- Email: [email protected]
The “NRL-SAWRHINO”: a nose for toxic gases
Sensors and Actuators B 65 Ž2000. 10–13 www.elsevier.nlrlocatersensorb
The ‘‘NRL-SAWRHINO’’: a nose for toxic gases R. Andrew McGill a,) , Viet K. Nguyen b, Russell Chung b, Ronald E. Shaffer a , Dan DiLella a , Jennifer L. Stepnowski b, Todd E. Mlsna b, David L. Venezky a , Dawn Dominguez a a b
NaÕal Research Laboratory, Washington, DC 20375-5345 USA Geo-Centers, 1801 RockÕille Pike, RockÕille, MD 20852 USA Accepted 12 October 1998
Abstract At the Naval Research Laboratory ŽNRL., surface acoustic wave ŽSAW. chemical sensor systems have been in development since 1981. The primary focus has been the detection and identification of chemical agents and other toxic gases or vapors. In the recently developed ‘‘NRL-SAWRHINO’’ system ŽRhino, Gr. Nose., a self-contained unit has been developed capable of autonomous field operation. An automated dual gas sampling system is included, for immediate and periodic detection capability. The latter, utilizes a trap-and-purge miniature gas chromatographic column, which serves to collect, concentrate, and separate vapor or gas mixtures prior to SAW analysis. The SAWRHINO includes all the necessary electronic and microprocessor control, SAW sensor temperature control, onboard neural net pattern recognition capability, and visualraudible alarm features for field deployment. The SAWRHINO has been trained to detect and identify a range of nerve and blister agents, and related simulants, and to discriminate against a wide range of interferent vapors and gases. q 2000 Elsevier Science S.A. All rights reserved. Keywords: Toxic gas; Field operation; Surface acoustic wave
1. Introduction The detection and identification of toxic gases and vapors in the field at trace concentrations is of interest to allow the user to identify the nature and location of the source material, take appropriate actions to protect human life, and monitor the decontamination process to a safe level. Real-time or near real-time chemical sensing capability is required for this application, and the sensor system must be able to operate under a variety of environmental conditions, and electrical power constraints, together with limitations on the system dimensions. Surface acoustic wave ŽSAW. chemical sensors are the focus of this work. SAW chemical sensors are SAW devices that have been coated with a thin layer of chemoselective material w1x, e.g., a functionalized polymer. The SAW chemical sensor is conceptually similar to the bulk acoustic wave sensor reported by King w2x in 1964. The SAW chemical sensor is sensitive to mass and physical
) Corresponding author. NRL code 6375, MS&T Division. Tel.: q1202-767-0063; fax: q1-202-767-5301; e-mail: [email protected]
changes in the properties of the coating w3x. The chemoselective polymer is tailored for gasŽes. of interest w4,5x and acts as a sponge to concentrate gas at the surface of the SAW device as shown in Fig. 1. During operation, a wave is propagated across the surface of the SAW device, and when the polymer coating sorbs gaseous molecules, the velocity of the SAW is perturbed w6x. The sorption of a gas is readily detected by monitoring the signal frequency of the device. Arrays of SAW sensors, conceptually shown in Fig. 2, are used to allow the implementation of pattern recognition techniques w6x, to identify gases. Several SAW sensor systems have been developed in the USA and Europe w7x. In this work a SAW sensor system, the SAWRHINO w8x shown in Fig. 3, has been developed for vehicular mounted field applications. The SAWRHINO is an automated chemical agent detector and alarm, designed for autonomous operation with rapid and reversible responses from low to high concentrations of G-nerve and H-mustard agents. The SAWRHINO includes a temperature controlled dual 3-SAW array with an automated dual gas sampling system. One pneumatic pathway is included for immediate detection capability, and another for periodic detection capability at lower concentrations. The latter gas sampling
0925-4005r00r$ - see front matter q 2000 Elsevier Science S.A. All rights reserved. PII: S 0 9 2 5 - 4 0 0 5 Ž 9 9 . 0 0 3 5 2 - 4
R.A. McGill et al.r Sensors and Actuators B 65 (2000) 10–13
11
Fig. 1. A SAW Chemical sensor with a polymer sorbing Sarin nerve agent. Fig. 3. The NRL-SAWRHINO.
