Environ. Sci. Technol. 2001, 35, 3193-3200
Field-Deployable Sniffer for 2,4-Dinitrotoluene Detection KEITH J. ALBERT,† M. L. MYRICK,‡ STEVE B. BROWN,§ DALE L. JAMES,§ FRED P. MILANOVICH,§ AND D A V I D R . W A L T * ,† The Max Tishler Laboratory for Organic Chemistry, Department of Chemistry, Tufts University, Medford, Massachusetts 02155, Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, and Lawrence Livermore National Laboratory, 7000 East Avenue, L-171, Livermore, California 94550
A field-deployable instrument has been developed to detect low-level 2,4-dinitrotoluene (2,4-DNT) vapors. The system is based on previously developed artificial nose technology and employs an array of sensory materials attached to the distal tips of an optical fiber bundle. Both semiselective and nonspecific, cross-reactive sensors were employed. Each sensor within the array responds differentially to vapor exposure so the array’s fluorescence response patterns are unique for each analyte. The instrument is computationally “trained” to discriminate target response patterns from nontarget and background environments. This detection system has been applied to detect 2,4-DNT, an analyte commonly detected on the soil surface above buried 2,4,6-trinitrotoluene (TNT) land mines, in spiked soil and aqueous and ground samples. The system has been characterized and demonstrated the ability to detect 120 ppb 2,4-DNT vapor in blind (unknown) humidified samples during a supervised field test.
Introduction Explosives and explosives-like compounds are extremely important chemical species to detect, especially in such complex environments such as mine fields, military bases, remediation sites, and urban transportation areas [airports (1), trains, subways]. In addition, explosives vapor detection is a key component in forensics investigations such as arson (2) or post-blast residue determinations (3). Techniques for detecting explosives and explosives-like compounds include fluorescence (4-8), surface-enhanced Raman (9), electrochemical (10), nuclear quadrupole resonance (11), energydispersive X-ray diffraction (12), ion mobility spectrometry (13), mass spectrometry (14), and various chromatographic approaches (15-17). Portable instrumentation for field explosives detection has also been developed (18). Detector systems that have the ability to detect low-level explosiveslike vapors may someday complement traditional laboratory analysis methods such as EPA Method 8330 and similar chromatographic methods (19-21). New technology and sensor systems could eventually be employed to monitor * Corresponding author telephone: (617)627-3470; fax: (617)6275773; e-mail:
[email protected]. † Tufts University. ‡ University of South Carolina. § Lawrence Livermore National Laboratory. 10.1021/es010829t CCC: $20.00 Published on Web 06/22/2001
2001 American Chemical Society
airport baggage and packages at postal offices or even be incorporated into conventional metal detectors used for land mine detection (22). Presently, trained dogs are thought to be the most dependable land mine detectors. If a trained dog can detect buried explosives, then it is hoped that an artificial system could be developed with similar sensitivity and selectivity. It is not known exactly what dogs smell at the soil surface to detect buried land mines. Recent studies suggest that, over time, explosives components within a buried land mine partition and migrate through the soil to the soil surface (23). The concentration levels, length of time, and exact chemical components that reach the soil surface are not yet known for all mine types, soil types, or environmental conditions. Much progress, however, has been made recently with 2,4-DNT and variable levels of 1,3-dinitrobenzene (DNB) found to be the most frequently detected chemical species over plastic TNT-based land mines (23). This chemical signature information has been used to develop sensor materials and sniffer technologies for these low-level target compounds (24). For explosives detection, however, the complex backgrounds and the low vapor concentrations make detection an extremely difficult and challenging problem. In this paper, we report on our development, characterization, and use of a field-deployable system to detect 2,4DNT vapor, based on previously developed optical “artificial” nose technology (25), employing an array of cross-reactive optical sensors. A cross-reactive sensor array is a collection of sensory materials that contains nonspecific sensors with enough chemical diversity so that the array differentially responds to different analytes (26). This approach reduces the demand for highly selective sensors and places more emphasis on sensor reproducibility, sensor stability, and the computational analysis software employed to differentiate the sensor-analyte response patterns for subsequent analyte discrimination. In the array design, identification of each analyte is not accomplished from the response of one sensor. Instead, the collection of sensor-analyte responses provides a fingerprint that allows classification and identification of each analyte (26). This cross-reactive array platform has similarities to other array-based systems that have been used to detect and discriminate vapors at higher concentrations (25-36). Our sniffer system employs two types of fluorescencebased vapor sensors. One sensor type is semiselective for nitroaromatic compound (NAC) vapors (4-6) while the other sensor types are designed to be nonspecific and cross-reactive (7, 8, 37). A similar array approach has been employed with electrochemical cross-reactive sensor arrays by incorporating both selective and semiselective electrodes (38) for the detection of specific ions in solution. With our array, fluorescence response patterns are monitored before, during, and after vapor exposure to produce time-dependent vapor response patterns. These response patterns are used to discriminate target vapor patterns from nontarget patterns. Our system has been fully characterized and shows promise for 2,4-DNT vapor detection in complex and uncontrolled environments. We had an opportunity to deploy our system at a practice land mine field, and under a supervised field test, the system was used to detect low-level 2,4-DNT vapors in spiked samples. Figure 1 shows a block diagram for the field-deployable unit, which is described below.
