Indiastrial Problem Solving S
pectroscopy has long played a key role in the chemical industry by contributing to basic research on chemical structures, establishing structure -property relationships for product optimization,developing quality control technologies, and providing instrumentation for on-line monitoring of chemical processes, In some areas, such as process control, the chemical industry has demanded that spectroscopic instrumentation and techniques provide analyses in real time with ever higher expectations of improved accuracy and precision. Spectroscopists have developed the techniques, and instrument companies have responded with new products. In this Report, I will describe the evolution of molecular spectroscopy techniques, discuss the challenges of integrating and automating analytical procedures within the laboratory, and touch on the challenges facing analytical chemists. Current role of molecular spectroscopy
IR techniques and instrumentation have evolved considerably over the past 10 years, as shown in the box on p. 516 A and as described by Miller in a Report on the early days of IR spectrometry (1).Early commercial single-beam spectrometers used galvanometers as recorders and
Jack L. Koenig Case Western Reserve University 0003 -2700/94/0366-5 15A/$04.50/0 0 1994 American Chemical Society
As analytical problems grow more complex, the demand for molecular spectroscopy to provide rapid and informative cha ra cterization techniques is increasing
Then came the workhorse of IR spectroscopy: the optical double-beam instrument with ratio analog recording. The quality and ease of the spectral measurements were vastly improved because the interfering atmospheric or solvent absorptions were partially removed. Spectral interpretation became much easier as vast catalogs of spectra were collected, and guides to structural interpretation of frequencies enhanced the utility of the spectra (2-4). Spectroscopists learned various correlation rules that aided the interpretation of “characteristic group frequencies.” Attenuated total reflection (ATR) was added to the arsenal of the spectroscopist’s sampling techniques, and one could actually obtain the spectrum of an opaque sample (5).Quantitative measurements were cautiously (and somewhat reluctantly) made (6),and double-beam instrumentation resulted in establishing vibrational specmade point-by-point measurements. Fortunately, these instruments were replaced troscopy as a valuable tool in the analytical laboratory. with “knob and paper” instruments that With the advent of FT concepts, lasers, produced noisy traces of poorly defined frequencies and intensities. The sampling and the minicomputer, a new generation of computer-monitored spectrometers procedure was almost exclusively transemerged. These instruments offered high mission, which required considerable sensitivity, short measurement times, physical effort and some experience. Spectral processing involved visually sepa- long-term stability, and good reproducibility (7).We now have “keyboard-andrating the signal from the background. Interpretation was based on only a limited screen” spectrometers, and no state-ofthe-art instrument can be found without number of spectra and was an “experian attached PC or other computing device ence-based” method. Only qualitative for control, digitization, measurement, analysis was possible; quantitative meaand color display. A major benefit, besides surements were nearly impossible. Analytical Chemistry, Vol. 66, No. 9, May 1, 1994 51 5 A
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the ones already mentioned, is that the instruments do not self-destruct as a result of careless or irresponsible use. The range of acceptable parameters for the spectrometer is limited by the software, and therefore the instrument cannot be harmed if an operator does something wrong. Spectral interpretation has been enhanced by computerized searching systems that scan and compare large databases of spectra with a spectrum of interest for suitable “hits.” Sufficient spectral similarities then justify further examination to confirm spectral identification (8).Quantitative spectral measurements flourished with the availability of digitized spectral data. A variety of software programs evolved for background correction, multivariate analysis, curve fitting, and deconvolution techniques (S11).Because of higher energy throughput, a number of new sampling techniques began to be used, including emission, photoacoustic, diffuse reflectance, and microscopic techniques (12,13). The recognition of the molecular specificity of vibrational spectra encouraged researchers to add other physical techniques of separation such as GC (14) and LC (15);physical methods such as differential scanning calorimetry (16),thermogravimetric analysis (I 7),and mechanical deformation (18);and environmental control (e.g., temperature, pressure, and weather) to generate a vast array of hyphenated ‘techniques.The ease of coupling various physical and separation systems has yielded a mass of spectroscopic data that result from various physical parameters. Numerous spectra are generated, and data processing techniques (chemometric methods) suitable for multiple spectral systems are required (19). 516 A
However, in spite of the tremendous progress in instrumentation, the basic method of spectroscopic analysis shown in Figure 1has not changed substantially. The most time-consuming part of the analysis remains the sample preparation stage, that is, the transformation of the as-received sample into a form suitable for spectroscopic analysis. When separation procedures must be used to isolate the desired sample from mixtures, the time required is compounded. Current computerized spectral measurements still require an operator to select the parameters and make the measurements. Finally, the spectroscopist examines the spectra, probably on a computer terminal, selects the procedures for preprocessing the spectra, and carries out the quantitative analysis. Although spectrometers have improved, we have not improved the basic spectroscopic method. It is still sampleand operator-intensive.
