluster ions and other small fragments containing C, H, N, and 0. Because a silver foil is used as a backing for the polymers, peaks characteristic of silver and its cluster ions are also observed. A few peaks due to silver combined with small organic fragments are seen. Of greater importance, protonation and cationization of polymer segments with silver, potassium, or sodium give series of peaks corresponding to (nR + H) +, (nR + K)+ , (nR + Ag)+ , and (nR + Na)+, where R = repeat unit. These peaks can be used for identification of the polymer by establishing the sequence of monomer units. The most significant cationization for both N6 and N8 occurred with silver and sodium. The (nR + Ag)+ series is observed in the range n = I-6, and for (nR + Na)+ the range was n = l-24 for N6 and 1- 17 for N8. The spacings between the peaks for both N6 and N8 correspond to the mass of the repeat unit, m/z 113 and 141, respectively. Nylon 66 and similar materials are formed from diamines and dicarboxylic acids. Thus, they contain one more amide function per repeat unit than N6 or N8. Stable fragment ions in the high mass range for N66 and N69 corresponding to (nR + Ag)+ and (nR + Na)+ were observed. A very important and interesting feature is that cleavage occurred at alternate amide linkages consistently throughout the detectable mass range. Thus, the spacing corresponds to the repeat unit of the polymer rather than to individual monomer units. Figure 6 shows a wide scan mass spectrum of Nylon 66(~6) in the range m/z

46

DAVID

1000

M. HERCULES

1500

2000

2500

3000

moss [amu]

FIG. 6. Replotted

TOF-SIMS

spectrum of nylon 66(a6) (m/z = 0-35Of-N (7).

100-4000. It should be noted that a regular sequence of paired peaks is observed corresponding to sodium and silver cationization. The highest number of units observed for this compound was n = 10. Figure 7 shows a portion of the same spectrum recorded by the instrument without any replotting. Note again that the series of peaks corresponding to silver and sodium cationization are separated by the repeat unit. The weaker peaks in the spectrum have spacings equal to A m/z = 14 and are observed throughout the entire range. This series of peaks corresponds to addition or subtraction of methylene groups from the repeat unit, and although they constitute “chemical noise,” they do not limit the signal-to-noise ratio of the system. The instrument noise level on the spectra can be seen by variations on the tops of the peaks; it is clear that the signal-to-noise ratio is quite good. To illustrate the effectiveness of the TOF-SIMS method for polymers other than the nylons, two other examples are presented: polydimethylsiloxane and polystyrenes. The TOF-SIMS spectrum of a polydimethylsiloxane (PDMSO) sample is shown in Fig. 8 (8). For masses below m/z 500 fragmentation is extensive, and although some structurally significant fragments are observed, this portion of the spectrum does not contain the main spectral information. In the range m/z 500-10,000, intact polymer molecules are detected cationized with silver. The most intense peaks correspond to a series (nR + CH, + Ag)+, where R = the repeat unit. The presence of a terminal CH, group in the ions detected indicates that desorption of intact oligomers has occurred. This series corresponds to the major peaks in the spectrum peaking in the vicinity of mass 2000. Fragmentation also is evident in the PDMSO spectrum. Loss of terminal methyl groups gives a series of peaks at lower intensity (nR + Ag) + , which constitute the lower intensity series peaking at approximately mass 1800. The largest ion detected for PDMSO is at approximately m/z = 9600 and is cationized with silver; it contains a terminal methyl group and consists of 128 repeat units.

MICROANALYSIS:

SMALL AMOUNTS/VOLUMES

47

OR BOTH

, I

(GM+Na) (~M+A~)’

L ,

2000 M*XIMUY

,

2100 22 6 5

ICI

FIG. 7. Raw TOF-SIMS spectrum of nylon 66((~6)in the range m/z = 1460-2480. Strong cationization of the repeat unit with Ag+ and Na+ and addition or loss of methylene groups are evident (n.