Instrumentation and Experimental Section Materials. Benzene, toluene, p-xylene, and ethanol (Fisher) were HPLC grade or better and used as received. All NACs, VOL. 35, NO. 15, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
3193
FIGURE 1. Block diagram for the field-deployable unit. Nile Red, and remaining solvents (HPLC grade or better) were acquired from Aldrich and used as received. DOW polymer was from Dow Corning. The coffee beans (Jim’s Organic Coffee) were used as purchased. All bead materials were extracted from Phenomenex HPLC columns. The seven fibers (1.52 m, NA 0.22, 300 µm inner core diameter) were purchased and bundled by InnovaQuartz, Inc. (Phoenix, AZ) in a six around one format. Sensor Materials and Optical Fiber Bundle. The array was designed with different combinations of the sensor materials listed in Table 1 so that each of the seven fibers consisted of different sensory materials or material combinations. The semiselective sensing material, which is a pentiptycene-derived fluorescent polymer obtained from the Swager group at MIT (4-6), was solvated in toluene (1 mg/ mL). The pentiptycene backbone provides rigidity and thereby prevents π-stacking and precludes self-quenching. The polymer’s electron-donating groups attract the highly electron-accepting NACs. Upon NAC-polymer interaction, electron flow within the polymer is interrupted and fluorescence is quenched. The nonspecific, cross-reactive Nile Red-based sensors were fabricated as previously published (7). Nile Red undergoes spectral changes (intensity, wavelength) due to changes in its microenvironment, and these properties are well-documented in the literature (39-41). Although the Nile Red-based microsensors respond to many analytes and are not designed to be analyte specific, some of the sensor materials are more sensitive to NACs even in higher volatile organic vapor backgrounds (7, 8). To adhere the microspheres to each fiber’s distal tip, a thin layer of clear polymer was first deposited, and as the polymer layer dried, a slurry of sensor stock (billions of beads) was deposited. To create more diversity in the response patterns, i.e., for more sensor array variability, different polymer layers [such as PAN (42)] and different microsensors were employed. All polymer materials were dipped onto the fiber tip by use of a glass capillary tube as follows (letters correspond to Table 1): (fiber 1) one dip of c and then h added; (fiber 2) one dip of a and then d added; (fiber 3) one dip of a and then e added; (fiber 4) one dip of a and then f added; (fiber 5) one dip of c; (fiber 6) one dip of a and then g added; (fiber 7) one dip of a, then h added, and then one dip of b. The proximal tip of the fiber bundle was fitted to an AMP Simplex black ABS plastic connector with a low fluorescent epoxy and was connected to the optical block. The rest of the ∼1.52-m bundle length was covered with a supporting black vinyl jacket except 0.3 m at the distal end which was enclosed within a 0.3 m long, 1/4 in. diameter stainless steel tube. The fibers within the tube were positioned in a circular pattern, and the tube could slide along the fiber bundle, allowing sensing materials to be attached to the individual fiber tips. 3194
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 35, NO. 15, 2001
Optical Block, Excitation Sources, Battery, and Detector. The system’s 12-V battery (5.9 kg, Newark Electronics) lifetime was 6-8 h at constant use, and its status was monitored with an attached voltmeter. All equipment was mounted and secured to a three-wheeled jogging stroller (Figure 2). The optical block accommodated two spectrally different fluorescent sensory materials (Figure 3). The optical block accommodates two different LED excitation sources, one for each of the two spectrally different sensory materials. A blue LED (λpeak 460 nm) was used to excite the pentiptycenederived fluorescent polymer, and a green LED (λpeak 525 nm) (Nichia) was used to excite the Nile Red-based porous silica microsphere sensors. LED excitation light introduced at the fiber’s proximal end excites the fluorescence-based sensors on the distal tip, and some of the resulting fluorescence light is captured by each fiber and transmitted to the CCD detector. The returning longer wavelength fluorescence light is measured as a change