Adrian P. Wade and David B. Sibbald Laboratory for Automated Chemical Analysis Chemistry Department University of British Columbia Vancouver, British Columbia, V6T 1Y6 Canada
Mark N. Bailey Department of Metals and Materials Engineering University of British Columbia Vancouver, British Columbia, V6T 1W5 Canada
Ron M. Belchamber Analysis and Instrumentation Branch British Petroleum Research Centre Chertsey Road Sunbury-on-Thames Middlesex,TW16 7LN United Kingdom
Shabtai Bittman Agriculture Canada Research Station Aggasiz, British Columbia, VOM 1AO Canada
John A. McLean Forestry Department University of British Columbia Vancouver, British Columbia, V6T 1W5 Canada
Peter D. Wentzell Chemistry Department Dalhousie University Halifax, Nova Scotia, B3H 4J3 Canada
0003-2700/91/0363-497A/$02.50/0 0 1991 American Chemical Society
Although analytical chemists have studied in detail the interaction of matter with electromagnetic radiation, heat, electrical energy, and electrical or magnetic fields, the relationship between sound and matter still receives comparatively little attention i n analytical chemistry. Sound, too, is absorbed and can be emitted by physical and chemical processes, and there are parallels between optical and sonic interactions (1). Existing sonic and ultrasonic techniques include ultrasonic reso nance spectroscopy (2), acoustic attenuation (31, acoustic microscopy (4), photoacoustic spectroscopy (5),
7= the use of surface (6) and bulk (7) acoustic wave devices and (immunolchemically selective microgravimetric sensors (8), and chemical acoustic emission (9). Acoustic emission (AE)is now the subject of research in areas such as analytical chemistry, experimental botany, entomology, and wood science. The diversity of these applications and the lack of interdisciplinary interaction has led to parallel development of similar instrumental techniques by different groups of scientists, each within their own discipline. This, in turn, has resulted in
fragmentation of the literature. In this REPORT,we will provide an inter disciplinary perspective on acoustic emission, including a brief overview of signal capture technology, a discussion of data analysis methods, and examples of applications. Historical context Sound generation by physical processes is an everyday phenomenon, and evaluative techniques for AE are of interest across a broad spectrum of the natural and applied sciences. Acoustic emissions are elastic waves generated by a rapid release of energy at a localized source; they are associated with bonding and reactivity. Emission occurs not only in the audible region (- 10 Hz to 16-20 kHz), but also in the ultrasonic range (above 20 kHz). AE can be simple to detect but difficult to interpret. Modern AE research began in 1936 when Forster and Scheil (10)observed that sound waves were generated during formation of martensite steel. In 1953, Kaiser (11)characterized t h e emission b e h a v i o r of stressed materials; he discovered that on stressing, relaxing, and restressing a material, detectable AE is absent until previously applied stress levels are exceeded. This phenome non is known as the “Kaiser effect.” In 1964, Green (12)determined the integrity of a Polaris Model A3 solid rocket motor case using AE. Subse-
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REPORT quent progress in understanding AE from materials has led to AE procedures for routine inspection of com mercial aircraft, pressure vessels, load- bearing structures, and components. The 1970s saw reports of AE from chemical systems, but it was not until 1981 that the possible extent of emission activity from chemical reactions was realized, when Bet teridge and co-workers studied a wide range of reactions (9). Since 1981, the Journal of Acoustic Emission (published by the Acoustic Emission Group, University of California, Los Angeles) has reported advances in AE in materials science and nondestructive testing, and has provided a forum for promotion of AE.
quency dependent. If the transducer is close to the source it is difficult to separate energy arriving by different transmission modes. Much remains to be learned regarding the generation and travel of transient waves in solids. A general, quantitatively accurate description of the conduct of AE in solids is currently unavailable. In chemistry, a wide variety of processes cause AE. These include gas evolution (e.g., in pyrolysis, electrolysis, and enzyme reactions), crystal growth (e.g., during solid-solid phase transitions), crystal fracture during recrystallization, cavitation, boiling, and swelling. This diversity further complicates development of theory, although progress is being made.