system includes a miniature trap-and-purge gas–solid chromatographic ŽTAP-GSC. column. The TAP-GSC column is utilized to collect and concentrate gases of interest over a period of approximately 45 s at the head of the column. Subsequent thermal desorption through the GSC column allows the separation of multiple analytes Žsee Fig. 4.. The eluted analytes are directed to a SAW sensor array for detection. The SAW sensors in each array sample air in a parallel arrangement so that each SAW sensor is exposed to vapor or gas at the same time. Data from each SAW sensor is sampled each second. Each SAW device in an array is coated with a different chemoselective polymer coating. For the application of interest, three polymers G, H, and O were selected for the detection of G-nerve agents, H-mustard agents, and Organic interferents, respectively. Utilizing the dual gas sampling design allows the system to detect G-nerve agents over a wide dynamic concentration range covering 7 orders of magnitude from 200 parts per trillion Žppt. to 400 parts per million Žppm.. Note, a lethal and incapacitation human dose of a G-nerve agent is approximately 25 ppm and 12 ppm, respectively, which lie well within the operating range of the SAWRHINO.
2. Experimental
MHz SAW resonators used are a piezoelectric ST quartz substrate with two sets of aluminum interdigitated electrodes ŽIDE. and a pair of Bragg reflectors deposited on each end of the substrate surface. One IDE is used to launch a wave across the surface of the device and is received by a second IDE, and the Bragg reflectors reflect the wave up and down the device. The SAW devices are epoxy mounted on circular 4-pin die with gold wire bonding, one device per header. In addition, the SAW devices are manufactured with a protective layer of silica Žca. 30 nm thick. deposited over the area to be coated with the chemoselective material w3x. Prior to any chemoselective coating deposition or surface chemical reaction, the silica surfaces of the SAW devices were solvent rinsed, and plasma cleaned as described before w3,8,9x. The SAW devices were cleaned and coated with the polymers as described before w3x, to provide a signal frequency shift of approximately 250 KHz, which corresponds to mean thickness of about 50 nm. The vapor source for the majority of analyte tests was a VG400 ŽMSI. vapor generator. Vapors were generated as previously described w3x, by bubbling dry N2 through thermostatted glass containers maintained at 158C, and diluted from near saturation by a pulsed width modulation technique. The VG400 is an automated vapor generator that allows computer controlled delivery of a vapor stream
ST-cut quartz, 250 MHz, SAW two-port resonator devices ŽMicrosensor Systems, wMSIx, Bowling Green, KY. were used in these studies. The basic elements of the 250
Fig. 2. A 3-SAW sensor array with different polymer coatings showing reversibly gas sorption.
Fig. 4. GSC Elution 3-SAW array response to a complex gas mixture.
12
R.A. McGill et al.r Sensors and Actuators B 65 (2000) 10–13
Fig. 5. Plot of G-SAW signal versus wGDx for TAP-GSC data at low and high RH.
glass dilution chamber with Teflone tubing Ž40 cm, i.d., 0.125 in.., to allow air from the glass chamber to be sampled. Excess air from the glass mixing chamber was vented through an additional outlet. All gas flow rates were measured with a BIOS International air flow meter ŽModel Dry-Cal DC-2M.. The SAWRHINO ŽMSI. was exposed to 20 interferent vaporsrgases, Sarin ŽGB., Soman ŽGD., dimethylmethylphosphonate ŽDMMP., Mustard ŽHD., and bischloroethylether ŽCEE. at a low Ž30%. and high Ž80%. relative humidity levels and trace gas concentrations. The nerve and blister agent tests were performed by an independent surety test facility ŽGEOMET Technologies, Gaithersburg, MD, USA..
3. Results and discussion
Fig. 6. Plot of G-SAW signal versus wGDx for real-time data at low and high RH.
with a known dilution factor and with the capability to switch between diluted vapor stream and clean carrier gas. During SAWRHINO vapor tests, a section of Teflone tubing Ž100 cm, i.d., 0.125Y . leading from the VG400 vapor outlet was used to deliver a vapor stream Ž120 mlrmin. to a glass dilution chamber where the vapor stream was further diluted by mixing with a humidified air stream from a Miller Nelson HCS-310 air flow, temperature and humidity control system Ž10 lrmin.. The two SAWRHINO gas inlets were connected directly to the
The SAWRHINO data collected for Soman nerve agent ŽGD. at low and high humidity is summarized for TAPGSC SAW array data and real-time SAW array data in Figs. 5 and 6, respectively. The SAWRHINO signal responses at low and high humidities were in close agreement. This result was the same whether examining data from the trap-and-purge gas sampling system or the realtime gas sampling system Žsee Figs. 5 and 6.. Using an extrapolation, the detection limits to produce 100 Hz SAW signal for GD were determined at 4 ppb and 200 ppt for real-time and TAP-GSC analysis, respectively. Typical SAW sensor signal noise levels were observed with an rms value of 2 Hz for a 10-s data sampling period. During agent tests involving incremental increases in vapor concentration, the real-time SAW array responded immediately after the stepwise concentration increase. Similar results were observed for mustard ŽHD. vapor tests with higher detection limits of 80 ppb and 2 ppb for real-time and TAP-GSC analysis.