in intensity for the array before, during, and after vapor exposure. Optical filter fabrication and spectral optimization have been published recently (43). An Epoch PXL-211 CCD camera detector (Texas Instruments) was employed to monitor fluorescence changes during the sniff cycle. Sensor Housing and Vapor Sampler. The distal end of the fiber bundle was positioned and secured within the custom-designed vapor-sampling unit (Figure 4). The aluminum-based unit was anodized black and consisted of a 0.5-L ballast tank, pressure gauge, micropumps, and valves. A 1.5 in. × 1.0 in. × 0.6 in. microdiaphragm pump (Virtual Industries, Inc) was employed to evacuate the ballast tank, and a pressure gauge was used to monitor ballast tank pressure. A second identical pump was used to flush external (clean) air through the sampler and out the nozzle of the sampler, i.e., the flushed (clean) air was pulled from above the sampling unit past the sensors and through the sampler’s nozzle in the opposite direction to the sniffed ground air. The vapor sampler’s sniff and flush rate were determined to be approximately 62 and 11.6 mL/s, respectively (see Supporting Information). System Operation Protocol and Overview. Optical sensors, a vapor sampling unit, and a customized computational analysis program were integrated to create a battery-powered field-deployable system for NAC vapor detection. The system’s software-controlled system parameters are described below. For specific information on the software or details regarding computational analysis, see Supporting Information. Sensor fluorescence patterns were monitored before, during, and after vapor exposure with a CCD detector to produce regression patterns for subsequent computational analysis. This process is referred to as the sniff cycle. The array’s response patterns are used to computationally train the system (8, 44) to recognize a “target” response because the patterns are unique for different analytes, even when analyte vapors are similar (Supporting Information). Different response patterns for the sensor array are shown in Figure 5. When unknown samples are sniffed, the software generates a semiquantitative prediction value for the unknown’s response pattern relative to the learned pattern. The custom-designed Labview 5.1 software incorporated a linear mathematical regression analysis employing principal components analysis (PCA) (45-47). The itemized operation protocols of the sensor package are described as follows: (i) The fluorescent sensor array fabricated on the fiber bundle was exposed to a range of solvent vapors to explore the variability of the array’s response patterns. (ii) The variability in which the sensor array operates was reduced using PCA. All measurements of the sensor thereafter were recorded in terms of the scores on these principal components.
TABLE 1. Sensor Materials Employed polymer (p) or semiselective (s) porous silica (s) or cross-reactive name material (c) sensor a b c d
p p p s
s c
e
s
c
f
s
c
g
s
c
h
s
c
material
namea
poly(dimethyl siloxane) dispersion coatingb (DOW) poly(acrylamide-co-N-acroxysuccinimide)c (PAN) pentiptycene-derived fluorescent polymerd (no. 58) 5 µm Selectosil, SCXe surface modification, 110 Å pore size, with adsorbed NRf 5 µm Phenosphere, cyano surface modification, 80 Å pore size, with adsorbed NR 5 µm Develosil, hydroxyl surface modification, 60 Å pore size, with adsorbed NR 3 µm Luna, phenylhexyl surface modification, 100 Å pore size, with adsorbed NR 3 µm Phenosphere, hydroxyl surface modification, 80 Å pore size, with adsorbed NR
shelf-storage time emission peak of sensor material in air (nm) prior to use NAg NA 495 640
13 months 2 weeks 11 months 4 months
620
4 months
650
2 months
602
4 months
640
4 months
a All porous silica microsphere materials were removed from Phenomenex HPLC columns and are named accordingly. b DOW is a clear hydrophobic polymer; protocol, 3% w/w in toluene. c PAN is a clear hydrophilic polymer. Details for polymer fabrication are in ref 42. d A fluorescent pentiptycenederived phenylene ethylene polymer (from MIT); protocol, 1 mg/mL in toluene; refs 4-6. e Strong cation-exchange surface functionality. f NR, the fluorescent and environmentally sensitive dye Nile Red. Details of staining the microspheres with dye are in ref 7. g NA, not applicable.