Theory Sound is propagated through air as a longitudinal wave, that is, as successive compressions and rarefactions of molecules along the direction of propagation. The velocity of sound varies with the medium through which it is propagating: In dry air at sea level at 20 "C it is 343.2 ms-'(Machl); in water, 1498 ms-'; in copper, 3800 ms-'; and in iron, 5000 ms-'. For ideal gases, the velocity of sound can be shown to be proportional to the square root of the absolute temperature and independent of pressure. However, factors such as temperature and moisture content change the velocity in air, and both increasing temperature and salinity increase the speed of sound in water (13). Sound waves propagate from their source in all directions, and the observed sound intensity decreases with increasing distance from the source. In bounded systems, the observed signals may contain components resulting from echoes and othe r phenomena. Materials exhibit nonuniform absorbance (attenuation) of different sound frequencies, and experimental apparatus can have resonant frequencies. The first energy to arrive at the transducer i s t h e compressional wave, known as the P-wave. In liquids and gases, this is the only mode of propagation, but in bulk solids transverse waves or shear waves (S-waves) also occur. These propagate a t a little over half of the P-wave's velocity, and hence reach the transducer after the first arrival of P-wave energy. In bounded solids, surface waves and more complex modes such as plate waves may be the dominant forms of propagation. Their velocities are usually lower than those of shear waves and in some cases may be fre-
Experimental considerations Regardless of the emission source, similar apparatus is used to detect AE: a sensor, signal-processing circuitry, and a recording device. Figure 1 shows some of the equipment options that can be used. Frequency components present in AE signals from chemical reactions can reach 1 MHz (14)and perhaps beyond. Figure 2a shows a chemical AE signal from reaction of anhydrous aluminum chloride with water, and Figure 2b its power spectrum. A
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background noise signal (Figure 2c) and its power spectrum (Figure 2d) are shown for comparison. Animals use a wide frequency range in communication and navigation. For example, sounds from the finback whale have been recorded in the 18-23-Hz frequency range, the male field cricket uses 3.6-4.4 kHz, and the bat uses 39-78 kHz (13). Frequencies used for health monitor ing can range from 1Hz to 35 kHz. Sensors for AE. Conventional audio microphones developed for the recording studio offer a n essentially flat frequency response from a few Hz to 20 kHz. These provide superior signal representation and convenience, at moderate cost, but are prone to interference from ambient noise. Even inexpensive devices do not suffer severe sensitivity losses below 12-16 kHz and have been used in chemical reaction monitoring (15). Commercial broad - band piezoelectric sensors can reach 1-2 MHz, with special transducers capable of 4 MHz; piezoelectric devices used as sound sources can reach 25 MHz. The frequency response of piezoelectric sensors is typically not flat (16), and devices with specific resonant frequencies and high immunity to ambient audible noise are widely
Figure 1. Experimental apparatus options for AE monitoring.
ANALYTICAL CHEMISTRY, VOL. 63, NO. 9, MAY 1, 1991
column, it pays to go through Whatman, If you're not using the right sample prep with your HPLC system, you could be wasting valuable time and money. That's why it pays to go through the Whatman Sample Prep System. Whatman sample prep filters help yield purer filtrates, to ensure reproducible results-which means less repeat testing for you and more life for your column. The Whatman Sample Prep System includes: PuradiscT M 25 (AS/TF/PP)-Sy ringe-tip filters are available with various membranes to help avoid generation of extractables: polysulfone,for aqueous applications (Puradisc 25 AS); PTFE, for solvent-based applications (Puradisc 25 TF); and polypropylene, for solvenVaqueous applications (Puradisc 25 PP). SPEC-The only solid-phase extraction device that contains an integral 0.2 micron polypropylene membrane to eliminate the need for secondary filtration. Solvent and Aqueous IFD'" -The only filter device that filters and degasses mobile phases in-lineto save time and extend the life of the HPLC column. So before you go through another HPLC column, go through Whatman. Contact Whatman Inc. for more informationon receiving a FREE column.
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REPORT used. Piezoelectric transducers that have high sensitivity and a flat frequency response over a wide range of frequencies are being developed in both industrial and academic research laboratories. Lead zirconate titanate (known as PZT) is commonly used in piezoelectric sensors. Chemical synthesis of the sensor material is a multiplestep process; coprecipitation of zirco nium and titanium salts with cupferron is one way to reliably achieve the required zirconium - to - titanium ra tio, which is known to be critical for good piezoelectric properties. Final fabrication of the device is presently both an art and a science. Signal processing circuitry. The weak signal generated by the sensor is first fed to a preamplifier and/or conditioning amplifier. A band-pass filter serves to provides good discrimination against noise (e.g., by eliminating audible frequencies) and eliminates aliasing of high frequencies back into the observed frequency range. Some devices also output a pulse each time an incoming signal is detected, the magnitude of which is often proportional to the peak amplitude of the signal. Spectrum analyzers, consisting of a set of band-pass filters, can be used to separate the acoustic energy detected into perhaps 8, 10, or 16 discrete frequency bands. These bands usually cover the entire working range of the instrument. Audible-
range spectrum analyzers are common in the sound reinforcement industry, where they a r e used i n conjunction with graphic equalizers to provide optimal concert sound. Similar units capable of handling higher frequencies (to 100 kHz) have been used for rotating machinery in industrial materials processing and machine health monitoring, but this technology has yet to be applied to chemical analysis. Recording devices. Signals can be counted, integrated, captured, or characterized i n many different ways, including by event counters, integrators, and high-frequency digitizers. The counting of acoustic events is simple and convenient, and it provides superior quantitative measure ments when acoustic events have similar magnitudes and frequencies. Early work in experimental botany involved counting of audio - frequency emissions by ear. Counting of ultrasonic emissions is done using highspeed comparator integrated circuits and data loggers (17).A threshold is set to some level above background such that when an emission occurs this level is breached and the signal is noticed. Circuits include a delay timer to ensure that only a single count is registered per incoming oscillating signal. Current research seeks to develop low-cost portable event counters (18). Counting events may be inade-
Figure 2. Comparison of AE and background signal. (a) Typical waveform from hydration of solid aluminum chloride and (b) its power spectrum; and (c) spurious background signal and (d) its power spectrum.