Fig. 7. Principal components plot for TAP-GSC data illustrating the clustering characteristics of nerve and mustard agent for robust interferent rejection capability.
R.A. McGill et al.r Sensors and Actuators B 65 (2000) 10–13
SAWRHINO data for an exposure to a mixture of DMMP, CEE, toluene, and water show that for a complex mixtures, the trap-and-purge GSC is effective at separating multiple components for SAW sensor array analysis Žsee Fig. 4.. From all the vapor or gases tested, all responses were completely reversible and the SAW sensors were unchanged in their performance after an extended series of tests. A probabilistic neural network ŽPNN. was developed and implemented on board the SAWRHINO w10x. 505 TAP-GSC patterns were used to train the system, providing a system performance that was able to distinguish between nerve and blister agents and over 20 different potential Interferents Ž100% correct.. This new PNN algorithm features automated outlier rejection to reduce the number of false alarms occurring in the field, a measure of certainty for each classification decision, and the potential for real-time training. Fig. 7 illustrates why the PNN can distinguish between the chemical agents and the interferent compounds tested. In this principal components plot, the nerve agents ŽGB, GD. and simulant ŽDMMP. patterns cluster tightly in the lower right-hand section, while the mustard agent ŽHD. and mustard simulant ŽCEE. are located in the upper middle space. The remainder of the space is comprised of the interferent vapors. The tight clustering of the agents and agent simulants and the lack of overlap with the background vapors provides justification for the robust performance of the SAWRHINO system.
4. Summary and conclusions A SAW sensor system, the SAWRHINO has been developed and tested against a wide range of vapors and gases including live chemical agents. The 3-SAW array utilized in the TAP-GSC sampling system provides the
13
SAWRHINO with a robust operational performance. For the PNN used, this was able to correctly identify 100% of the agents with no false alarms. No false alarms were reported on subsequent long-term studies in either the laboratory or in field experiments. The 3-SAW array utilized in the real-time mode provided adequate detection capability but was not a large enough array to provide robust identification. From previous work at the NRL a 5-SAW array is preferred for real-time detection.
Acknowledgements Financial support for this work was provided by DARPA with funds administered by Dr. Millie Donlon. Support from the Office of Naval Research is also acknowledged.
References w1x H. Wholtjen, R.E. Dessy, Anal. Chem. 51 Ž1979. 1458–1475. w2x W.H. King, Anal. Chem. 36 Ž1964. 1735–1739. w3x R.A. McGill, J. Grate, M.R. Anderson, in: T.E. Mallouk, J.D. Harrison ŽEds.., Interfacial Design and Chemical Sensing, American Chemical Society, Washington DC, ACS Symposium Series 561, 1994, pp. 280–294. w4x R.A. McGill, M.H. Abraham, J. Grate, CHEMTECH 24 Ž9. Ž1994. 27–37. w5x R.A. McGill, Proc. Society of Plastic Engineers Annual Technical Conf., Indianapolis, IN, USA, May, 1996, 2080–2084. w6x D.S. Ballentine, R.M. White, S.J. Martin, A.J. Ricco, E.T. Zellers, G.C. Fryer, H. Wohltjen, Acoustic Wave Sensors: Theory, Design and Physicochemical Applications, MA, Academic Press, 1996. w7x M. Rapp, B. Bob, A. Voigt, H. Gemmeke, H.J. Ache, Fresenius’ J. Anal. Chem. 352 Ž1995. 699–704. w8x R.A. McGill, Surface Warfare 21 Ž1996. 32–33. w9x J.W. Grate, R.A. McGill, Anal. Chem. 67 Ž1995. 4015–4019. w10x R.E. Shaffer, R.A. McGill, S.L. Rose Phehrson, Analytica Chimica Acta 384 Ž1999. 305–317.
