- Email: [email protected]
Selective thermally cycled gas sensing using fast Fourier-transform techniques
Sensors and Actuators B, 2 ( 1990) 283-289
283
Selective Thermally Cycled Gas Sensing Using Fast Fourier-transform Techniques W. M. SEARS* and KONRAD COLBOW Physics Department, Simon Fraser University, Budy,
BC, VSA IS6 (Cam&)
RICK SLAMKA and FRANC0 CONSADORI Halitec Industries, 2724 SE Marine Drive, Vrmcouver, BC, V5S WI (Cam&z) (Received November 8, 1989; accepted in revised form June 8, 1990)
Abstract The conductance versus time signatures of three types of thermally cycled gas sensors have been measured for a number of different reducing gases over a wide range of concentrations. A fast Fourier-transform data analysis technique is used to produce first-harmonic polar plots of phase angle versus magnitude of both single gases and mixtures as a function of concentration. It is found that this produces a good separation among many of the gases tested and leads to a clear selective criterion for the detection of single unknown gases. Gas mixtures can sometimes be distinguished as well, but with less reliability.
1. Introduction
Thermal cycling of semiconductor gas sensors has been examined over the past few years because of its promise as a technique for selective detection of different reducing gases [l-9]. Thermal-cycling techniques take advantage of the fact that different classes of reducing gases have different reaction rates. For example, carbon monoxide and hydrogen sulfide will oxidize on the sensor at relatively low temperatures (starting near room temperature), alcohols and ketones at intermediate temperatures (200 “C and above) and alkanes (propane and methane in particular) at high temperatures (above about 400 “C) [l-4, lo]. Thus sensors run at different temperatures will show a degree of selectivity. It is in fact better than the temperatures given would indicate, as at high temperatures the more easily oxidized gases are strongly consumed at the sensor surface, and therefore decrease the conductance change as measured in the sensor interior. By cycling the temperature, one detects the whole range of gas reactions in different regions
‘Present address: Physics Department, Lakehead University, 955 Oliver Road, Thunder Bay, Ont., P7B 5E1, Canada. 09254005/90/$3.50
of the heating or cooling cycle, and also avoids the surface contamination that occurs after too long exposure at low temperature. The conductance signatures seen for temperature cycling in the presence of different gases have been the subject of recent patents [ 11- 151. The conductance signatures seen for gases are due to two effects. One reflects the temperature dependence of the gaseous reaction rates just mentioned, the other involves the conductance changes induced by the high-temperature reactions of chemisorbed gases with surface oxygen. The gases are adsorbed in the cool parts of the thermal cycle. A number of pattern-recognition techniques [16-211 have been applied to sensor arrays as a means to improve selectivity. In such methods the outputs of N sensing elements are plotted in an N-dimensional space. If the sensor outputs are a linear function of concentration and if the principle of superposition holds, the various gases will be clearly separated [ 171. In general, one cannot expect these conditions to hold. Pattem-recognition techniques can still be used, however, and it is common to map the N-dimensional space onto two dimensions representing the principal components (factor analysis). Various gases are then expected to cluster in different regions with various degrees of success [ 17,19,20]. We have attempted to obtain the same type of information from one sensor. In the past we have studied various ways of deriving the maximum amount of information possible from the thermalcycle signatures including open- and restrictedflow mounting [7,8]. In what follows we present a further improvement, using a fast Fourier-transform technique, and further address the perennial problem of gas mixtures. 2. Experimental Thermal-cycling measurements were performed using three types of Taguchi gas sensors, manufactured by Figaro Engineering Inc. [22]. The active ingredient of these sensors is sintered tin 0 Elsevier Sequoia/Printed
in The Netherlands
284
oxide powder mixed with palladium and a binder. In the TGS # 812 and TGS # 822 this material is baked onto the outside of a small ceramic tube which is covered by an interdigitated gold pattern [23]. The gold allows the electrical conductance of the tin oxide to be measured. Inside the tube is a coiled metal wire which is used to heat the sensor. These two sensors are used to detect general combustible gases; the TGS # 822 is the replacement for the TGS # 812, which is no longer manufactured. The response to various gases is very similar but not identical for these two sensors, as we will see. The TGS # 109 is constructed in a different manner. In this case two heating elements are embedded at opposite ends of a small rectangular box-shaped pellet of the sintered tin oxide/binder mixture. In operation one element is connected to an appropriate current supply to heat the sensor and also serves as ground for the conductance measurement. The other element is biased above ground so as to allow a measurement to be made of the conductance between them. Figaro has designed this sensor to operate from an a.c. transformer with a heater bias of 1 V and a signal bias of 100 V. However, conductance measurements can be made with lower voltages. This sensor was designed for alkanes or general fuel gas detection. It is also used by Figaro as part of their thermally cycled TGS # 203, which is used for the selective detection of carbon monoxide. The sensor temperature is determined by the size of the voltage applied across the heater wire. In this study we have cycled the TGS # 812 between 0 and 6.2 V, the TGS # 822 between 0 and 5 V, and the TGS # 109 between 0 and 1 V, in a thermal cycle of 32 s on and 32 s off for the last two. The cycle for the TGS # 812 was 25 s on and 25 s off. These voltages were chosen to give a similar temperature for the three sensors, which is estimated as 400 “C from earlier work [6, 16, 231; The sensor surface temperature is affected not only by the heater voltage but also by Joule heating caused by the current used to measure the conductance, and by the exothermic nature of the inflammable gases the sensor is detecting. This makes knowledge of the exact surface temperature under all conditions that much more difficult to obtain. The TGS # 109 is particularly susceptible to this effect. In our experiments the heaters of the TGS # 8 12 and TGS # 822 were driven directly by a Hewlett-Packard function generator (model 3310B), but the TGS # 109 required a higher current and in this case a Darlington pair power amplifier was placed between the output of the
function generator and the gas sensor heating element. The changes in conductance of the sensors were measured at an applied bias of 5 V across the tin oxide. Digital electrometers were used to measure simultaneously the voltage and current and this allowed the real-time measurement of the conductance changes to be done using a computer data-acquisition system. The sensor was tested inside a 100 1 box where gases and vapors to be tested were directly injected. Laboratory air was used as the clean air baseline, which was sufficient for the concentrations used, as seen in Figs. 3, 4, 6 and 7 where ‘zero’ concentration values are plotted.