FIGURE 2. Photographs of the field-deployable unit taken at Fort Leonard Wood, MO. (iii) A sensor sniff cycle was defined in terms of a number of measurements prior to the sniff, a number of measurements during the sniff, and a number of measurements after the sniff. (iv) The sensor was calibrated by making measurements of samples (e.g., soil samples) with known or estimated concentrations of the desired analyte. In our work, we typically tried to measure analytes and nonanalytes under a range of realistic environmental conditions. For example, to calibrate the system for measurement of nitroaromatics on the ground surface, we recorded calibration samples over the range of ground surface types that characterized the site being measured (e.g., open soil, grass, weedy clumps, etc). Sensor scores for all measurements during a single sniff cycle were joined by concatenation, and PCA was used to develop a regression vector relating the sniff cycle results to the analyte concentration. Since the sensors may respond to many compounds or backgrounds, such as humidity, as well
as NAC vapors, it was typically necessary to perform a sequence of calibration measurements in an environment representative of the field prior to operation. This calibration procedure was designed to characterize environmental background variability so that such effects were removed from the samples being analyzed. An additional set of calibration sniff cycles, referred to as validation sniff cycles, were also recorded. The validation sniff cycles simply evaluate the value of the regression and allow the system’s calibration to be checked. An important output of the calibration/validation process is the computational standard error of prediction (SEP). The SEP is a measure of the error associated with the sensor output and serves as a basis for confidence in the result. In operation, results from a measurement were compared against a detection threshold to decide whether the target analyte was present in the sample. The software enabled rapid (∼1 s) analysis once sensor responses were calibrated to known vapors. The detection threshold was determined VOL. 35, NO. 15, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
3195
SEP values were 7/26%. In the test phase, the sensor was applied to sniff all the calibration samples and three additional (blind) sample sets (Figure 7B). Each sniff cycle was 10 s. Twenty-five sniff measurements were recorded during the spiked aqueous samples, and 19 measurements were recorded during spiked soil testing.
Results and Discussion
FIGURE 3. Field-deployable unit’s optical block accommodated the two spectrally different sensor materials. This figure was modified from ref 43 where the details for filter fabrication and spectral optimization can be found. F1, green LED excitation filter; F2, green LED beam splitter; F3, blue LED beam splitter; F4, blue LED excitation filter; F5, emission filter. using the SEP and operator input about acceptable detection and false alarm probabilities. (v) The fluorescent sensor array was used for measuring unknown samples by recording sniff cycles and calculating the scalar product of the sniff cycle measurements and the regression vector found in protocol iv. In measurement, or test, mode (protocol v), the sensor produced a target score that quantified the similarity of the analyte vapor to the sample presented in the calibration phase. This score was presented on a scale where 100% denoted an identical response to that observed for the target during calibration and 0% denoted a response similar to the background samples tested during calibration. The percent values employed to train the system were useful when concentrations of calibration samples were known. When concentrations were unknown, however, the procedure employed was to name any samples containing target as 100% (hit) and any blank as 0%. Blind test samples were qualitatively ranked against known sample calibrations. Ground Sniffs. Four different ground spots were spiked with seven drops of a saturated 2,4-DNT/ethanol solution and labeled as A, B, C, and D. Only spot A was used to train the system for hits (100% target). A few areas were spiked with the same amount of ethanol, and the system was trained to recognize these and other ground areas as containing 0% target. The generated standard error of prediction (SEP) values were 0/7%, and measurement sniff data were acquired (Figure 6). Local air temperature and humidity were 31 °C and 50%. Each sniff cycle was 4 s, which included a 0.8-s vapor exposure. Thirty sniff measurements were recorded. Spiked Aqueous and Soil Samples. During system calibration each spiked sample’s headspace was allowed to reequilibrate for 15 min after being sampled. Each sniff cycle was approximately 10 s, which included a ∼2.5-s vapor exposure. Local air temperature and humidity were 32 °C and 44%. Three aqueous samples (WA-6) were used for positive hits (100% target), and three samples (WA-5) were used as blanks (0% target) for system calibration. Table 2 lists the aqueous samples. Three principal components were used, and the SEP for the calibration/validation data were ∼7/18%. In the supervised testing, the sensor was applied to sniff all the calibration samples and two additional (blind) sets of vials (Figure 7A). For the soil samples, calibration was performed using three blanks (WS-6) and three (hit) samples with 120 ppb 2,4-DNT (WS-5) (Table 3). Three principal components were used for the calibration, and the calculated 3196
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 35, NO. 15, 2001