The ‘‘NRL-SAWRHINO’’: a nose for toxic gases R. Andrew McGill a,) , Viet K. Nguyen b, Russell Chung b, Ronald E. Shaffer a , Dan DiLella a , Jennifer L. Stepnowski b, Todd E. Mlsna b, David L. Venezky a , Dawn Dominguez a a b
NaÕal Research Laboratory, Washington, DC 20375-5345 USA Geo-Centers, 1801 RockÕille Pike, RockÕille, MD 20852 USA Accepted 12 October 1998
Abstract At the Naval Research Laboratory ŽNRL., surface acoustic wave ŽSAW. chemical sensor systems have been in development since 1981. The primary focus has been the detection and identification of chemical agents and other toxic gases or vapors. In the recently developed ‘‘NRL-SAWRHINO’’ system ŽRhino, Gr. Nose., a self-contained unit has been developed capable of autonomous field operation. An automated dual gas sampling system is included, for immediate and periodic detection capability. The latter, utilizes a trap-and-purge miniature gas chromatographic column, which serves to collect, concentrate, and separate vapor or gas mixtures prior to SAW analysis. The SAWRHINO includes all the necessary electronic and microprocessor control, SAW sensor temperature control, onboard neural net pattern recognition capability, and visualraudible alarm features for field deployment. The SAWRHINO has been trained to detect and identify a range of nerve and blister agents, and related simulants, and to discriminate against a wide range of interferent vapors and gases. q 2000 Elsevier Science S.A. All rights reserved. Keywords: Toxic gas; Field operation; Surface acoustic wave
1. Introduction The detection and identification of toxic gases and vapors in the field at trace concentrations is of interest to allow the user to identify the nature and location of the source material, take appropriate actions to protect human life, and monitor the decontamination process to a safe level. Real-time or near real-time chemical sensing capability is required for this application, and the sensor system must be able to operate under a variety of environmental conditions, and electrical power constraints, together with limitations on the system dimensions. Surface acoustic wave ŽSAW. chemical sensors are the focus of this work. SAW chemical sensors are SAW devices that have been coated with a thin layer of chemoselective material w1x, e.g., a functionalized polymer. The SAW chemical sensor is conceptually similar to the bulk acoustic wave sensor reported by King w2x in 1964. The SAW chemical sensor is sensitive to mass and physical
) Corresponding author. NRL code 6375, MS&T Division. Tel.: q1202-767-0063; fax: q1-202-767-5301; e-mail: [email protected]
changes in the properties of the coating w3x. The chemoselective polymer is tailored for gasŽes. of interest w4,5x and acts as a sponge to concentrate gas at the surface of the SAW device as shown in Fig. 1. During operation, a wave is propagated across the surface of the SAW device, and when the polymer coating sorbs gaseous molecules, the velocity of the SAW is perturbed w6x. The sorption of a gas is readily detected by monitoring the signal frequency of the device. Arrays of SAW sensors, conceptually shown in Fig. 2, are used to allow the implementation of pattern recognition techniques w6x, to identify gases. Several SAW sensor systems have been developed in the USA and Europe w7x. In this work a SAW sensor system, the SAWRHINO w8x shown in Fig. 3, has been developed for vehicular mounted field applications. The SAWRHINO is an automated chemical agent detector and alarm, designed for autonomous operation with rapid and reversible responses from low to high concentrations of G-nerve and H-mustard agents. The SAWRHINO includes a temperature controlled dual 3-SAW array with an automated dual gas sampling system. One pneumatic pathway is included for immediate detection capability, and another for periodic detection capability at lower concentrations. The latter gas sampling
0925-4005r00r$ - see front matter q 2000 Elsevier Science S.A. All rights reserved. PII: S 0 9 2 5 - 4 0 0 5 Ž 9 9 . 0 0 3 5 2 - 4
R.A. McGill et al.r Sensors and Actuators B 65 (2000) 10–13
11
Fig. 1. A SAW Chemical sensor with a polymer sorbing Sarin nerve agent. Fig. 3. The NRL-SAWRHINO.