3. Data Reduction In analyzing the response of the three sensors used in this study, a series of gas concentrations was injected into the test chamber. The concentrations were spread along a logarithmic scale from zero to about 2000 ppm for gases such as propane, carbon monoxide, acetone, ethanol, isopropanol and hydrogen. For each injection the sensor was allowed to come to a steady-state thermal cycle, usually within two or three cycles. Starting when the heater switches on, conductance versus time data were stored in a computer file for one complete cycle. For measurements on the TGS # 822 and the TGS # 109, 64 data points were taken, one per second as shown in Fig. 1. For the TGS # 812, 256 data points were taken over a 50 s period. The conductance response is ‘steady state’; it will repeat every cycle as long as the concentration does not change. We see in Fig. 1 that different gases give characteristic shapes under thermal cycling, and it was assumed that a Fourier-transform technique would help in separating the response to one type of gas from another.
G
I I\
I
Fig. I. Typical thermal-cycling signatures of a number of gases for the TGS # 822. For the first 32 s, the heater is on (5 V). 500 ppm of (A) propane, (B) carbon monoxide, (C) ethanol and (D) hydrogen.
285
A discrete Fourier transform is defined by N-l
Y,=(l/N)
1
&IV-t
(1)
m=O
where k = 0 to N - 1 and W =
exp( - 2n j/N)
(2)
where j equals the square root of minus one. X,,, represents values taken from the conductance versus time curves and Yk represents the transform in the frequency domain; in this paper we are primarily concerned with the zeroth (k = 0) and the first (k = I) harmonic of the transform. We have normalized the transform by the total number of points (N). This is non-standard, ,but was done so that the result would be independent of the size of the tranform, except for resolution effects. The transform was calculated using the Cooley -Tukey fast Fourier-trahsform (FFT) technique under the conditions that the values (X,,,) are equally spaced in time and number in total a power of two [24]. This algorithm gives data expressed as the real and imaginary components of Y,. For convenience of display and analthis into polar ysis, we have recalculated coordinates, magnitude and phase angle. The zeroth harmonic is plotted as the magnitude versus the concentration of the gas tested (see Fig. 2) because the phase angle is always zero. The first harmonic is plotted as the phase angle versus magnitude for various gases and concentrations (see for example Figure 3). The phase angle is only defined within 360” and has been shifted in the Figures for convenience of display. 4. Results Figure 1 shows typical thermal cycling conductance curves for a number of gases at a concentra-
tion of 500 ppm for the TGS # 822. The features of the four classes of reducing vapors shown here have been studied in detail in previous papers [6,7] and we will only summarize them here. Propane, and alkanes in general, shows a flat response as it only responds at a high sensor temperature. Carbon monoxide, at the other extreme, responds at low temperature and gives peaks early and late in the cycle. Ethanol (alcohols and ketones in general) and hydrogen represent an intermediate position, with slight differences occurring in the position of the initial peak and the size of the recovery peak when the sensor is turned off. These features are a function of concentration as well as of the type of gas, and show up most plainly in the Fourier transform. The curves for the TGS # 8 12 and the TGS # 109 are slightly different; this will be discussed when the transform results are presented. The zeroth harmonic of the transformed data of Fig. 1 and that of other gas concentrations is shown in Fig. 2. This harmonic is a direct average of the 64 data points and therefore should follow the power-law dependence usually seen in tin oxide sensors: G =G,+&C”
(3)
Go is the conductance with no reducing gas present, &, is a constant and C is the reducing gas concentration. Figure 2 shows that this happens only to an approximate degree. Nevertheless, in a practical device based on FFT’ analysis the sensor could be calibrated for concentration using this harmonic. Figure 2 shows the relative sensitivity of the device to three gases as well as the effect of relative humidity. We see that the effect of humidity is much larger at low concentrations, except for propane. Figure 3 displays the first harmonic for several gases; hydrogen will be discussed later. The con--_
-2,
I
1
,
I
Fig. 2. Zeroth-harmonic FFT vs. concentration for the TGS+ 822. Propane at 50% (open circles) and 95% (Elled circles) relative humidity; carbon monoxide at 50% (open triangles) and 95% (f&i triangles); ethanol at 50% (open squares) and 95% (@led squares). The propane data have been scaled up by a factor of ten for clarity.