All results were from tests performed at Fort Leonard Wood, MO, under the supervision of DARPA personnel or their contractors. All spiked sample characteristics (Tables 2 and 3), i.e., concentrations, humidity levels, or types of interfering compounds, were not disclosed until after all tests were finished. The system was calibrated in the field prior to each test set. We did not employ laboratory calibrations because the field calibrations better simulated actual test conditions, i.e., sample environment and unknown vapor matrixes. Spiked Ground Spots. Using a saturated 2,4-DNT in ethanol solution, several ground areas were spiked with 7 drops, and other areas were spiked with the same amount of pure ethanol. Four different hot spots were labeled (A-D) but only spot A was used to calibrate the system for hits (100% target). Many different ground (blank) areas, including the spots spiked with pure ethanol, were trained as containing 0% target. The spots were used to build calibration data so that the system could discriminate between ground areas with and without 2,4-DNT vapor. Different types of ground areas, i.e., grassy, sandy/dry, and moist ground, were sniffed during the calibrations and measurements. Once the system was calibrated on ground areas with and without DNT vapors, measurement sniffs on the same areas were performed. Each measurement sniff was 4 s. Only two target sniffs fell under three times the SEP value of 7%, and both were for spot A (see Experimental for SEP definition). Our system can easily distinguish ground spots with spiked DNT from ground spots that do not have spiked target (Figure 6). A general trend of decreasing prediction values was observed after each spot was measured. The measurements for spot A, however, did not follow this pattern. The prediction values may have been lower due to sampler position over the spot or because of analyte loss. The prediction values are based on pattern response comparison to the regression vector, which is determined from the calibration process involving both sensor calibration and sensor validation (protocol iv above). These sniffs (calibration, validation, and measurement) are all subject to errors due to variability in sampling such as with the headspace vapors of a small vial. The headspace is sampled by mixing with air drawn from the surroundings; although the vapor volume consumed during sampling is greater than the vapor volume of the containers, the mixing process may vary from measurement to measurement. Thus, the errors observed in these measurements are due to both the intrinsic prediction error of the measurement and a sampling error. Our SEP incorporates both. The concentration of target probably affects pattern intensity, and the regression vector uses this pattern to determine the prediction value (see Supporting Information). As with any analytical measurement process, sampling can be a major complication. Because our sensors are extremely small and have fast response times (7), we tried to use only minimal sample volumes. Our goal was to sample the smallest possible volume because oversampling near the surface dilutes the relative target concentration with nonequilibrated air. The system provided exceptional results for the ground (blank) spots because no blanks were determined to contain target and the ground spots spiked with pure ethanol did not cause any confusion for the system. Spiked Aqueous Samples. Prepared aqueous samples (from CRREL, Table 2) were employed to train our system to discriminate between spiked samples with and without
FIGURE 4. Schematic and cross-section of the sensor housing and vapor sampler.
FIGURE 5. Four different intensity images for the seven-fiber array represent the array’s response variability after exposure to different vapors. 2,4-DNT vapor. The samples consisted of variable 2,4-DNT and water vapor levels but the actual sample characteristics were not disclosed at the time of testing. We were informed by DARPA personnel as to which series to use for system calibration. The WA-6 series was trained as containing 100% target, and the WA-5 series was trained as containing 0% target. Once system calibration was completed, measurement sniffs were performed on hits (WA-6), blanks (WA-5), and two more sets of blind (unknown) samples (Figure 7A). Any generated prediction score following a measurement sniff would therefore be relative to the samples employed for the calibration process. From the system’s measurement values, the WA-6 (200 ppb DNT) samples produced the highest prediction values for target “closeness”. The high prediction values were expected for this series because the system was
calibrated to recognize the WA-6 series as containing 100% target. A score of less than 100% for the measurements may indicate lack of complete headspace recovery in the sample vial or a possible variation in the array’s response pattern as compared to the calibration profiles (as discussed above). The prediction values for the blind WA-7 (blank + interferent) series were similar to the WA-5 (blank) series. The interferent compound (acetone) in the WA-7 series did not affect the system’s performance even though acetone was not present during system calibration. Although acetone was an interferent in these samples and can be readily detected by identical, cross-reactive microsensors (35), it did not cause output error for detecting the low-level DNT vapor. The system was still capable of detecting 200 ppb regardless of high water vapor levels. VOL. 35, NO. 15, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
3197
FIGURE 6. Results for sniffed ground samples spiked with a saturated 2,4-DNT in ethanol solution show that only areas contaminated with 2,4-DNT produced high prediction values.
TABLE 2. Spiked (Blind) Aqueous Samplesa,b sample name
contents
aq concn of target (mg/L)
expected headspace (ppb)
WA-4 WA-5 WA-6 WA-7
DNT blank DNT blank + interferentc
16.2 0 81.2 0
40 0 200 0 + interferent
a All samples prepared off-site by U.S. Army Cold Regions and Engineering Laboratory. b All samples were blind: sample contents and/or target concentrations were not known at the time of testing. c The interferent was acetone; 50 µL of acetone was added to the 100mL aqueous sample.