In addition to polydimethylsiloxane, several polystyrenes having various substituent groups were studied. A typical example is shown in Fig. 9. Polymer fragments were observed cationized with silver to produce the most intense peaks. The spacing between the peaks corresponds to one repeat unit. In the segment of the spectrum shown it is clear that structure is present within the spacing of the polystyrene groups. The most prominent peaks are due to the Ag+ cationized multiples of repeat units; the other peaks differ by 14-16 mass units and are characteristic. It is possible to distinguish between isobaric polymers (9) on the basis of the individual structure within the groups. For example, poly(cy-methylstyrene) and poly(4-methylstyrene) are isobaric, having repeat units equal to 118. However, the fragmentation patterns are sufficiently different that these two polymers can be distinguished. It is also possible to measure molecular weight distributions from mass spectra obtained from polystyrenes. Figure 10 shows a spectrum of a polystyrene standard having an average molecular weight of 5100 (8). The most intense peaks in this series are characteristic of the oligomer distribution. In the range m/z 3000 to 7000 oligomers are detected intact (as indicated by their terminal groups) and correspond to a series (nR + C,H, + Ag)+. The weaker peaks decreasing in intensity in the range m/z 3000 to 6500 represent fragment ion peaks. Using the

48

DAVID M. HERCULES POLYCDIMETHYL

SILOXANE)

::::I

2500

.“T’

500

0 3000

2000

4000

5000

6000

m/z

8. Wide scan TOF-SIMS spectrum of polydimethylsiloxane.

FIG.

oligomer distribution from the TOF-SIMS spectrum it was possible to calculate an average molecular weight (M,) of 4500 for this specific polymer, within 11% of the known value of 5100. A low M, value would be expected because the TOFSIMS detection efficiency decreases as a function of increasing mass. ,

‘,

I

I

POLYSTYRENE

MAX. 61442

tcl

MAX.

[cl

32965

1600

“”

1000

1600

2100

‘2000

FIG.

2200

9. Narrow scan TOF-SIMS spectra of polystyrene.

MICROANALYSIS:

POLYSTYRENE

SMALL

AMOUNTS/VOLUMES

49

OR BOTH

M n.cdc.

= 4550 M “.Smr. = 5600

M, =4964

1.4 1.2

0.2 0.0 3000

4000 mass

FIG. 10. TOF-SIMS

5000 [omu]

spectrum of polystyrene

6000

of molecular

7000

weight 5100 (8).

CONCLUSION

The general ideas presented in this paper, along with the specific examples from ESCA and SIMS, indicate that the nature and concept of microanalysis has changed dramatically over the last 30 years. Microanalysis has expanded from the idea of using small samples for macroscopic analysis to using even smaller samples for trace component analysis. The entire field of surface analysis really represents a subset of microanalysis because one is always dealing with small quantities of material, whether one measures major components or minor components. The entire field of microprobe analysis also is a subfield of microanalysis because again one is always dealing with small quantities of material. From the information and examples presented it is clear that the number of atoms measured correlates better with the concept of microanalysis than sample size. The future development of microanalysis must focus on the development of signal amplification from small numbers of atoms rather than detecting fewer numbers of atoms. Undoubtedly, microanalysis has a rosy future; it will probably change more drastically in the next decade than it has in the last 30 years. ACKNOWLEDGMENTS The research discussed in this paper was supported by the U.S. Department of Energy, the National Science Foundation, and the Deutsche Forschugsgerneinscheft. The Pittsburgh-Mtinster cooperative research was stimulated by the Alexander von Humboldt Foundation.

REFERENCES 1. Kolthoff, I. M., and Sandell, E. R., “Textbook of Quantitative Inorganic Analysis,” 2nd ed., p. 6, Macmillan Co., New York. 1952. 2. Pauling, L., “The Nature of the Chemical Bond,” Cornell Univ. Press, Ithaca, N.Y., 1960. 3. Kleinberg, J., Argersinger, W. J., and Griswold, E., “Inorganic Chemistry.” Heath, Boston, 1960.

50

DAVID M . HERCULES

4. Li, C. P., Proctor, A., and Hercules, D. M., Appl. Spectrosc. 38, 880-886 (19841. 5. Wu, M., and Hercules, D. M., J. Phys. Chem. 83, 2003-2008 (1979). 6. Steffens, P, Niehuis, E., Friese, T., Greifendorf, D., and Benninghoven, A., J. Vat. SC;. Technol. A3. 1322-1325 (1985). 7. Bletsos, I. V., Hercules, D. M., Greifendorf, D., and Benninghoven, A., Anal. Chem. 53, 2384-2388 (1985). 8. Bletsos, I. V., Hercules, D. M., Benninghoven, A., and Greifendorf, D., “Proceedings, 5th International SIMS Conference, Washington, D.C., September, 1985,” in press. 9. Bletsos, I. V., Hercules, D. M., and Benninghoven, A., submitted for publication.