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quate, however, for some quantitative applications because acoustic signals differ in both peak amplitudes and total energies. Instruments a r e commercially available that approximate the total energy in each emission by weighted summation of the outputs of multiple event counters, each set to trigger at a different acquisition threshold. Another approach useful for fast emitting systems is to integrate the output either from a peak level detection device (19) or, better, a “true R M S meter. Interference from intense but very short -lived transients resulting from electrical switching can be reduced by considering the total energy of acoustic events, rather than their maximum amplitude alone. Early chemical AE studies used minicomputers with dedicated data acquisition cards (9).Portable systems based on digital storage oscilloscopes have recently been constructed for use in analytical chemistry (14),pulp and paper science (ZO), and experiment a1 bot any. Typic ally, these capture multiple 1024-point signals with 8-bit resolution and send them to a microcomputer for later processing and interpretation. Instead, 10-MHz 8-bit flash digitizer cards internal to a microcomputer may fulfill this purpose. Data analysis There are several common approaches to analyzing audible and ultrasonic signals, regardless of their source. Acoustic spectral analysis. A convenient way to look at the frequency components in AE is to apply a fast Fourier transform (FFT) to each time - domain signal. Processing of a 1024-point raw signal yields information at 512 frequencies; the FFT thus emulates the function of a dedicated multichannel analyzer based on multiple band-pass filters but at a much lower cost. Plug-in signal - processing cards can achieve FFTs in 3 ms or less (i.e., 2000 times faster than a 12-MHz Intel 80286based microcomputer). The transformed data are usually viewed as a power spectrum, and power observed within various frequency ranges can be summed to provide better quantitative information. Averaged power spectra are preferred, because of the natural variability of individual power spectra, and have been found to be reproducible over a period of months (14). Commonly, system parameters such as temperature in recrystallization and extension rate in polymer stressing studies (21)as well as applied po-
tential in electrolysis (22) influence the emission rate and intensity far more than they do the power spectrum. Subtraction of background spectra has been reported (16). Waveform analysis. The ability to identify the source of signals correctly is important. As shown in Figure 2, it enables signals to be separated from spurious background emissions (21). I t also enables processes that produce AE by a number of different mechanisms to be studied (231, and changes in process conditions to be detected. Empirical approaches offer a useful alternative to complex theoretical models. Everybody knows that a bottle of lager beer that does not fizz on opening will probably not taste good. This is a purely learned response. Emission signals contain much infor mation, but only certain features of the signal may be relevant to the analytical task at hand. Pattern recognition can automate the learning procedure and is ideal for dealing with AE signals. Betteridge and co-workers (9)were the first to use a pattern recognition technique to show the similarity between signals from various subsets of 34 different acoustically emissive chemical reactions. This work could be considered seminal, in that it addressed the issue of signal analysis from fresh perspectives. These methods are all the more attractive in view of recent phenomenal advances in signal acquisition hardware and laboratory computing pow er. Many p a t t e r n recognition techniques that are now commonplace for analyzing and classifying more conventional chemical data are also applicable to chemical (and other) sources of AE. Although there have been relatively few papers in the scientific literature on pattern recognition of AI3 signals, some patents have been filed. Most of these are aimed a t process and plant monitoring, where AE can sometimes provide unique physical and chemical information. Here the real- time, nonintrusive nature of AE monitoring can provide a very real advantage over other techniques. Statistical pattern recognition techniques fall into t h r e e main groups: exploratory methods, such as principal components analysis (PCA), are used to visually examine complex data sets; unsupervised learning methods, such as dendrograms (24), are used to detect the presence of distinct categories in the data; and supervised learning techniques, such as SIMCA, are used to assign data to
predetermined classes. These have all been applied to AE signal analysis (23, 25). Neural network techniques also show much promise (26). Raw digitized signals (signal amplitude vs. time) are not well suited to pattern recognition. Thus, the shape of each signal is first represented by parameters that are calculated directly from the time record of the signal (e.g., rise time, half-life, RMS energy, and maximum amplitude). Typically, a n appropriately chosen combination of perhaps only six descriptors will effectively describe the signal well enough that it can be correctly assigned to a signal class. For example, many authors studying the fracture mechanics of composites have used the five descriptors shown in Figure 3 (peak amplitude, signal duration, energy, counts above threshold, and rise time) to describe waveforms collected during static or fatigue failure of composites or their constituents. The frequency distribution of the signal is also characteristic if a broad-band transducer is used. Descriptors such as median frequency and bandwidth can be extracted from the power spectrum. The utility of multiple descriptors for class separation has been demonstrated (21, 23). This multidimensional “descriptor space,” coupled with the possibilities of spectral analysis and time series analysis, provides much to interest chemometricians. Without knowledge of the position of the acoustic source there is usually little to be gained in trying to describe the signal more precisely. Source location. Acoustic source
location using sensor arrays has been achieved in materials science, seismology, ultrasonic flaw detection, structural evaluation, process engineering, passive sonar, and location of animals (13). I t is complicated when the medium through which the wave propagates is not homogeneous; in particular, problems are caused by passage through regions that have different wave velocities and by faults in the medium material. Applications