analyses, and reports the results in a manner most easily assimilated by the user (Figure 2). All of the required components exist today for integrating the analytical process into a virtual molecular spectroscopic machine. The challenge here is to develop an automated, integrated, spectroscopic machine capable of analyzing many samples without manual intervention in sample preparation. Spectroscopic chemical analysis can be divided into th&e primary categories: sample selection, separation, and preparation for the spectroscopic system; spectral detection and substance identification; and material quantification.We seek integrated procedures in which the entire process from sample preparation through chemical analysis to data handling can be accomplished in a single automated step. Traditionally, the bottleneck has been the problem of converting the raw sample into a form suitable for spectroscopic analysis. We need to rethink this part of the process. Better results will be obtained by having the sample as optically ideal as possible, but not at the expense of substantial loss of time and effort. The time is right to re-examine our sampling procedures in terms of the potential for time and labor savings. For many modern molecular spectroscopic methods, little sample preparation is required when the spectra are obtained by reflection, emission, or microscopic methods rather than by traditional transmis-
Integrating and automating analytical procedures
Today’s chemical laboratories are striving to increase productivity, maintain quality, and simultaneously comply with an everincreasing number of regulatory mandates. They also are faced with other challenges: greater sample complexity, expanding numbers of samples, and a decreasing number of skilled professionals. The answer to these challenges is total integration and automation of analytical procedures. Such integrated and automated instrumentation can be termed a virtual machine (20)-one that receives the sample, ascertains the appropriate sampling technique, designs the most expedient measurements, performs the tasks in a prescribed way, carries out the needed data
Analytical Chemistry, Vol. 66, No. 9, May 1, 1994
Figure 1. Current approach to spectroscopic analysis.
sion methods. One should also consider fiber-optic evanescent wave spectroscopy, where the optical fiber acts as an ATR sensing element, for sampling liquids and streams. Using newly developed AgClBr optical fibers with optical losses of only 0.2 dB/m, one can obtain measurable multifrequency signals from a sample a few meters away (21). Computer-drivenrobotics for positioning and aligning the sample or the beam increase the viability of automated sampling techniques. Robotic operations and transfer systems are commonplace, and automatic sampling is available for most spectroscopic systems. Using robotics has several advantages, including the obvious savings in human effort.Costs can be cut Figure 2. Virtual machine approach dramatically,precision can be improved to spectroscopic analysis. by about a factor of 5, and sample turnaround time can be cut in half (22). The design and evaluation of the spectroscopic measurements can be done by an Expert software systems coupled with the appropriate sensors can be used to expert system, a reasoning system that uses a knowledge base to capture and repli- establish and modify the measurement procedures when necessary. Networked cate the problem-solvingability of human tandem instrumentation allows several experts (23).One example is Lahiri and simultaneous determinations using indeStillman’swork in applying expert systems pendent sampling methods. Preprocessto an assessment of the proper method of ing spectral data can eliminate the nonacquiringAAS data (24). It appears that an specific absorptions arising from optical expert system can be trained and develinhomogeneities (e.g., particles and interoped on the basis of laboratoryexperiface scattering) and can “purify“the specences, which allow the system to analyze tra in terms of interfering absorbances the results of the spectroscopicmeasurefrom the atmosphere and other sources of ments in terms of previously experienced contamination. Multivariant analysis syssampling problems and alert the spectrostems and statistical testing methods can copist if necessary. be used to automate the final data proAn obvious extension is to have the expert system make the necessary correc- cessing and calibration. tions once the problem is recognized. Anyone who has used a computer for Virtual machine analysis of a more than an hour has encountered the polymer sample Let us consider a scenario involving the inevitable “error message.” Seldom do analysis of a solid polymer sample by such messages tell you exactly what is wrong, only that something is amiss. Why FT-IR spectroscopy. First, we must select the spectroscopic sampling technique can’t the message contain options sugmost likely to lend itself to automation. gesting possible sources of the error and Transmission spectroscopy is limited beallow you to select an appropriate “correccause the thickness of the film is critical tion” that is initiated by the computer? Or and difficult to fabricate automaticallyfor better yet, have the system do a self-diagsamples of various histories. External renosis and correct the error itself. There is some risk here because the computer can- flection spectroscopy requires proper optinot read your mind, but within a precal beam alignment and flat sample surfaces. Finally, internal reflection described framework of experimental exspectroscopy presents the problem of duperiences, the expert system can recogplicating the optical contact between the nize proper measurements relative to imcrystal and the sample for solids. proper ones.