system includes a miniature trap-and-purge gas–solid chromatographic ŽTAP-GSC. column. The TAP-GSC column is utilized to collect and concentrate gases of interest over a period of approximately 45 s at the head of the column. Subsequent thermal desorption through the GSC column allows the separation of multiple analytes Žsee Fig. 4.. The eluted analytes are directed to a SAW sensor array for detection. The SAW sensors in each array sample air in a parallel arrangement so that each SAW sensor is exposed to vapor or gas at the same time. Data from each SAW sensor is sampled each second. Each SAW device in an array is coated with a different chemoselective polymer coating. For the application of interest, three polymers G, H, and O were selected for the detection of G-nerve agents, H-mustard agents, and Organic interferents, respectively. Utilizing the dual gas sampling design allows the system to detect G-nerve agents over a wide dynamic concentration range covering 7 orders of magnitude from 200 parts per trillion Žppt. to 400 parts per million Žppm.. Note, a lethal and incapacitation human dose of a G-nerve agent is approximately 25 ppm and 12 ppm, respectively, which lie well within the operating range of the SAWRHINO.
2. Experimental
MHz SAW resonators used are a piezoelectric ST quartz substrate with two sets of aluminum interdigitated electrodes ŽIDE. and a pair of Bragg reflectors deposited on each end of the substrate surface. One IDE is used to launch a wave across the surface of the device and is received by a second IDE, and the Bragg reflectors reflect the wave up and down the device. The SAW devices are epoxy mounted on circular 4-pin die with gold wire bonding, one device per header. In addition, the SAW devices are manufactured with a protective layer of silica Žca. 30 nm thick. deposited over the area to be coated with the chemoselective material w3x. Prior to any chemoselective coating deposition or surface chemical reaction, the silica surfaces of the SAW devices were solvent rinsed, and plasma cleaned as described before w3,8,9x. The SAW devices were cleaned and coated with the polymers as described before w3x, to provide a signal frequency shift of approximately 250 KHz, which corresponds to mean thickness of about 50 nm. The vapor source for the majority of analyte tests was a VG400 ŽMSI. vapor generator. Vapors were generated as previously described w3x, by bubbling dry N2 through thermostatted glass containers maintained at 158C, and diluted from near saturation by a pulsed width modulation technique. The VG400 is an automated vapor generator that allows computer controlled delivery of a vapor stream
ST-cut quartz, 250 MHz, SAW two-port resonator devices ŽMicrosensor Systems, wMSIx, Bowling Green, KY. were used in these studies. The basic elements of the 250
Fig. 2. A 3-SAW sensor array with different polymer coatings showing reversibly gas sorption.
Fig. 4. GSC Elution 3-SAW array response to a complex gas mixture.
12
R.A. McGill et al.r Sensors and Actuators B 65 (2000) 10–13
Fig. 5. Plot of G-SAW signal versus wGDx for TAP-GSC data at low and high RH.
glass dilution chamber with Teflone tubing Ž40 cm, i.d., 0.125 in.., to allow air from the glass chamber to be sampled. Excess air from the glass mixing chamber was vented through an additional outlet. All gas flow rates were measured with a BIOS International air flow meter ŽModel Dry-Cal DC-2M.. The SAWRHINO ŽMSI. was exposed to 20 interferent vaporsrgases, Sarin ŽGB., Soman ŽGD., dimethylmethylphosphonate ŽDMMP., Mustard ŽHD., and bischloroethylether ŽCEE. at a low Ž30%. and high Ž80%. relative humidity levels and trace gas concentrations. The nerve and blister agent tests were performed by an independent surety test facility ŽGEOMET Technologies, Gaithersburg, MD, USA..
3. Results and discussion
Fig. 6. Plot of G-SAW signal versus wGDx for real-time data at low and high RH.
with a known dilution factor and with the capability to switch between diluted vapor stream and clean carrier gas. During SAWRHINO vapor tests, a section of Teflone tubing Ž100 cm, i.d., 0.125Y . leading from the VG400 vapor outlet was used to deliver a vapor stream Ž120 mlrmin. to a glass dilution chamber where the vapor stream was further diluted by mixing with a humidified air stream from a Miller Nelson HCS-310 air flow, temperature and humidity control system Ž10 lrmin.. The two SAWRHINO gas inlets were connected directly to the
The SAWRHINO data collected for Soman nerve agent ŽGD. at low and high humidity is summarized for TAPGSC SAW array data and real-time SAW array data in Figs. 5 and 6, respectively. The SAWRHINO signal responses at low and high humidities were in close agreement. This result was the same whether examining data from the trap-and-purge gas sampling system or the realtime gas sampling system Žsee Figs. 5 and 6.. Using an extrapolation, the detection limits to produce 100 Hz SAW signal for GD were determined at 4 ppb and 200 ppt for real-time and TAP-GSC analysis, respectively. Typical SAW sensor signal noise levels were observed with an rms value of 2 Hz for a 10-s data sampling period. During agent tests involving incremental increases in vapor concentration, the real-time SAW array responded immediately after the stepwise concentration increase. Similar results were observed for mustard ŽHD. vapor tests with higher detection limits of 80 ppb and 2 ppb for real-time and TAP-GSC analysis.