Analytical chemistry. An early use (ca. 1350 A.D.) of AE for chemical analysis is reported in the Codex Germanicus (27):“If thou wilt try whether sulphur be good or not, take a lump of sulphur in thine hand and lift it to thine ears. If the sulphur crackle, so that thou hearest it crackle, then it is good; but if the sulphur keep silent and crackle not, then it is not good. . . .” AI3 from chemical reactions occurs as a series of short bursts, of 0.1-1.0 ms in duration, with peak energies per burst of -0.02-1250 pW (9). Some reactions emit many thousands of bursts over a period of hours. 0thers, perhaps stimulated by a sudden change in pressure or temperature, are over very quickly. The complex waveforms seen result not only from the source of emission and background noise, but also from the many ways the signal can reach the receiver. Recent chemical AE studies have focused on general investigation of the AE phenomenon (9,28-30),thermosonimetry (16, 31, 32), instrument development (19, 33), analysis of frequency domain information (14, 151,
Figure 3. AE signal and five parameters used by materials scientists to describe its waveform. ANALYTICAL CHEMISTRY, VOL. 63, NO. 9, MAY 1,1991
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chemometric methods for characterization and separation of signals (21, 341, quantitative analysis (15, 23), and use of AE as a kinetic (21, 35) and mechanistic (15, 16, 22) probe. A commercial AE instrument designed for analytical chemistry research is still not available. More work is needed before the chemical origins of AE are fully understood. From the perspective of analytical chemistry, the potential of AE as a mode of transduction for quantitative chemical analysis is of special inter-
est. AE is an extensive property. A direct proportionality between acoustic activity and amount of material undergoing reaction is expected but is difficult to obtain in practice: Selfabsorption by the macroscopic sample and other effects may contribute to nonlinearity, which has been observed for both low and high concentrations. Quantitative measurements from passive monitoring of spontaneous and stimulated chemical AEs have been demonstrated. Belchamber and
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co-workers (23) showed that, in the hydration of silica gel, the total integrated acoustic signal increased with the weight and particle size of the silica gel and decreased with the moisture content of the particles. Other researchers have used acoustic detection in titration (36))precipitation (211, and electrolysis (22). An earlier study showed that a frog’s heartbeat rate changed with the amount of calcium injected into it (37). This premature foray into quantitative biosensors was not pursued; aside from the experimental difficulties noted in the paper, each set of experiments required a new frog. More recently, a novel chemically modified AE sensor was reported (15) that used bovine liver catalase immobilized onto the surface of a piezoelectric transducer (Figure 4) to selectively determine hydrogen peroxide in solution. The catalase accelerates conversion of the peroxide to oxygen and water. Audible effervescence is detected from 0, bubbles produced a t the transducer. A linear working curve based on counting acoustic events was established for 2-125 mM hydrogen peroxide, and a large relative standard deviation and negative deviation from the line was observed a t a hydrogen peroxide concentration of 150 mM. Neither gas evolution nor AE signals were observed from solutions containing below 2 mM hydrogen peroxide. The high detection limit was attributed to diffusion processes and precluded the coupling of the system to peroxideproducing oxidase reactions. Acoustic emission as a kinetic probe.Vanslyke has shown that one can fit model equations to cumulative AE curves (35). He determined that for the hydration of alkali metal hydroxides, the best fit was obtained using a model that represented two simultaneous first -order processes: Integrated peak AE intensity(t1 =
A exp(-k,t) + B exp ( 4 , t ) (1) where k , and k, are rate constants that differ by - 1 order of magnitude and (A + B) indicates the total emission after a long time. Crystal fracture and gas evolution are two processes proven to occur during this reaction, but there may be others. Measures other than “peak intensity” have also been used (211, and this approach has been applied to other chemical systems. The start-up kinetics of an electrolysis cell have been monitored using a digitizer capable of a record length of up to 2 million samples (Le., a 2 097 152point signal), 12-bit precision, and
acquisition rates > 2 MHz (22). Acoustic emission as a mechanistic probe. Different emissionproducing mechanisms can be detect ed by separating t h e i r acoustic
Figure 4. Selective chemical sensor based on passive AE.
signals into classes on the basis of their shapes, frequency domain characteristics, and amplitude (23).These classes are associated with mechanisms by visual or other observations. Lee and co-workers (16) were able to distinguish crystal growth from crystal fracture during experim e n t s t h a t combined AE w i t h dilatometry. Wade and co-workers (21) suggested t h a t the dominant emission mechanism during recrys tallization of salts such as potassium nitrate may be crystal fracture occurring during growth. Wentzell and coworkers (15) found t h a t different bubble evolution sites on an enzyme catalytic surface produce repeatable emission waveforms, and each has its own particular acoustic signature. The hydration of silica gel is a process in which violent audible emission from gel granule fracture is accompanied by considerable emission at ultrasonic frequencies (14). A detailed study of this reaction (23)used five descriptors (maximum amplitude, variance, half-life, median frequency, and bandwidth) to characterize each of 80 AE signals. Five principal components (eigenvectors) were then extracted from the covari-
ance matrix of these data. These orthogonal (uncorrelated) linear combinations of the original five variables were then ranked. Most of the (vari-
Figure 5. Principal-component projection of data from hydration of silica gel granules. Groups marked 1 and 4 are higher energy signals that were visually related to the cracking of the silica gel granules. Groups 2 and 3 are of lower energy and associated with gas evolution processes. Group 5 is of uncertain origin. (Adapted from Reference 23.)