Diffuse reflection spectroscopy (DRIFT), on the other hand, can be automated most easily. In the visible region of the spectrum, diffuse reflection measurements have been the method of choice for color determinations for many years. To analyze pur solid polymer, we need a powdered sample that can be prepared by automated pulverization methods. The powdered sample can be automatically sieved; weighed; mixed with KBr; and robotically transported to the samplechamber, where it is deposited in the pan of the diffuse reflectance attachment. In this manner, we can get the solid polymer sample transformed into the appropriate form and placed in the spectrometer automatically. Now automated spectroscopic measurements begin. The first step is a survey scan to identify the sample; this will ensure that it falls into the classification anticipated by the input information provided with the sample. A correlation with the database spectra will allow this classification to be made with some certainty. Verification can be made by using spectral covariance mapping with a set of predetermined standard spectra to confirm that the proper sample is being examined (25).Covariance mapping will reveal a highly contaminated sample that would not fit the general pattern of the covariance map. A sample reject mode can be introduced to eliminate the attempted analysis of an improperly labeled, misrep resented, or corrupted sample. Having established that the sample is the proper one and suitable for the requested analysis, we must now select the optimum parameters to record the spectra. If the initial spectrum from the sample fits within the proper preestablished, quantitativelycalibrated database, control parameters can be set to meet the requirements of signal-to-noiseratio, resolution, and frequency range using the database criteria. The spectra, including the necessary replicates, can now be run using the predetermined and calibrated protocol. An examination of the acquired spectra is now made to ensure that the instrument is operating properly and that the spectra meet the specificationsfor the analysis at hand. If the signal-to-noise ratio is too low, the expert system can request more scanning for signal-averaging purposes. Spectral ,rejection criteria can
Analytical Chemistry, Vol. 66, No. 9, May 1, 1994 517 A
be established and used to stop the process at this point and query the operator about malfunctions. Preprocessing of the accepted spectra is now appropriate. The nonspecific absorbance background, artifacts, and the presence of interfering absorbencies must be determined. Digital filters can be used to remove specified frequency components from measured data (26). For automated quantitative analysis of spectra, considerable work remains to be done, and a wide variety of approaches are available. The methods of principal component regression (PCR) and partial least squares (PLS) can be combined with artificial neural networks for quantitative analysis. The basic quantitative problem is that the desired interpretation is not a linear function of the spectrum, that is, there is nonlinearity of the spectral response (27).Rather than attempting to make the system linear, one must develop a calibration system that accounts for these nonlinearities (28).Neural networks, for example, tolerate modeling of nonlinear systems (29). For automated analysis, one needs a method for constructing a multivariate calibration covering the entire concentration range of samples expected to be analyzed. First, therefore, one needs to collect a series of control samples within the appropriate range of concentration for use in the calibration set. One should separate the available control samples into a calibration set and a replicate set that can be used to test the final calibration model. It is appropriate to use the maximum number of samples available, consistent with a reasonable but necessary allotment of laboratory measurement time. These calibration samples will be analyzed by the usual spectroscopic methods. Calibration involving the training of the neural network with principal component scores results in calibrations that are substantially better than those obtained with traditional chemometric methods. This simplifies the postprocessing of the spectral data. The virtual machine described above is one approach to solving some of the challenges of the current spectroscopic analysis laboratory. With it we are on the briqk of solving and automating the quantitation problem.