Fig. 3. First-harmonic FFf polar plot for the TGS # 822 at 50% relative humidity. The gases arc propane (6lled circles), carbon monoxide (open circles), acetone (open triangks), ethanol (open squares) and isopropanol (open diamonds). The concentrations for carbon monoxide are 0, 5, 10, 20, 40, 80, 100,200,400 and 500 ppm. For all other gases they are 0, 5, 10,20, 50, 100,200,500, loo0 and 2000 ppm.
286
centrations increase along the displayed data from left to right. That is, increased concentration leads to an increased magnitude as for the zeroth harmonic, but less reliably. The first harmonic is best used to enhance the sensor selectivity. Second and higher harmonics of the transform can also be displayed; however, we find that the information obtained is very similar to that of the first harmonic but less reproducible and thus less selective. In Fig. 3 we see a number of effects. First, the phase angle can be taken as a measure of curve shape or gas signature and the shift in phase angle as a measure of the degree to which this signature changes with concentration. All gases start from an approximately constant point representing zero concentration and only separate when the concentration is sufficient to develop unique signatures. At high concentrations the phase shift stabilizes for all gases shown, except for carbon monoxide where the signature continues to change with concentration. Another feature of Fig. 3, one of paramount interest, is the separation in ‘phase space’ of the three types of reducing gases. Mathematical algorithms can easily be developed to delineate these regions. Figure 4 shows the same type of data for gases at a higher relative humidity (95%). The general features are similar, but there are nonlinear shifts in magnitude and angle for the gases displayed. The major shift is nonlinear in magnitude, with the low concentrations shifted much more than the higher ones. Figure 5 examines the effect of mixtures on the first-harmonic response. The six sets of mixture data converge onto the solid line representing the pure ethanol response (20, 100 and 500 ppm) as seen in Fig. 3. The sensor element is a different TGS # 822 from the one used so far and all values are shifted 15” downward in phase angle. This shift may be due to a slight difference in operating temperature, as such an effect does occur. As propane or CO is added to ethanol, the
Fig. 4. First-harmonic FFT polar plot for the TGS + 822 at 95% relative humidity; otherwise as in Fig. 3.
Fig. 5. Effect of gas mixtures on the first-harmonic response at 50% relative humidity for a second TGS # 822. 20, 50, 100, 200, 500, 1000 and 2OOOppmpropane in 20 ppm ethanol (filled circles); 50, 100,200, 500, 1000 and 2WOppm propane in 1OOppm ethanol (filled triangles); 200, 500, 1000 and 2000 pii propane in 500 ppm ethanol @lied squares); 10,40, 100, 200. 400 and 5OOm_nncarbon monoxide in 2Oppm ethanol (open circles); lb: 40, 100, 200, 400 and 5OOppm carbon monoxide in 1OOppm ethanol (open triangles); 40, 200,400 and 5OOppm carbon monoxide in 5OOppm ethanol (open squares). The solid line represents the pure ethanol response.
phase shifts gradually towards the proper position for the pure gas (down for propane or up for CO). To a certain extent mixtures represent a unique point in phase space as long as only two gases are considered. Note, however, that mixtures of propane and CO will show phase shifts in the region of ethanol response. Figure 6 is the first-harmonic transform for the TGS # 8 12 and the pattern is similar to that of Fig. 3, with a slightly greater spread in phase angle. The major point of interest in this Figure is the response to hydrogen gas, which spirals from one section to another with increasing concentration. This makes hydrogen difficult to identify with this method for both the TGS # 812 and the TGS # 822, which shows the same effect.
Fig. 6. First-harmonic FFT polar plot for the TGS + 812 at 40% relative humidity. The gases are propane (open circles), carbon monoxide (open triangb), acetone (open squares), ethanol (open diamonds) and hydrogen (Bled diamonds). The concentrations are 0, 3, 10,40, 80, 150, 500, 1000, 3000 and 5OOOppmfor all gases except for the tit two, which lack a 3 ppm point.
287
380 -
Fig. 7. First-harmonic FFT polar plot for the TGS # 109 at 60% relative humidity. The gases are propane (open circles), carbon monoxide (open squares) and acetone (open diamonds). The concentrations are 0, 5, 10, 20, 50, 100, 200, 500, 1000 and 2000 ppm for propane and acetone, but 0, 5, 10, 20, 50, 80, 100, 200, 400 and 500ppm for carbon monoxide.