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REPORT ance) information is retained in the first few members. Figure 5 shows the first two principal components (PC1 and PC2). These contain the largest contribution to the variance and define a plane onto which the original data points are projected. The closer together the signals appear in the plot, the more similar they are. Clustering of the signals is apparent, and mechanisms were readily assigned to the clusters. The large intraclass variance makes it unlikely that principal -component projection alone would provide enough evidence to confirm the presence of the four distinct signal types. Green plants. Many species of green plants (e.g., tomatoes, cucumbers, sunflowers) emit acoustic signals during periods of water stress; emission frequencies extend to a t least 300 kHz. This phenomenon was f i r s t observed by Milburn a n d Johnson in 1966 (38).Over the next seven years, Milburn and co-workers provided mounting evidence t h a t these sounds were caused by cavitation of the water columns in special conduits in plants, known as xylem vessels, that conduct water from the soil to the leaves. The water columns are under considerable tension (negative pressure, suction) when the rate of evaporation from leaves exceeds the rate of water uptake from the soil. Under conditions of water stress, such as a prolonged drought, tensions reach -1 to -2 MPa and may go as low as -10 MPa. Acoustic emissions are produced by the sudden release of tension on the xylem cell walls when cavitation of the water columns occurs. It is widely believed that xylem vessels with the greatest diameters and greatest hydraulic conductivity are the most susceptible to cavitation. Cavitation is important because it reduces the capacity of plants to conduct water. In early investigations Milburn and co-workers focused on only the audible range; they used conventional audio equip ment-a modified pickup head from a record player served as the transducer. Background noise resulted in no other reports on this phenomenon until Tyree and Dixon (39) used instrumentation designed t o detect structural failure in materials for ultrasonic AE. Since 1983,many studies have demonstrated the relationship between declining tissue water potential and AE. Although cavitation can be demonstrated in other ways, AE monitors the progression of water stress in plants nondestructively. 504 A
Ritman and Milburn (40) recently reported that audible and ultrasonic emissions need not coincide. They suggested that audible-range emissions are closely related to cavitation of the major vessels, whereas some ultrasonic emissions are not produced by cavitation. In contrast, Tyree and Sperry (41) have suggested that power spectra provide little information about cavitation events because the frequencies are attenuated differently depending on the hardness of the tissue conducting the sound, and transducers can suffer from harmonics or “ringing.” Better and cheaper instrumentation for acoustic detection of plants (41) will help elucidate the relationship between cavitation and other physiological reactions to drought such as reduced growth, leaf senescence, stomatal closure, cell membrane dam-
age, loss of turgor, and reduced hydraulic conductivity. Already, there is research into use of acoustic detection for programming of irrigation in greenhouses. When the relationship of cavitation to other drought reactions is better understood, this information may become useful for breeding superior drought -resistant crops. Wood products. The rate of drying of woody tissues can be monitored via the A E s caused by fiber failures. Winter-cut trunk samples of eastern white pine and three hardwoods were found to emit ultrasonic acoustical emissions as they began to dry out (42). Waxing the ends of the samples sharply reduced the emission rate, indicating that much of the drying out of t h e tissues occurs through the cut open ends of the sap conduits. The rate for the (softer) pine was higher than those of the
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Figure 6. Principal-component analysis of condnuous AE from a leaking gas release valve and representative time domain plots of the acoustic emissions. (a) Acoustic power spectrum, (b) plot of principal component 1 versus principal component 2, and (c) plot of principal component 1 versus principal component 3. 1, slow leak; 2, moderate leak; 3, fast leak; E, electrical noise; T, remote tapping; B, remote hammering.
ANALYTICAL CHEMISTRY, VOL. 63, NO. 9, MAY 1,1991
hardwoods for the first three weeks. Difficulties in drying out log disks have been resolved by using acoustic monitoring in a feedback control loop (43). A high rate of fiber fracture emissions indicates when drying rates are too high; the drying process is then slowed by introducing steam (43, 44). High-quality, well-dried samples are thus obtained in minimum time. Leak detection. Processes t h a t produce emissions a t a very fast rate pose special problems because it is often impossible to resolve individual events. Here, a broad-band transducer is used to monitor the signal intermittently for a short period of time. A frequency analyzer or FFT of the digital record then provides frequency domain data suitable for pattern recognition. This approach has been used to monitor gas leaking through a defective valve (45). The power spectra for all signals (Figure 6a) were divided into 10 equal bands. The acoustic power in each band was used as one of the variables for PCA. A plot of PC1 versus PC2 (Figure 6b) shows that spurious background noises are easily discriminated against. There is a unique vector (the dotted line) that serves as a n excellent quantitative measure of the leak rate. A plot of PC1 versus PC3 (Figure 6c) discriminates electrical noise spikes from signals obtained at a slow leak rate. The signals shown on the PC plots are time domain representations of typical signals captured by a transient recorder. Metals and materials science. It is in this domain that most AE r e s e a r c h a n d development h a s occurred. Monitoring of AE from metals andor fiber-reinforced composites is now common for in-service monitoring, proof testing, welding, continuous monitoring of structures, and fracture mechanics research. Emissions are generated in metals by dislocation motion, grain boundary sliding, crystal twinning, phase transformations, crack initiation, crack growth from fatigue or corrosion, and cracking of second-phase particles. Emissions are generated in fiber - reinforced plastic composites by matrix cracking, matrix splitting, debonding, fiber fracture, fiber pullout, and post-debond friction. The sources of AE can be located and correlated with the event or mechanism that produced them. Early workers had little appreciation of the physics of wave propagation and detection, and some optimistic claims were made that later