Interactive process monitoring
Currently we face a new challenge in the chemical industry because of the move to a global economy. Improvements in quality are required if our products are to be competitive. To ensure these improvements, we need characterization techniques that are rapid, informative, nondestructive, convenient, easy to use, and inexpensive. What we need are on-line monitoring techniques that allow interactive process control. Interactive monitoring allows detection of a process going out of control and provides the information necessary to immediately correct the process and prevent the production of unacceptable product. The idea is to use real-time information not only to determine whether the processing is progressing properly, but
Interactive monitoring allows detection of a process going out of control and provides the information needed to immediately correct the process. ultimately to control the process dynamically. Often it is not possible to perform such monitoring because insufficient online analytical information is available about industrial chemical processes. Ideally, one would like to perform the characterization procedure in real time with a feedback loop that controls the parameters of the chemical process. The spectroscopic methods currently used in the analytical laboratory must be modified to solve this problem. Samples cannot be taken back to the lab and examined; on-
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line or in-line sampling must be done. The spectrometer must be in the ready configuration for instant measurements, and analysis must occur nearly simultaneously with sampling. Spectroscopic techniques can be used for interactive monitoring because of their simplicity and proven ability. We need to develop more sophisticated sampling systems. For process monitoring using absorption spectroscopy, we have used IR filters that yield a single frequency or near-IR monitors that look at several frequencies. Unfortunately, for near-IR monitors, the sensitivity is low. Simple systems used for this purpose are selective-wavelength spectrometers or “filtometers” (a contraction of the terms “filter” and “photometer”). These devices consist of a source of radiation, energy-collecting optics, sample compartment, and radiation detectors. Interference filters provide wavelength selection. These systems are particularly useful for monitoring a single species in the process stream because a specific wavelength characteristic of an analyte can be selected. Another potential system that would offer high sensitivity is a tuned laser source with a frequency specific to the species being monitored. Fiber sensors are developing into viable modes of remote sensing. These fibers operate as IR transmission or reflection probes at the distal end of the probe. They must have high transmission in the spectral region of interest, and they must have some flexibilityand long durability. The geometry of the fiber probes must be optimized to ensure efficient optical coupling. A typical Y-shaped (or bifurcated) reflection probe consists of a bundle of fibers that transmits the signal from the FT-IR spectrometer and another bundle that brings the reflected signal from the distal end of the sensor to the detector. In some cases, it is possible to use a bypass line from the process stream to the spectrometer. Recently a patent was issued for the use of FT-IR on line (30),and an instrument (ROS-100 process control system, Automatik Machine Corp., Charlotte, NC) that allows continuous IR analysis of a polymer melt stream from a bypass on the polymer line has been developed. Such instruments are the first step, and it is clear that others will follow. In a sense, we are seeking to make the
spectrometers chemical sensors, and the specificity of molecular spectroscopy should allow multiple species to be monitored simultaneously. Fiber-optic systems can be used to transmit the signal to and from a sample region to an instrument for analysis; the instrumental requirements for a fiberoptic chemical sensor have been reviewed by Arnold (31).Powell et al. reported a spectroscopic system that includes an optical sampling system for remote samples regardless of their size (32).These concepts can be extended for multifrequency detection and analysis. The mid-IR region is sensitive and highly specific but, as indicated above, sampling is a problem. Fortunately, we do not need to identify every spectral feature of the process, With the chemometric analysis methods now available, we can cross-correlate the spectral data with the property of interest. Spectral measurements then allow prediction of performance properties. As a result, process control can be realized not in terms of spectral properties, but in terms of enduse engineering properties.
the image are much smaller than the objects in the image. Here the local variance will be low because many adjacent brightness measurements will have the same value. In the second, known as the Lresolution case, the pixels are much larger than the objects. Here there will be many objects in each pixel, and the brightness will depend on the number of objects. For the spectroscopy of bulk materials, the L-resolution case generally applies. The image is assumed to be obtained with a pixel of fixed shape. In spectroscopy, we typically regard the pixels in the image as being square or rectangular. This assumption is made largely for the convenience of apportioning the image space to values associated with a regularly spaced grid. In truth, the instantaneous field of view of a detector is blurred by optical and electronic effects.