In Fig. 7 we examine the TGS # 109 and see that it is very selective to carbon monoxide. The other gases discussed in this paper give much the same signature as acetone and propane, and produce a phase angle of about 250”. The strongly concentration-dependent phase shift of carbon monoxide is caused by its increased oxidation at low temperature. 5. Discussion We can use the phase plots of Fig. 3 to select specific gases in the ambient environment. If we assume that only one unknown reducing gas is present, the best approach is to split the phasespace plots into separate regions, each defining a gas or group of gases. Different functional forms can be used for this, but the easiest is to fit straight line segments to the semi-log plots displayed. The equation of a straight line then becomes Y = A log[BX]
(4)
where Y is the phase angle of the first-harmonic FFT, X is the magnitude, and A and B are constant. A line segment can be defined to separate carbon monoxide from the alcohol region, which in turn can be separated from the alkane region by another line segment (see Fig. 3). The constants A and B must be determined through a calibration of the individual sensor element by measuring low and high concentration values of points in phase space of representative gases in each region. A simple averaging technique will determine the midpoints between the regions and the resultant values will define eqn. (4). This can be done with the minimum of two points necessary to define a straight line, or a least-squares
technique can be applied for a larger number of points. Once this is done, a simple mathematical algorithm can be used to determine the region that an unknown gas lies in. For example, one would block out phase space in bounded regions which completely surround each gas of interest, using the straight line segments of eqn. (4) as well as minimum magnitude threshold values and any other horizontal or vertical segments deemed necessary. The regions can be made as complex as desired with enough separation between regions to account for small humidity shifts. It then becomes only a matter of algebra to determine where an unknown point lies. In Fig. 7 the algorithm can be very simple if one only wants to detect carbon monoxide; the phase angle can be used as a direct measure of concentration with respect to a horizontal threshold. In general, once the gas is selected by the first order, the zeroth-order FFT magnitude can be used to measure concentration. It is also possible to detect two gas mixtures as implied by Fig. 5. Assuming that only two possible reducing gases can be present, but in unknown concentration, it is seen that every point in phase space represents a unique concentration for each gas. The degree of effort necessary to calibrate a sensor element is immense given the nonlinearity of the problem, as a very large number of mixtures must be tested. Once this is done, it is then only necessary, in principle at least, to compare a new measurement to the look-up matrix generated during the calibration procedure. In this method only the first-harmonic transform is used. It is possible to calibrate simultaneously for a number of different two-gas mixtures. For example, mixtures of carbon monoxide and ethanol, mixtures of ethanol and propane, and mixtures of carbon monoxide and propane could all be detected as long as all three gases are never present at one time. The presence of three gases precludes a unique solution. This method has potential as a diagnostic monitor of environmental air quality in buildings, indicating when further examination is necessary. There are a number of limitations in the use of the above techniques. Given the complexity of sensor calibration, the stability of sensor response is a major concern; tin oxide sensors are prone to aging and poisoning effects [2,7,23,25]. The effect of relative humidity can also greatly complicate the process, see Fig. 4. Finally, certain ‘noise’ gases such as hydrogen can produce a very confusing response, see Fig. 6. With the future development of sensor stability in both semiconductor and other types of sensors, the
288
fast Fourier-transform very important.
technique
should
become
Acknowledgements Halitec Industries wishes to acknowledge the financial support of an IRAP Grant from the National Research Council of Canada. We also acknowledge the assistance of Mahin Bahrami in some of the measurements.
References I D. D. Lee. B. K. Sohn and D. S. Ma, Low-power
thickfilm CO gas sensors, Sensors and .4cruarors. I2 (1987) 441447. 2 G. Heiland, Homogeneous semiconducting gas sensors, Sensors and Actuators, 2 (1982) 3433361. 3 N. Yamazoe, Y. Kurokawa and T. Seiyama, Effects of additives on semiconductor gas sensors. Sensors and Actuators, 4 ( 1983) 283 -289. 4 R. Lalauze, N. D. Bui and C. Pijolat, Chemical gaseous treatment of SnOz for a selective detection of gas, in T. Seiyama, K. Fueki, J. Shiokawa and S. Suzuki (eds.), Proc. Inr. Meet. Chemical Sensors. Fukuoka, Japan, Analytical Chemistry S_ymposia Series, Vol. 17. Chemical Sens0r.s. Elsevier, Amsterdam, 1983, pp. 47--S]. 5 R. Lalauze and C. Pijolat, A new approach to selective detection of gas by an SnO, solid-state sensor, Sensors and Acfuators, 5 (1984) 55. 63. 6 W. M. Sears, K. Colbow and F. Consadori, General characteristics of thermally cycled tin oxide gas sensors, Semicond. Sci. Technol., 4 (1989) 351~~359. 7 W. M. Sears, K. Colbow and F. Consadori, Algorithms to improve the selectivity of thermally cycled tin oxide gas sensors, Sensors and Actuators, 19 ( 1989) 333 349. 8 W. M. Sears, K. Colbow, R. Slamka and F. Consadori, A restricted flow thermally cycled gas sensor. Sensors und Actuators B, I (1990) 62267. 9 W. M. Sears, K. Colbow. R. Slamka and F. Consadori, Surface adsorption and gas consumption in restricted flow thermally driven gas sensors, Semicond. Sci. Technol., 5 (1990) 45553. 10 N. Komori, S. Sakai and K. Komatsu, Sintered SnO, sensor for methane, in T. Seiyama. K. Fueki, J. Shiokawa and S. Suzuki (eds.), Proc. Int. Meet. Chemicul Sensors, Fukuoka, Japan, Analytical Chemisrry Symposia Series, Vol. 17. Chemical Sensors. Elsevier. Amsterdam, 1983, pp. 57-61. 11 H. D. Le Vine. Method and apparatus for operating a gas sensor, U.S. Patent No. 3 906 473 (Sept. 16, 1975). 12 H. Eicker, Method and apparatus for determining the concentration of one gaseous component in a mixture of gases, U.S. Patent No. 4 012 692 (Mar. 15, 1977). 13 L. J. Owen, Gas monitors. U.S. Parent No. 4 185 491 (Jan. 29, 1980). 14 G. N. Advani, R. Beard and L. Nanis, Gas measurement method, U.S. Patent No. 4 399 684 (Aug. 23, 1983). 15 S. Bukowiecki, G. Pfister, A. Reis, A. P. Troup and H. P. Uhi, Gas or vapor alarm system including scanning gas sensors, U.S. Patent No. 4 567 475 (Jan. 28, 1986). 16 T. Oyabu, Y. Ohta and T. Kurobe, Tin oxide gas sensor and countermeasure system against accidental gas leaks, Sensors and Actuators, 9 ( 1986) 30 I -3 Il..