The prediction values for the blind aqueous series WA-4 (40 ppb DNT) were slightly higher than those for the blank series but were only 1× higher than the SEP. These results indicate that the detection limit for the system, as configured in this test, was between 40 and 200 ppb DNT. A narrower detection limit range may have been possible if there had been a wider range of spiked samples available at the time of testing. The system was capable of detecting and discriminating the low-level DNT vapor even in the presence of high water vapor content and an unknown interferent. Spiked Soil Samples. For the soil samples (Table 3), our system was calibrated in a fashion similar to the spiked aqueous samples. For system calibration, we were informed to use the WS-5 series as 100% target and the WS-6 series as 0% target. Following calibration, the system was employed to measure the WS-5 (120 ppb DNT) series, the WS-6 (blank) series, and a series of blind samples (Figure 7B). As with the prediction values for the spiked aqueous samples, the WS-5 (120 ppb DNT) series produced the largest response because the system was trained to recognize these samples as being 100% target. Again, a prediction score of less than 100% may indicate that there was a lack of complete headspace recovery or there may have been some sensor response variation. The prediction values for the blind WS-8 (50 ppb DNT) series showed only marginal discrimination from blank samples, but these values were below the SEP value for the validation data. The prediction values for the WS-8 series were higher than most of the blind WS-1 (10 ppb DNT) series and the WS-6 (blank) series. As with the aqueous samples, the interferent in the WS-7 soil samples (diesel fuel) did not confuse the system even though diesel fuel was not present during system calibration. These results were consistent with the sensitivity exhibited in the aqueous sample test, and the detection limit for the soil samples was between 50 and 120 ppb DNT. Although we do not know whether the diesel fuel concentration was at a detectable level for this array, a recent 3198
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 35, NO. 15, 2001
FIGURE 7. Prediction values for test sniffs of spiked 2,4-DNT in (A) aqueous and (B) soil samples. The prediction values show that the highest 2,4-DNT vapor levels were easily detected in both sample types regardless of humidity levels or interfering compounds. The interferent in the aqueous sample was acetone, and the interferent in the soil sample was diesel fuel.
TABLE 3. Spiked (Blind) Soil Samplesa,b sample name WS-1 WS-5 WS-6 WS-7 WS-8
desired soil aq concn amt of DNT moisture of target solution content NAC (mg/L) added (mL) 50 50 50 50 50
DNT DNT DNT DNT DNT
7.9 94.8 0 0 39.5
25 25 25 25 25
expected headspace (ppb) 10 120 0 0 + interferentc 50
a All samples prepared off-site by U.S. Army Cold Regions and Engineering Laboratory. b All samples were blind: sample contents and/or target concentrations were not known at the time of testing. c The interferent was diesel fuel; 2 µL of diesel fuel was added to the 50-g soil sample.
study has shown that similar cross-reactive microsensors could discriminate between low-level benzene, toluene, and three different NACs, even in binary mixtures (8). From some of the acquired data, the first time point of vapor exposure during a sniff cycle, i.e., the first data point corresponding to air intake, consistently displayed more variability than the other time points. This anomalous variability may reflect a systematic effect, which if corrected, might substantially reduce the “noise level” of the generated sensor response patterns. Some of this variability may include valve timing during initial sample intake, variable ballast tank pressure, or strong initial turbulence in the airflow after the valve is opened. Tests performed against prepared aqueous and soil samples indicated that the sensor threshold sensitivity to
2,4-DNT is approximately at the 50-80 ppb level, which may be 1 or 2 orders of magnitude higher than the level believed to be required for successful mine detection (23). Also, the results for the spiked aqueous and soil samples were obtained with a 10-s sniff cycle time, which is much too long for a practical land mine sniffer. Regardless of present detection limits, our signal processing plays a critical role in the system, so the performance described in this paper may not necessarily reflect the true potential of the sensory materials for NAC detection (7, 8, 35). The principal component calibration algorithm employed in our analysis software was a first attempt and may be suboptimal in some respects. Improved algorithms are likely to result in substantial performance gains even with the existing hardware setup. The sensor configuration is also not optimal because the microsphere sensors are adhered to the distal tip of an optical fiber via a clear polymer. The total surface area of the microsensors may not be exposed to vapor, and this limitation increases background fluorescence and decreases analytedye interactions. Ideally, the microsensors would each be individually addressed by employing high-density arrays with optical imaging fibers (8, 35, 37, 48, 49) or conventional glass coverslips (7, 50). With these array configurations, we have shown that 23 ppb 2,4-DNT can be detected in clean, dry air (7) and that low-level NAC vapors can be detected in variable higher level organic vapor background (8, 50). We have demonstrated and described the performance of a field-deployable vapor detector employing an optically based sensor array for detecting and discriminating 2,4-DNT in spiked samples during a supervised field test. During field testing, we were instructed to “make a call” after each sniff measurement to simulate real-world explosives detection. Our predictions fell into two classes: hit and no hit. In a real world scenario, a third class “not sure” would be desirable. Categorization in this class would require further tests to be run on that sample, thereby improving measurement confidence. The instrument is computationally “trained” to recognize target response patterns from nontarget and background environments, just as a dog may be trained to recognize hidden explosives or drugs. Our system may some day complement current systems for explosives-like vapors detection in airports, remediation sites, or mine fields. Although it may easily be modified for other sensing applications, the system was designed to detect 2,4-DNT, which is often found on the soil surface above buried TNTbased land mines. If the system is to be implemented for mine detection, however, size and weight must be decreased, the sampling unit must be improved to accommodate smaller volumes, the sensing materials must be improved in sensitivity and discriminating power, and better computational methods must be employed.