An example of the utility of spatially resolved IT-IR microscopy is shown in Figure 3 (35).The polarized optical image of the polymer-dispersed liquid crystal is shown in Figure 3a. A single particle isolated with the aperture (143 pm) is then examined by FT-IR spectroscopy to show the distribution of polymer (epoxy resin) and liquid crystal E7, which is a mixture of liquid crystal molecules. The polymer absorption for the 3428 cm-’ 0 - H stretching vibration with $spatial resolution of - 10 pm is shown in Figure 3b, and the liquid crystal absorbance map of the 2228 cm-I nitrile stretching mode for the same spatial domain is shown in Figure 3c. The bright spots in the liquid crystal spectrum reflect the high orientation of the liquid crystals that result from defects called “boogums,” which are “defect points” in the crystal-like structure of the oriented liquid crystal molecules within a
Fz
Determining spatial effects
The performance of engineering materials is determined largely by the number, type, and distribution of chemical or physical defects. These defects usually occur at boundaries or interfaces between phases. To produce a material product of high quality, we must understand the nature (morphology) and the spatial arrangement (images) of the supramolecular structures throughout the engineered sample. This information can be obtained by using spatially resolved spectroscopy with microscopic techniques (33,34). In spatially resolved spectroscopy, an image is a series of measured digital “brightness” values, each of which is associated with a spatial position (x,y) .The brightness values can be associated with absorption, reflection, or fluorescence of the molecular groups. In interpreting the image, we attempt to express it in terms of a spatial arrangement of two- or threedimensional objects superimposed on a background. These spectroscopic images fall into two general classes. In the first, referred to as the H-resolution case, the pixels in
Figure 3. Images of a polymer-dispersedliquid crystal. (a) Polarized optical image. (b) Polymer absorbance map at 3428 cm-’. (c) Liquid crystal absorbance map at 2228 cm-’.
Analytical Chemistry, Vol. 66, No. 9, May 1, 1994 519 A
given droplet. Using spatially resolved FT-IR microscopy, not only the concentration but also the spatial distribution of the concentration of multiple species can be identified and measured. All of the molecular spectroscopic techniques can provide spatial measurements by using either a scanning beam of known shape or a computer-controlled microscope stage. Currently, however, the spectral maps or images generated are poor in contrast or require considerable time for acquisition. Improvements are on the horizon, and rapid progress is being made in areas such as Raman and NMR spectroscopy. These spatial results must be integrated with machine vision techniques before they can be used properly in industry. Time-resolved spectroscopy
One of the dreams of spectroscopists is very rapid spectral acquisition. One should recognize that in the length dimensions, magnificationsof lo6 are necessary to reach atomic size. In the time dimension, in contrast, an increase of a factor of 10l2is necessary to go from the human time scale to the atomic time scale. Thus the challenge for rapid spectral acquisition to reach the atomic time scale is great. The real problem in analysis is that the sample can undergo physical or chemical changes during spectral acquisition, so that the recorded spectrum reflects some average state of the sample rather than the state of the sample at any given time. Conversely, we are often interested in applying some time-dependent physical change (i.e., stress or temperature) to the sample and observing the changes with use of spectroscopic techniques. Two basic methods are available for making such temporal measurements: multidimensional measurements and stroboscopic methods (36).Two-dimensional techniques allow one to obtain correlations between applied physical perturbations and the spectroscopic responses. These techniques have reached high levels of sophistication in NMR spectroscopy, and excellent progress has been made in IR spectroscopy as well. Stroboscopic measurements allow rapid determinatiQn of spectroscopic changes in short time intervals by use of light pulses or