J. W. Gardner and H. T. Chan, The 17 H. V. Shurmer, application of discrimination techniques to alcohols and tobaccos using tin-oxide sensors, Sensors and Actuators. I8. (1989) 361 .371. T. Nakamoto and T. Moriizumi, 18 K. Ema. M. Yokoyama, Odour-sensing system using a quartz-resonator sensor array and neural-network pattern recognition, Sensors und Actuarors, I8 ( 1989) 291-296. A 19 B. R. Kowalski and C. F. Bender. Pattern recognition. powerful approach to interpreting chemical data, J. Am. Chem. Sot.. 94 (1972) 5632 -5639. of 20 J. R. Stetter, P. C. Jurs and S. L. Rose, Detection hazardous gases and vapors: pattern recognition analysis of data from an electrochemical sensor array, Anal. Chem., 58 (1986) 860.. 866. Microcomputational selectivity enhance21 P. K. Clifford, ment of semiconductor gas sensors, in T. Seiyama, K. Fueki. J. Shiokawa and S. Suzuki (eds.), Proc. Inf. Meet. Chemical Sensors. Fukuoku, Japan, Analytical Chemistry Symposiu Series, Vol. Il. Chemical Sensors. Elsevier. Amsterdam, 1983, pp. 153.. 158. Gas Sensors, Technical Information from 22 Semiconductor Figaro Engineering Inc., Japan. 23 J. Watson, The tin oxide gas sensor and its applications. Sensors and ,4cruators, 5 (1984) 29-42. S. A. Teukolsky and W. T. 24 W. H. Press, B. P. Flannery, Vetterling, Numerical Recipes in Fortran. Cambridge University Press, New York. 1986. 25 S. J. Gentry and T. A. Jones, The role of catalysis in solid-state @is sensors, Sensors and Actuators, 10 (1986) 141~ 163.
Biographies
W. M. Sears received his B.Sc in physics from Acadia University in Nova Scotia ( 1972) and his Ph.D. from McMaster University in Ontario (1978). Since then he has worked as a researcher at the University of Guelph, the University of Ottawa, and at the Energy Research Institute, Simon Fraser University. He is presently assistant professor of physics at Lakehead University, Thunder Bay. His present interests include surface science and gas sensors. K. Colbow received his Ph.D. in physics from the University of British Columbia in 1963. Following an appointment at Bell Telephone Laboratories ( 1963 -65) he joined the faculty at Simon Fraser University, where he is presently professor of physics and a member of the Energy Research Institute. His main interests are in thin films, surface physics and chemical sensors. Rick Slamka received his B.A.Sc. in engineering physics from the University of British Columbia in 1986. Since that time he has worked on several research projects involving optics, electrooptics and high-speed digital processing systems, and is now working on gas-sensing technology.
289
Franc0 Consadori received his Ph.D in physics from the University of Pavia, Italy in 1969. He has held research fellowships at Simon Fraser University (1969-71) and the University of Montreal (1971-72), as well as industrial positions at
EG&G/ORTEC ( 1973-76), CTF Systems (197778), and Newtec Industries (197885). Presently he is president of Halitec Industries, Vancouver, BC, Canada, with wide-ranging interests in gas sensors and sensor systems.
283
Selective Thermally Cycled Gas Sensing Using Fast Fourier-transform Techniques W. M. SEARS* and KONRAD COLBOW Physics Department, Simon Fraser University, Budy,
BC, VSA IS6 (Cam&)
RICK SLAMKA and FRANC0 CONSADORI Halitec Industries, 2724 SE Marine Drive, Vrmcouver, BC, V5S WI (Cam&z) (Received November 8, 1989; accepted in revised form June 8, 1990)
Abstract The conductance versus time signatures of three types of thermally cycled gas sensors have been measured for a number of different reducing gases over a wide range of concentrations. A fast Fourier-transform data analysis technique is used to produce first-harmonic polar plots of phase angle versus magnitude of both single gases and mixtures as a function of concentration. It is found that this produces a good separation among many of the gases tested and leads to a clear selective criterion for the detection of single unknown gases. Gas mixtures can sometimes be distinguished as well, but with less reliability.