resulted in disillusionment. Theoret ical developments since t h e l a t e 1970s have helped remove the mystique that once surrounded the subject. Acoustic emission monitoring has a unique ability to detect and locate AE generated as flaws in metals initiate and grow. In arc and spot welding, cracks in welds have been easily and reliably detected, with excellent correlation between emission activity and weld strength (46).AE is used to continuously “listen” to offshore oil rigs, bridges, nuclear reactors, pressure vessels, and aircraft wings for early warnings of possible defect growth. Harris and co-workers (47) have proposed empirical relations between AE amplitudes and crack growth rates in steels. Pao (48) h a s discussed several important principles of AEs, such as source mechanisms, signal dispersion, and source characterization. In fiber-reinforced plastic composites, AE has been used to monitor tanks and pipes. Many thousands of tank and vessel tests have been run using standard procedures set forth by the Society of the Plastics Industry and the American Society of Mechanical Engineers. Similar standards exist for, reinforced plastic piping; these provide guidelines to determine the structural integrity of pipe, fittings, and joints. Acceptance criteria are based primarily on the emission rate a t constant proof loads (to 110% of maximum pressure). Testing of fiber-reinforced plastic manlift booms on aerial personnel lifting devices (such as those used by electrical utilities to service overhead power lines) has proven to be one of the biggest applications. In general the procedures are similar to those used for tanks, vessels, and pipes, with special attention paid to the joints. As many as 50,000 tests on booms may have been conducted in t h e course of recertifying bucket trucks. In typical tests, a composite material specimen is loaded quasistatically or by cycling, and A E s are recorded. The data are analyzed after the experiments to attempt to distinguish distinct failure modes and track damage progression. I n f r a c t u r e mechanics, where acoustic monitoring is routinely used as a powerful tool, there is a wealth of theoretical knowledge regarding the origins and nature of AE signals. For some simple systems it is possible to predict the form of a n AE sign a l from knowledge of t h e size, shape, position, and orientation of t h e source. For example, phase changes in small spherical inclusions
in isotropic media lead to predictable AEs, provided that the geometry of the test piece is rigorously defined (49).More often we are concerned with the inverse problem, t h a t is, having recorded a n AE we either want to identify or whose source we want to characterize. This problem, too, has been solved for some welldefined systems using deconvolution techniques (50). One recent method takes into account factors such as crack orientation, which causes the deconvolution method to produce erroneous results (51). A measurement system using five transducers mounted in different positions records a n AE event, and a “moment tensor” is derived from the digital records of this signal. Eigenvector analysis enables the tensor to be separated into a mean component, a shear component, and a component known a s the compensated linear vector dipole. It is possible to classify a n AE source as either a tensile crack or a shear crack from the ratios of these three components. Entomology. I n recent y e a r s much attention has been paid to the chemical signals that insects use to communicate with one another. We have not fully explored the forms of sonic communication t h a t many groups of insects use. The chirping of a cricket, the clacking stridulation of a flying grasshopper, and the deafening din of cicadas after a periodic emergence are examples of what may be a n ancient form of signaling among some of the more primitive insect orders. There is a high metabolic cost to this form of signaling as it requires an energy expenditure an order of magnitude greater than the basic metabolic rate (52). Such signaling is not without risk as it might attract conspecifics, predators, or parasites. In addition to the acoustic emissions made by insects signaling each other directly, there is transmission of the song through the host plant (53).There are also the acoustic emissions that insects generate as they feed on their hosts. These various components can be assessed by the judicious choice of broad bandwidth detectors and filters. A good example of this approach is the study by Fujii and co-workers (54)of the patterns of AE associated with termite colonization of wood. Typically, the sensor is placed on the outside of the wood sample that contains a colony, with an appropriate acoustic coupling between sensor and Sam le. As the size of the colony expan s, the AE events multiply. Signals are attenuated by distance, but
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REPORT judicious placement of multiple de tectors can result in the localization of the feeding colony. Bark beetles have long been known to use ultrasound as a means of communicating with one another (55) and are known to favor colonization of distressed trees. Trees suffering from moisture stress during drought or drying out after felling are also a source of AEs (42). It is possible that tree colonizers such as bark beetles can detect and use these AEs. The future The quest for fundamental knowledge and industrial application of AE is gathering momentum. Perhaps fastest progress can best be made by strong interdisciplinary interaction. Aspects of sonochemistry (56), including chemical AE, will become more widely taught in colleges and universities. We can expect to see the development of more rugged and sensitive sensors, and their application to a wider range of problems within the disciplines discussed above and elsewhere. There will be a broader use of multichannel analyzers and high-frequency digitizers capable of greater resolution. The vast data sets obtained by instruments capable of capturing multiple signals, or by simultaneous reading of multiple channels of frequency information, can only be analyzed by computer. Chemometricians (and their equivalents in other disciplines) will develop new automated methods of data analysis, and well-established signal analysis techniques will be borrowed from these other disciplines. There is the need for development of a rugged, sensitive, selective, miniaturized, quantitative chemical acoustic sensor that can be dipped into solutions or used in flowing streams. This acoustic sensor would be used similarly to conventional electrodes and fiber-optic sensing. Real-time use of AE for industrial process control and laboratory monitoring of chemical re actions and other processes will be of special interest.