step scanning. Both of these methods are undergoing development, and suitable instrumentation is available commercially. There are three methods of timeresolved absorbance spectroscopy: continuous scan, stroboscopic, and interrupted stepscan methods. In the continuous-scan method, the spectra of the transients are sampled point by point over the spectral range of interest. With rapid scanning,
The innovative
talents of the spectroscopist w ill be needed to develop new and improved methods, techniques, and instrumentation. results can be obtained in a time frame of a few milliseconds. In the stroboscopic approach, a repetitive process is triggered during the acquisition, a narrow portion of the spectrum is recorded, and sequential frequency segments are sampled until a complete spectrum is reconstructed. Results can be obtained using the stroboscopic method in FT-IR spectroscopy in as little as 50 ps. In the stepscan mode, the data are collected at the desired intervals after an impulse, and the interferometric mirror is held stationary for a preset time (which determines the time resolution) at each of its sampling positions. The process is repeated as many times as necessary for the desired S/N. The data are sorted into time intervals and transformed to time-resolved spectra. The maximum time resolution for stepscan FT-IR appears to be 1ns (36). Spectral sensitivity
The universal problem in spectroscopic measurements is sensitivity. Spectro-
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scopic bands are inherently weak in intensity, and the amount of sample probed by the beam is small. Therefore, the limits of detection are low and improvements must be made to achieve higher detection power. For example, in the study of monolayer adsorption, the amount of material is only 2 mg/cm2; thus, the quantity of material in the beam is about 2.6 x l O I 4 molecules/cm2. The most intense IR band for an adsorbed monolayer would be - 5 x lo4 absorbance units in intensity (37).Even high-performance FT-IR detectors allow only a peak-to-peaknoise level of 5 x absorbance units per scan, so the signal is an order of magnitude lower than the state-of-the-artdetector noiselimited spectrum. Therefore, enhancement of the signal by using resonance techniques, multiple pass geometries, long optical paths, and brighter sources is needed. Alternatively, we can decrease the noise with higher throughputs, multiplexing, lower-noise detectors, and signal averaging. Where are we going?
The demands on molecular spectroscopy are constantly increasing as structural and process problems become more complicated. But, as in the past, the innovative talents of the spectroscopist will be needed to develop new and improved methods, techniques, and instrumentation. The role of the computer in this type of analysis is evolving. Users request results, and the computer can use its artificial intelligence to select the method (s), schedule the tasks, review the data and performance of the methods, and relay high-level information back to the user (38).Instrumentation will continue to improve and become smaller and more convenient. Molecular spectroscopy as we know it will change, but that is good. No one wants to return to the days of single-beam spectroscopy with galvanometers. Nothing gives more l i e to research than growth and change, and so it is with molecular spectroscopy. The author acknowledges the support of ALCOM (Advanced Liquid Crystalline Optical Materials) DMR 8920147 and the students whose work was responsible for the citations in this paper.