1. Introduction
Thermal cycling of semiconductor gas sensors has been examined over the past few years because of its promise as a technique for selective detection of different reducing gases [l-9]. Thermal-cycling techniques take advantage of the fact that different classes of reducing gases have different reaction rates. For example, carbon monoxide and hydrogen sulfide will oxidize on the sensor at relatively low temperatures (starting near room temperature), alcohols and ketones at intermediate temperatures (200 “C and above) and alkanes (propane and methane in particular) at high temperatures (above about 400 “C) [l-4, lo]. Thus sensors run at different temperatures will show a degree of selectivity. It is in fact better than the temperatures given would indicate, as at high temperatures the more easily oxidized gases are strongly consumed at the sensor surface, and therefore decrease the conductance change as measured in the sensor interior. By cycling the temperature, one detects the whole range of gas reactions in different regions
‘Present address: Physics Department, Lakehead University, 955 Oliver Road, Thunder Bay, Ont., P7B 5E1, Canada. 09254005/90/$3.50
of the heating or cooling cycle, and also avoids the surface contamination that occurs after too long exposure at low temperature. The conductance signatures seen for temperature cycling in the presence of different gases have been the subject of recent patents [ 11- 151. The conductance signatures seen for gases are due to two effects. One reflects the temperature dependence of the gaseous reaction rates just mentioned, the other involves the conductance changes induced by the high-temperature reactions of chemisorbed gases with surface oxygen. The gases are adsorbed in the cool parts of the thermal cycle. A number of pattern-recognition techniques [16-211 have been applied to sensor arrays as a means to improve selectivity. In such methods the outputs of N sensing elements are plotted in an N-dimensional space. If the sensor outputs are a linear function of concentration and if the principle of superposition holds, the various gases will be clearly separated [ 171. In general, one cannot expect these conditions to hold. Pattem-recognition techniques can still be used, however, and it is common to map the N-dimensional space onto two dimensions representing the principal components (factor analysis). Various gases are then expected to cluster in different regions with various degrees of success [ 17,19,20]. We have attempted to obtain the same type of information from one sensor. In the past we have studied various ways of deriving the maximum amount of information possible from the thermalcycle signatures including open- and restrictedflow mounting [7,8]. In what follows we present a further improvement, using a fast Fourier-transform technique, and further address the perennial problem of gas mixtures. 2. Experimental Thermal-cycling measurements were performed using three types of Taguchi gas sensors, manufactured by Figaro Engineering Inc. [22]. The active ingredient of these sensors is sintered tin 0 Elsevier Sequoia/Printed
in The Netherlands
284
oxide powder mixed with palladium and a binder. In the TGS # 812 and TGS # 822 this material is baked onto the outside of a small ceramic tube which is covered by an interdigitated gold pattern [23]. The gold allows the electrical conductance of the tin oxide to be measured. Inside the tube is a coiled metal wire which is used to heat the sensor. These two sensors are used to detect general combustible gases; the TGS # 822 is the replacement for the TGS # 812, which is no longer manufactured. The response to various gases is very similar but not identical for these two sensors, as we will see. The TGS # 109 is constructed in a different manner. In this case two heating elements are embedded at opposite ends of a small rectangular box-shaped pellet of the sintered tin oxide/binder mixture. In operation one element is connected to an appropriate current supply to heat the sensor and also serves as ground for the conductance measurement. The other element is biased above ground so as to allow a measurement to be made of the conductance between them. Figaro has designed this sensor to operate from an a.c. transformer with a heater bias of 1 V and a signal bias of 100 V. However, conductance measurements can be made with lower voltages. This sensor was designed for alkanes or general fuel gas detection. It is also used by Figaro as part of their thermally cycled TGS # 203, which is used for the selective detection of carbon monoxide. The sensor temperature is determined by the size of the voltage applied across the heater wire. In this study we have cycled the TGS # 812 between 0 and 6.2 V, the TGS # 822 between 0 and 5 V, and the TGS # 109 between 0 and 1 V, in a thermal cycle of 32 s on and 32 s off for the last two. The cycle for the TGS # 812 was 25 s on and 25 s off. These voltages were chosen to give a similar temperature for the three sensors, which is estimated as 400 “C from earlier work [6, 16, 231; The sensor surface temperature is affected not only by the heater voltage but also by Joule heating caused by the current used to measure the conductance, and by the exothermic nature of the inflammable gases the sensor is detecting. This makes knowledge of the exact surface temperature under all conditions that much more difficult to obtain. The TGS # 109 is particularly susceptible to this effect. In our experiments the heaters of the TGS # 8 12 and TGS # 822 were driven directly by a Hewlett-Packard function generator (model 3310B), but the TGS # 109 required a higher current and in this case a Darlington pair power amplifier was placed between the output of the
function generator and the gas sensor heating element. The changes in conductance of the sensors were measured at an applied bias of 5 V across the tin oxide. Digital electrometers were used to measure simultaneously the voltage and current and this allowed the real-time measurement of the conductance changes to be done using a computer data-acquisition system. The sensor was tested inside a 100 1 box where gases and vapors to be tested were directly injected. Laboratory air was used as the clean air baseline, which was sufficient for the concentrations used, as seen in Figs. 3, 4, 6 and 7 where ‘zero’ concentration values are plotted.
3. Data Reduction In analyzing the response of the three sensors used in this study, a series of gas concentrations was injected into the test chamber. The concentrations were spread along a logarithmic scale from zero to about 2000 ppm for gases such as propane, carbon monoxide, acetone, ethanol, isopropanol and hydrogen. For each injection the sensor was allowed to come to a steady-state thermal cycle, usually within two or three cycles. Starting when the heater switches on, conductance versus time data were stored in a computer file for one complete cycle. For measurements on the TGS # 822 and the TGS # 109, 64 data points were taken, one per second as shown in Fig. 1. For the TGS # 812, 256 data points were taken over a 50 s period. The conductance response is ‘steady state’; it will repeat every cycle as long as the concentration does not change. We see in Fig. 1 that different gases give characteristic shapes under thermal cycling, and it was assumed that a Fourier-transform technique would help in separating the response to one type of gas from another.