(2)Lai, E.P.C.; Chan, B. L.; Chen, S. Appl. Spectrosc. 1988,42,526-29. (3)Poursartip, A.; Dorosh, M.; Nadeau, J. S.; Bennett, R. Acoustic Attenuation as a Measure of Damage to GFRP Rods; Reinforced Plastics/Composites Institute: Montreal, Canada, July 1986. (4)Nagy, P. B.; Adler, L. J. AppZ. Phys. 1990,67(8),3876-78. (5)Coufal, H.; McClelland, J. F. J. Mol. StnCt. 1988,173,129-40. (6)Fox, C. G.; Alder, J. F. Analyst 1989, 114,997-1004. (7)Guilbault, G. G.; Jordan, J. M. CRC Crit. Rev. Anal. Chem. 1988,19,1-28. (8) Thompson, M.; Dhaliwal, G. K.; Arthur, C. L.; Calabrese, G. S. IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency Control 1987, UFFC-34, 12735. (9)Betteridge, D.; Joslin, M. T.; Lilley, T. Anal. Chem. 1981,53,1064-73. (10)Forster, F.; Scheil, E. 2. Metallii. 1936, 24,245-47. (11)Kaiser, J. Arch. Eisenhuettenwes. 1953, 24,43-45. (12)Green, A. T.; Lockman, C. S.; Steele, R. K. Mod. P h t . 1963,41(11),137-39. (13)Spiesberger, J. L.; Fristrup, K. M. Am. Nat. 1990,135,107-53. (14)Wentzell, P. D.; Wade, A. P. Anal. Chem. 1989,61,2638-42. (15)Wentzell, P. D.;Vanslyke, S. J.; Bateman, K. P. Anal. Chim. Acta, in press. (16)Lee, 0.; Koga, Y.; Wade, A. P. TaZanta 1990,37(9),861-73. (17)Tyree, M. T.; Dixon, M. A.; Thompson, R. G. Plant Physiol. 1984,74,104649. (18)Tyree, M. T.; Sperry, J. S. Plant Cell Environ. 1989,12,371-82. (19)Wentzell, P. D.; Vanslyke, S. J.; Wade. A. P. Trends Anal. Chem. 1990. 9(1),3-8. (20)Dumont. G. A.: Wade. A. P.: Lee. 0. . PCT Int. Appl. CA 90 00,015,1990.’ (21)Wade, A. P.; Chow, P.Y.T.; Soulsbury, K. A.; Brock, I. H. Anal. Chim. Acta, in press. (22)Crowther, T. G.;Wade, A. P.; Wentzell, P. D.; Gopall, R., unpublished results. (23)Belchamber, R. M.;Betteridge, D.; Collins, M. P.; Marczewski, C. Z.; Wade, A. P. Anal. Chem. 1986,58,1873-77. (24)Sibbald, D.B.; Wentzell, P. D.; Wade, A. P. Trends Anal. Chem. 1989,8(8),28991. (25)Belchamber, R. M.; Betteridge, D.; Chow, Y. T.; Lilley, T.; Cudby, M.E.A. J. Acoustic Emission, 1985,4,71-83. (26)Zgonc, K.; Grabec, I. “A Neural-Like System Applied to Acoustic Emission Analysis”; Intl. Neural Network Conference, (IEEEAnternational Neural Network Society), Paris, July 9-13,1990. (27)Codex Germanicus (ca. A.D. 13501,p. 75 in Read. J. Prelude to Chemistrv: Bell: London, 1939. (28)Van Ooijen, J.A.C.; van Tooren, E.; Reedijk, J..I. Am. Chem. SOC.1978, . 100, . 5569170. (29)Sawada, T.; Gohshi, Y.; Abe, C.; Furaya, K. Anal. Chem. 1985,57,174345. (30)Sawada, T.; Gohshi, Y. Anal. Chem. 1985,57,366-67. (31)Clark, G. M. Thermochim. Acta 1979, 34,365-75. (32)Lanvik, K. Thermochim. Acta 1987, 110,253-64. (33)Lee, 0.; Wentzell, P. D.; Boyd, D. A.; Wade, A. P. Trends Anal. Chem. 1990, 9(7),217-22. (34) Brock, I. H.; Lee, 0.;Soulsbury, _
The authors thank J. C. Madden and the B.C. Advanced Systems Institute for initiating the scientific interaction that led to this paper, and D. Betteridge and A. Poursartip for helpful suggestions.A.P.W. thanks the Institute for Chemical Science and Technology for technical support. R.M.B. thanks the management of British Petroleum PLC for permission to publish this paper.