References (1) Miller, F. A. Anal. Clzem. 1992,64,824A. (2) Bellamy, L. J. Advances in Infiared Group Frequencies; Barnes and Noble: New York, 1968. (3) Bellamy, L. J. The Infrared Spectra of Complex Molecules, 3rd ed.; John Wiley and Sons: New York, 1976. (4) Colthup, N. B.; Daly, L. H.; Wiberley, S. E. Introduction to Infrared and Raman Spectroscopy, 2nd ed.; Academic Press: New York, 1975. (5) Harrick, N. J. Internal Rejection Spectroscopy; Harrick Scientific Corp.: New York, 1979. (6) Smith, A. L. Applied Infrared Spectroscopy: Fundamentals, Techniques and Analytical Problem-Solving;Wiley Interscience: New York, 1986. (7) Griffiths, P. R.; de Haseth, J. A. Fourier Transform Infra red Spectroscopy; John Wiley and Sons: New York, 1986. (8) Sebesta, R. W.; Johnson, G. G., Jr. Anal. Chem. 1972,44,260. (9) Computerized Quantitative Infiared Analysis; McClure, G. L., Ed.; American Society for Testing and Materials: Philadelphia, 1987. (10) Gillette, P. C.; Lando,J. B.; Koenig, J. L. In Fourier Transform Infiared Spectroscopy, 4th ed.; Ferraro, J. R; Basile, L. J., Eds.; Academic Press: New York, 1985; p. 1. (11) Haaland, D. M. In Practical FT-IR Spectroscopy:Industrial and Laboratory Chemical Analysis; Ferraro, J. R.; Krishnan, K., Eds.; Academic Press: New York, 1990. (12) Practical FT-IR Spectroscopy: Industrial and Laboratory Chemical Analysis; Ferraro, J. R.; Krishnan, K., Eds.; Academic Press: New York, 1990. (13) Coleman, P. Practical Sampling Techniques for Infrared Analysis; CRC Press: Boca Raton, FL, 1993. (14) Griftiths, P. R. In Fourier Transform Inpared Spectroscopy; Ferraro, J. R.; Krishnan, K., Eds.; Academic Press: New York, 1978; p. 143. (15) Hellgeth, J. W.; Taylor, L. T. Anal. Chem. 1987,59,295. (16) Compton, D.A.C.; Johnson, D.; Powell, J. R In Structure - Propedy Relations in Polymers, Spectroscopy, and Performance; Urban, M. W.; Craver, C. D., Eds.; Advances in Chemistry Series 236; American Chemical Society: Washington, DC, 1993; p. 661. (17) a. Compton, D.A.C.; Johnson, D..J.; Mittleman, M. L. Research &Development, February 1989, p. 89; b. Compton, D.A.C.; Johnson, D. J.; Mittleman, M. L. Research &Development, April 1989, p. 68. (18) Siesler, H. W.; Holland, M. Infrared and Raman of Polymers;Marcel Dekker: New York, 1980. (19) Shard, M. A.; Illman, D. L.; Kowalski, B. R. Chemometrics;John Wiley and Sons: New York, 1986. (20) Doubraava, C.; Kay, M. Am. Lab., Sept. 1993, p. 29. (21) Krska, R.; Taga, K.; Kellner, R. Appl. Spectrosc. 1993,47, 1484. (22) Stevenson, J. Am. Lab. January 1993, p. 6. (23) Boose, J. H. Advances in Human Factors/ Ergonomics; Elsevier: Amsterdam, 1986; p. 2.
(24) Lahiri, S.; Stillman, M. J. Anal. Chem. 1992,64,283 A. (25) Marcott, C.; Noda, I.; Dowrey, A. E. Anal. Chim. Acta 1991,250,131. (26) Small, G. W.; Arnold, M. A.; Marquardt, L. A. Anal. Chem. 1993,65,3279. (27) Gemperline, P. J.; Long, J. R.; Gregoriou, V. Anal. Chem. 1991,63,2313. (28) Sekulic, S.; Seasholtz, M. B.; Wang, 2.; Kowalski, B. R.; Lee, S. E.; Holt, B. R. Anal. Chem. 1993,65,835A. (29) Borggaard, C.; Thodberg, H. H. Anal. Chem. 1992,64,545. (30) U. S. Patent 4, 717,827. (31) Arnold, M. A.Ana1. Chem. 1992,64, 1015A. (32) Powell, G. L.; Milosevic, M.; Lucania, J.; Harrick, N. J. Appl. Spectrosc. 1992,46, 111. (33) Infrared Microscopy Theory and Applications; Messerschmidt, R, G.; Harthcock, M. A., Eds.; Marcel Dekker: New York, 1988. (34) The Design, Sample Handling and Applications of Infrared Microscopes-ASTM STP 949; Rousch, P. B., Ed.; American Society for Testing and Materials: Philadelphia, 1987. (35) Wall, B.; Koenig, J. L., unpublished work. (36) Palmer, R. A.; Chao, J. L.; Dittmar, R. M.; Gregoriou, V. G.; Plunkett, S. E. Appl. Spectrosc. 1993,47, 1297.
(37) Tripp, C. P.; Hair, M. L. Appl. Spectrosc. 1992,46,100. (38) Littlewood, B.; Strigini, L. Sci. Am. 1992,11,62.
Jack L. Koenig is the J. Donne11 Institute Professor in the Departments of Macromolecular Science and Chemistry at Case Western Reserve University (Cleveland, OH 44106-7202). He received his B.A. degree from Yankton College (South Dakota) in 1955 and his Ph.D. in theoretical spectroscopy from the University of Nebraska in 1959. His research interests include spectroscopic determination of structare-activity relationships of polymers using NMR, IR, and Raman techniques. ,,I
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