G
I I\
I
Fig. I. Typical thermal-cycling signatures of a number of gases for the TGS # 822. For the first 32 s, the heater is on (5 V). 500 ppm of (A) propane, (B) carbon monoxide, (C) ethanol and (D) hydrogen.
285
A discrete Fourier transform is defined by N-l
Y,=(l/N)
1
&IV-t
(1)
m=O
where k = 0 to N - 1 and W =
exp( - 2n j/N)
(2)
where j equals the square root of minus one. X,,, represents values taken from the conductance versus time curves and Yk represents the transform in the frequency domain; in this paper we are primarily concerned with the zeroth (k = 0) and the first (k = I) harmonic of the transform. We have normalized the transform by the total number of points (N). This is non-standard, ,but was done so that the result would be independent of the size of the tranform, except for resolution effects. The transform was calculated using the Cooley -Tukey fast Fourier-trahsform (FFT) technique under the conditions that the values (X,,,) are equally spaced in time and number in total a power of two [24]. This algorithm gives data expressed as the real and imaginary components of Y,. For convenience of display and analthis into polar ysis, we have recalculated coordinates, magnitude and phase angle. The zeroth harmonic is plotted as the magnitude versus the concentration of the gas tested (see Fig. 2) because the phase angle is always zero. The first harmonic is plotted as the phase angle versus magnitude for various gases and concentrations (see for example Figure 3). The phase angle is only defined within 360” and has been shifted in the Figures for convenience of display. 4. Results Figure 1 shows typical thermal cycling conductance curves for a number of gases at a concentra-
tion of 500 ppm for the TGS # 822. The features of the four classes of reducing vapors shown here have been studied in detail in previous papers [6,7] and we will only summarize them here. Propane, and alkanes in general, shows a flat response as it only responds at a high sensor temperature. Carbon monoxide, at the other extreme, responds at low temperature and gives peaks early and late in the cycle. Ethanol (alcohols and ketones in general) and hydrogen represent an intermediate position, with slight differences occurring in the position of the initial peak and the size of the recovery peak when the sensor is turned off. These features are a function of concentration as well as of the type of gas, and show up most plainly in the Fourier transform. The curves for the TGS # 8 12 and the TGS # 109 are slightly different; this will be discussed when the transform results are presented. The zeroth harmonic of the transformed data of Fig. 1 and that of other gas concentrations is shown in Fig. 2. This harmonic is a direct average of the 64 data points and therefore should follow the power-law dependence usually seen in tin oxide sensors: G =G,+&C”
(3)
Go is the conductance with no reducing gas present, &, is a constant and C is the reducing gas concentration. Figure 2 shows that this happens only to an approximate degree. Nevertheless, in a practical device based on FFT’ analysis the sensor could be calibrated for concentration using this harmonic. Figure 2 shows the relative sensitivity of the device to three gases as well as the effect of relative humidity. We see that the effect of humidity is much larger at low concentrations, except for propane. Figure 3 displays the first harmonic for several gases; hydrogen will be discussed later. The con--_
-2,
I
1
,
I
Fig. 2. Zeroth-harmonic FFT vs. concentration for the TGS+ 822. Propane at 50% (open circles) and 95% (Elled circles) relative humidity; carbon monoxide at 50% (open triangles) and 95% (f&i triangles); ethanol at 50% (open squares) and 95% (@led squares). The propane data have been scaled up by a factor of ten for clarity.
Fig. 3. First-harmonic FFf polar plot for the TGS # 822 at 50% relative humidity. The gases arc propane (6lled circles), carbon monoxide (open circles), acetone (open triangks), ethanol (open squares) and isopropanol (open diamonds). The concentrations for carbon monoxide are 0, 5, 10, 20, 40, 80, 100,200,400 and 500 ppm. For all other gases they are 0, 5, 10,20, 50, 100,200,500, loo0 and 2000 ppm.
286
centrations increase along the displayed data from left to right. That is, increased concentration leads to an increased magnitude as for the zeroth harmonic, but less reliably. The first harmonic is best used to enhance the sensor selectivity. Second and higher harmonics of the transform can also be displayed; however, we find that the information obtained is very similar to that of the first harmonic but less reproducible and thus less selective. In Fig. 3 we see a number of effects. First, the phase angle can be taken as a measure of curve shape or gas signature and the shift in phase angle as a measure of the degree to which this signature changes with concentration. All gases start from an approximately constant point representing zero concentration and only separate when the concentration is sufficient to develop unique signatures. At high concentrations the phase shift stabilizes for all gases shown, except for carbon monoxide where the signature continues to change with concentration. Another feature of Fig. 3, one of paramount interest, is the separation in ‘phase space’ of the three types of reducing gases. Mathematical algorithms can easily be developed to delineate these regions. Figure 4 shows the same type of data for gases at a higher relative humidity (95%). The general features are similar, but there are nonlinear shifts in magnitude and angle for the gases displayed. The major shift is nonlinear in magnitude, with the low concentrations shifted much more than the higher ones. Figure 5 examines the effect of mixtures on the first-harmonic response. The six sets of mixture data converge onto the solid line representing the pure ethanol response (20, 100 and 500 ppm) as seen in Fig. 3. The sensor element is a different TGS # 822 from the one used so far and all values are shifted 15” downward in phase angle. This shift may be due to a slight difference in operating temperature, as such an effect does occur. As propane or CO is added to ethanol, the
Fig. 4. First-harmonic FFT polar plot for the TGS + 822 at 95% relative humidity; otherwise as in Fig. 3.