Acknowledgments The authors gratefully acknowledge generous support from the Defense Advanced Research Projects Agency (DARPA). We would also like to thank Professor Timothy Swager and Dr. Claus Lugmair (MIT) for supplying the polymer sensing material, Jeevananda Karunamuni and Katie Stitzer (USC) for work with the optical filter fabrication, Thomas Jenkins and his team at the U.S. Army Cold Regions and Engineering Laboratory for the spiked samples, Vivian George (Walcoff) for meteorological data, Brian Bodkter (LLNL) for electronics work, Professor Gary Settles (Penn State Gas Dynamics Lab) for advice on sniffer design, and DARPA and their contractors for the opportunity to test in a simulated land mine field at Fort Leonard Wood, MO.
Supporting Information Available Experimental details for flush and sniff rates, system software descriptions, a figure showing discrimination for similar
vapors, and a figure with response patterns for a subset of the spiked ground sniffs from Figure 6. This material is available free of charge via the Internet at http://pubs.acs.org.
Literature Cited (1) Fainberg, A. Science 1992, 255, 1531-1537. (2) Barshick, S.-A. J. Forensic Sci. 1998, 43, 284-293. (3) Smith, K. D.; McCord, B. R.; MacCrehan, W. A.; Mount, K.; Rowe, W. F. J. Forensic Sci. 1999, 44, 789-794. (4) Yang, J. S.; Swager, T. M. J. Am. Chem. Soc. 1998, 120, 53215322. (5) Yang, J. S.; Swager, T. M. J. Am. Chem. Soc. 1998, 120, 1186411873. (6) Swager, T. M. Acc. Chem. Res. 1998, 31, 201-207. (7) Albert, K. J.; Walt, D. R. Anal. Chem. 2000, 72, 1947-1955. (8) Bakken, G. A.; Kauffman, G. W.; Jurs, P. C.; Albert, K. J.; Stitzel, S. E. Sens. Actuators B, in press. (9) Sylvia, J. M.; Janni, J. A.; Klein, J. D.; Spencer, K. M. Anal. Chem. 2000, 72, 5834-5840. (10) Buttner, W. J.; Findlay, M.; Vickers, W.; Davis, W. M.; Cespedes, E. R.; Cooper, S.; Adams, J. W. Anal. Chim. Acta 1997, 341, 6371. (11) Anferov, V. P.; Mozjoukhine, G. V.; Fisher, R. Rev. Sci. Instrum. 2000, 71, 1656-1659. (12) Luggar, R. D.; Farquharson, M. J.; Horrocks, J. A.; Lacey, R. J. X-ray Spectrom. 1998, 27, 87-94. (13) Asbury, G. R.; Klasmeier, J.; Hill, H. H., Jr. Talanta 2000, 50, 1291-1298. (14) Hakansson, K.; Coorey, R. V.; Zubarev, R. A.; Talrose, V. L.; Hakansson, P. J. Mass Spectrom. 2000, 35, 337-346. (15) Bailey, C. G.; Yan, C. Anal. Chem. 1998, 70, 3275-3279. (16) Bouvier, E. S. P.; Oehrle, S. A. LC-GC 1995, 13, 120-130. (17) Gates, P. M.; Furlong, E. T.; Dorsey, T. F.; Burkhardt, M. R. Trends Anal. Chem. 1996, 15, 319-325. (18) Williams, D.; Pappas, G. Field Anal. Chem. Technol. 1998, 2, 299-308. (19) Jenkins, T. F.; Miyares, P. H.; Myers, K.; McCormick, E. F.; Strong, A. B. Anal. Chim. Acta 1994, 289, 69-78. (20) U.S. EPA Second Update SW846 Method 8330; U. S. EPA: Washington, DC, 1995. (21) Nam, S.