Fig. 7. Principal components plot for TAP-GSC data illustrating the clustering characteristics of nerve and mustard agent for robust interferent rejection capability.
R.A. McGill et al.r Sensors and Actuators B 65 (2000) 10–13
SAWRHINO data for an exposure to a mixture of DMMP, CEE, toluene, and water show that for a complex mixtures, the trap-and-purge GSC is effective at separating multiple components for SAW sensor array analysis Žsee Fig. 4.. From all the vapor or gases tested, all responses were completely reversible and the SAW sensors were unchanged in their performance after an extended series of tests. A probabilistic neural network ŽPNN. was developed and implemented on board the SAWRHINO w10x. 505 TAP-GSC patterns were used to train the system, providing a system performance that was able to distinguish between nerve and blister agents and over 20 different potential Interferents Ž100% correct.. This new PNN algorithm features automated outlier rejection to reduce the number of false alarms occurring in the field, a measure of certainty for each classification decision, and the potential for real-time training. Fig. 7 illustrates why the PNN can distinguish between the chemical agents and the interferent compounds tested. In this principal components plot, the nerve agents ŽGB, GD. and simulant ŽDMMP. patterns cluster tightly in the lower right-hand section, while the mustard agent ŽHD. and mustard simulant ŽCEE. are located in the upper middle space. The remainder of the space is comprised of the interferent vapors. The tight clustering of the agents and agent simulants and the lack of overlap with the background vapors provides justification for the robust performance of the SAWRHINO system.
4. Summary and conclusions A SAW sensor system, the SAWRHINO has been developed and tested against a wide range of vapors and gases including live chemical agents. The 3-SAW array utilized in the TAP-GSC sampling system provides the
13
SAWRHINO with a robust operational performance. For the PNN used, this was able to correctly identify 100% of the agents with no false alarms. No false alarms were reported on subsequent long-term studies in either the laboratory or in field experiments. The 3-SAW array utilized in the real-time mode provided adequate detection capability but was not a large enough array to provide robust identification. From previous work at the NRL a 5-SAW array is preferred for real-time detection.
Acknowledgements Financial support for this work was provided by DARPA with funds administered by Dr. Millie Donlon. Support from the Office of Naval Research is also acknowledged.
References w1x H. Wholtjen, R.E. Dessy, Anal. Chem. 51 Ž1979. 1458–1475. w2x W.H. King, Anal. Chem. 36 Ž1964. 1735–1739. w3x R.A. McGill, J. Grate, M.R. Anderson, in: T.E. Mallouk, J.D. Harrison ŽEds.., Interfacial Design and Chemical Sensing, American Chemical Society, Washington DC, ACS Symposium Series 561, 1994, pp. 280–294. w4x R.A. McGill, M.H. Abraham, J. Grate, CHEMTECH 24 Ž9. Ž1994. 27–37. w5x R.A. McGill, Proc. Society of Plastic Engineers Annual Technical Conf., Indianapolis, IN, USA, May, 1996, 2080–2084. w6x D.S. Ballentine, R.M. White, S.J. Martin, A.J. Ricco, E.T. Zellers, G.C. Fryer, H. Wohltjen, Acoustic Wave Sensors: Theory, Design and Physicochemical Applications, MA, Academic Press, 1996. w7x M. Rapp, B. Bob, A. Voigt, H. Gemmeke, H.J. Ache, Fresenius’ J. Anal. Chem. 352 Ž1995. 699–704. w8x R.A. McGill, Surface Warfare 21 Ž1996. 32–33. w9x J.W. Grate, R.A. McGill, Anal. Chem. 67 Ž1995. 4015–4019. w10x R.E. Shaffer, R.A. McGill, S.L. Rose Phehrson, Analytica Chimica Acta 384 Ž1999. 305–317.