References (1)Wade, A. P. Chem. Br. 1989,25,36163. 506 A
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K. A.; Sibbald, D. B.; Wentzell, P. D.; Wade, A. P. Presented at the FACSS Conference, Cleveland, OH, October 1990;paper 990. (35)Vanslyke, S.J. B.Sc. Thesis, University of British Columbia, Vancouver, British Columbia, Canada, 1989. (36)Ranke-Madsen, E. Ph.D. Thesis, University of Copenhagen, Denmark, 1957. (37)McLean, F.; Hastings, A. B. J. Biol. Chem. 1934,107,337-50. (38)Milburn, J. A.; Johnson, R.P.C. Plant 1966,69,43-52. (39)Tyree, M. T.;Dixon, M. A. Plant Physiol. 1983,72,1094-99. (40)Ritman, K. T.; Milburn, J. A. J. Exp. Bot. 1988,39,1237-48. (41)Tyree, M. T.; Sperry, J. S. Ann. Rev. Plant Physiol. Molec. Biol. 1989,40, 1938. (42)Haack, R. A.; Blank, R. W.; Fink, F. T.: Mattson. W. J. Fla. Entomol. 1988. 71,427-40. ‘ (43)Honeycutt, R. M.; Skaar, C.; Simpson, W. T. Forest Prod. J. 1985,35,4850.
(44)-Noguchi,M.; Kityama, S.; Satoyoshi, K.; Umetsu, J. Forest Prod. J. 1987,37, 38-34. (45)Belchamber, R. M.; Collins, M. P., unpublished results, 1990. (46)Stephens, R.; Kim, H. I n Acoustic Emission-A Diagnostic Tool in NonDestructive Testing; Szilard, J. R., Ed.; John Wiley: New York, 1982;pp. 45989. (47)Harris, D.; Dunnegan, H.; Tetelman, A. Technical Bulletin DRC 105;Dunnegan Engineering Consultants Inc.: San Juan Capistrano, CA, 1983. (48)Pao, Y. S. Theory of Acoustic Emission; ASME 1978;pp. 107-28. (49) Simmons, J. A.; Wadley, H.N.G. J. Res. Nat. Bur. Stand. (US) 1984,89,5564. (50) Hsu, N.; Simmons, J. A.; Hardy, S. Mater. Eval. 1977,35,100-106. (51)Ohtsu, M. NDTZnt. 1989,22,14. (52)Burk, T.Fla. Entomol. 1988,71,40009. (53)Michelsen, A. F.;Finck, A. F.; Gogala, M.; Traune, D. Behav. Ecol. Sociobiol. 1982,11,269-81. (54)Fujii, Y.; Noguchi, M.; Imamura, Y.; Tokoro, M. Forest Prod. J. 1990,40,3436. (55) Rudinsky, J. A.; Michael, R. R. Science 1975,175,1386-90. (56) Lorimer, J. T.; Mason, P. J. Sonochemistry-Theory, Applications and Uses of Ultrasound in Chemistry; Ellis Horwood Series in Physical Chemistry; Wiley: New York, 1988.
I
Wade obtained his Sc. in Adrian chemistry with computer studies f r o m Southampton University in 1981 and his Ph.D. in analytical chemistry from University College Swansea, University of Wales, in 1985. After postdoctoral work with S. R. Crouch and C. G. Enke a t
Michigan State University, he joined the faculty of the ChemistryDepartment at the Universityof British Columbia (UBC) in 1987. His research interests include flow injection analysis, chemical acoustic emission, chemometrics, and artficial intelligence.
search interests include on-line spectroscopy, process acoustic measurements, and analytical instrumentation.
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David B. Sibbald received his B.Sc. in chemistry, with a minor in computer science, from UBC in 1987 and completed his M.Sc. in chemistry in 1990. His interests include chemical acoustic emission and chemometrics.
I Shabtai Bittman obtained his B.Sc. in biology in 1972 and his M.Sc. in agronomy
in 1975fiom McGill University. He received his Ph.D. in crop science from the University of Saskatchewan in 1985. He was a biologist and research scientist with Agriculture Canada research stations in Nova Scotia and Saskatchewan from 1977 until joining the Agassiz Research Station, B.C., in 1987. His current research concerns are the improvement of yield and nutritional quality of forage crops and reducing the environmental impact offarming practices.
Ron M. Belchamber is leader of the Process Analysis and Automation Team at the British Petroleum (BPI Research Centre, Sunbury-on-Thames, U.K.He obtained his B S c in chemistryfiom Imperial College, London University, in 1975, and his Ph.D. from the University of Alberta in 1981. He was a postdoctoral research associate at University College Swansea until joining BP in 1982. His re-
Mark N. Bailey received both his B. A. Sc. in mechanical engineering (1985) and his M.A. Sc. in metals and materials engineering (1988) fiom UBC. He is currently an engineering consultant in materials f a i l u r e s f o r M a c i n n i s Bigg Associates, Vancouver.
John A. McLean, a professor in the Department of Forest Sciences at UBC, obtained his B.Sc. (1965) and M.Sc. (1968)fiom the University of Auckland, New Zealand, and his Ph.D. from Simon Fraser University, B.C. (1976). His major research interests are the ecology and management of forest insects, especially the semiochemical ecology of ambrosia beetles, and the utilization of semiochemicals in pest management systems to reduce the economic impact of these insects.
Peter D. Wentzell obtained a B.Sc. from Dalhousie University (1982) and his Ph.D. from Michigan State University (1987)- After completing postdoctoral work with A. P. Wade at UBC, he joined the faculty of Dalhousie University as an assistant professor in 1989, where he is a member of the Trace Analytical Research Centre in the Chemistry Department. His current research interests are in the areas of chemometrics (with emphasis on digital filtering and response su?face methodologies), flow injection analysis, and chemical sensors.
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