-I.; Leggett, D. C.; Jenkins, T. F.; Stutz, M. H. Am. Lab. (Shelton, Conn.) 2000, 32, AEL4, 6-7. (22) Rouhi, A. M. Chem. Eng. News 1997, 75, 14-22. (23) George, V.; Jenkins, T. F.; Leggett, D. C.; Cragin, J. H.; Phelan, J.; Oxley, J.; Pennington, J. In Proceedings of SPIE-International Optics Engineering, Part 1, Detection and Remediation Technologies for Mines and Minelike Targets IV; Dubey, A. C.; Harvey, J. F.; Broach, J. T.; Dugan, R. E., Eds.; No. 3710; SPIE: 1999, pp 258-269. (24) Dubey, A. C., Harvey, J. F., Broach, J. T., Dugan, R. E., Eds. Proceedings of SPIE-International Optics Engineering, Part 1, Detection and Remediation Technologies for Mines and Minelike Targets IV; No. 3710; SPIE: 1999. (25) Dickinson, T. A.; White, J.; Kauer, J. S.; Walt, D. R. Nature 1996, 382, 697-700. (26) Albert, K. J.; Lewis, N. S.; Schauer, C. L.; Sotzing, G. A.; Stitzel, S. E.; Vaid, T. P.; Walt, D. R. Chem. Rev. 2000, 100, 2595-2626. (27) Eklov, T.; Lundstrom, I. Anal. Chem. 1999, 71, 3544-3550. (28) Lonergan, M. C.; Severin, E. J.; Doleman, B. J.; Beaber, S. A.; Grubbs, R. H.; Lewis, N. S. Chem. Mater. 1996, 8, 2298-2312. (29) Grate, J. W.; Rose-Pehrsson, S. L.; Venezky, D. L.; Klusty, M.; Wohltjen, H. Anal. Chem. 1993, 65, 1868-1881. (30) Gardner, J. W.; Bartlett, P. N. Electronic Noses: Principles and Applications; Oxford University Press: Oxford, UK, 1999. (31) Carey, W. P.; Beebe, K. R.; Kowalski, B. R. Anal. Chem. 1987, 57, 1529-1534. (32) Dickert, F. L.; Hayden, O.; Zenkel, M. E. Anal. Chem. 1999, 71, 1338-1341. (33) Stetter, J. R.; Jurs, P. C.; Rose, S. L. Anal. Chem. 1986, 58, 860866. (34) Persaud, K. C.; Dodd, G. H. Nature 1982, 299, 352-355. (35) Albert, K. J.; Gill, D. S.; Pearce, T. C.; Walt, D. R. Anal. Chem. 2001, 73, 2501-2508. (36) Jurs, P. C.; Bakken, G. A.; McClelland, H. E. Chem. Rev. 2000, 100, 2649-2678. (37) Dickinson, T. A.; Michael, K. M.; Kauer, J. S.; Walt, D. R. Anal. Chem. 1999, 71, 2192-2198. (38) Otto, M.; Thomas, J. D. R. Anal. Chem. 1985, 57, 2647-2651. (39) Meinershagen, J. L.; Bein, T. J. Am. Chem. Soc. 1999, 121, 448449. (40) Vauthey, E. Chem. Phys. Lett. 1993, 216, 530-536. VOL. 35, NO. 15, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
3199
(41) Deye, J. F.; Berger, T. A. Anal. Chem. 1990, 62, 615-622. (42) Pollak, A.; Blumfield, H.; Wax, M.; Baughn, R. L.; Whitesides, G. M. J. Am. Chem. Soc. 1980, 102, 6324-6336. (43) Karunamuni, J.; Stitzer, K. E.; Albert, K. J.; Walt, D. R.; Brown, S. B.; Myrick, M. L. Opt. Eng., in press. (44) Johnson, S. R.; Sutter, J. M.; Engelhardt, H. L.; Jurs, P. C. Anal. Chem. 1997, 69, 4641-4648. (45) Martens, H.; Naes, T. Multivariate Calibration; John Wiley & Sons: New York, 1989. (46) Thomas, E. V.; Haaland, D. M. Anal. Chem. 1990, 62, 1091-1099. (47) Ruyken, M. M. A.; Visser, J. A.; Smilde, A. K. Anal. Chem. 1995, 67, 2170-2179.
3200
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 35, NO. 15, 2001
(48) Steemers, F. J.; Ferguson, J. A.; Walt, D. R. Nat. Biotechnol. 2000, 18, 91-94. (49) Michael, K. L.; Taylor, L. C.; Shultz, S. L.; Walt, D. R. Anal. Chem. 1998, 70, 1242-1248. (50) Stitzel, S. E.; Cowen, L.; Albert, K. J.; Walt, D. R. Manuscript submitted.
Received for review April 6, 2001. Revised manuscript received April 30, 2001. Accepted May 1, 2001. ES010829T