Fig. 5. Effect of gas mixtures on the first-harmonic response at 50% relative humidity for a second TGS # 822. 20, 50, 100, 200, 500, 1000 and 2OOOppmpropane in 20 ppm ethanol (filled circles); 50, 100,200, 500, 1000 and 2WOppm propane in 1OOppm ethanol (filled triangles); 200, 500, 1000 and 2000 pii propane in 500 ppm ethanol @lied squares); 10,40, 100, 200. 400 and 5OOm_nncarbon monoxide in 2Oppm ethanol (open circles); lb: 40, 100, 200, 400 and 5OOppm carbon monoxide in 1OOppm ethanol (open triangles); 40, 200,400 and 5OOppm carbon monoxide in 5OOppm ethanol (open squares). The solid line represents the pure ethanol response.
phase shifts gradually towards the proper position for the pure gas (down for propane or up for CO). To a certain extent mixtures represent a unique point in phase space as long as only two gases are considered. Note, however, that mixtures of propane and CO will show phase shifts in the region of ethanol response. Figure 6 is the first-harmonic transform for the TGS # 8 12 and the pattern is similar to that of Fig. 3, with a slightly greater spread in phase angle. The major point of interest in this Figure is the response to hydrogen gas, which spirals from one section to another with increasing concentration. This makes hydrogen difficult to identify with this method for both the TGS # 812 and the TGS # 822, which shows the same effect.
Fig. 6. First-harmonic FFT polar plot for the TGS + 812 at 40% relative humidity. The gases are propane (open circles), carbon monoxide (open triangb), acetone (open squares), ethanol (open diamonds) and hydrogen (Bled diamonds). The concentrations are 0, 3, 10,40, 80, 150, 500, 1000, 3000 and 5OOOppmfor all gases except for the tit two, which lack a 3 ppm point.
287
380 -
Fig. 7. First-harmonic FFT polar plot for the TGS # 109 at 60% relative humidity. The gases are propane (open circles), carbon monoxide (open squares) and acetone (open diamonds). The concentrations are 0, 5, 10, 20, 50, 100, 200, 500, 1000 and 2000 ppm for propane and acetone, but 0, 5, 10, 20, 50, 80, 100, 200, 400 and 500ppm for carbon monoxide.
In Fig. 7 we examine the TGS # 109 and see that it is very selective to carbon monoxide. The other gases discussed in this paper give much the same signature as acetone and propane, and produce a phase angle of about 250”. The strongly concentration-dependent phase shift of carbon monoxide is caused by its increased oxidation at low temperature. 5. Discussion We can use the phase plots of Fig. 3 to select specific gases in the ambient environment. If we assume that only one unknown reducing gas is present, the best approach is to split the phasespace plots into separate regions, each defining a gas or group of gases. Different functional forms can be used for this, but the easiest is to fit straight line segments to the semi-log plots displayed. The equation of a straight line then becomes Y = A log[BX]
(4)
where Y is the phase angle of the first-harmonic FFT, X is the magnitude, and A and B are constant. A line segment can be defined to separate carbon monoxide from the alcohol region, which in turn can be separated from the alkane region by another line segment (see Fig. 3). The constants A and B must be determined through a calibration of the individual sensor element by measuring low and high concentration values of points in phase space of representative gases in each region. A simple averaging technique will determine the midpoints between the regions and the resultant values will define eqn. (4). This can be done with the minimum of two points necessary to define a straight line, or a least-squares
technique can be applied for a larger number of points. Once this is done, a simple mathematical algorithm can be used to determine the region that an unknown gas lies in. For example, one would block out phase space in bounded regions which completely surround each gas of interest, using the straight line segments of eqn. (4) as well as minimum magnitude threshold values and any other horizontal or vertical segments deemed necessary. The regions can be made as complex as desired with enough separation between regions to account for small humidity shifts. It then becomes only a matter of algebra to determine where an unknown point lies. In Fig. 7 the algorithm can be very simple if one only wants to detect carbon monoxide; the phase angle can be used as a direct measure of concentration with respect to a horizontal threshold. In general, once the gas is selected by the first order, the zeroth-order FFT magnitude can be used to measure concentration. It is also possible to detect two gas mixtures as implied by Fig. 5. Assuming that only two possible reducing gases can be present, but in unknown concentration, it is seen that every point in phase space represents a unique concentration for each gas. The degree of effort necessary to calibrate a sensor element is immense given the nonlinearity of the problem, as a very large number of mixtures must be tested. Once this is done, it is then only necessary, in principle at least, to compare a new measurement to the look-up matrix generated during the calibration procedure. In this method only the first-harmonic transform is used. It is possible to calibrate simultaneously for a number of different two-gas mixtures. For example, mixtures of carbon monoxide and ethanol, mixtures of ethanol and propane, and mixtures of carbon monoxide and propane could all be detected as long as all three gases are never present at one time. The presence of three gases precludes a unique solution. This method has potential as a diagnostic monitor of environmental air quality in buildings, indicating when further examination is necessary. There are a number of limitations in the use of the above techniques. Given the complexity of sensor calibration, the stability of sensor response is a major concern; tin oxide sensors are prone to aging and poisoning effects [2,7,23,25]. The effect of relative humidity can also greatly complicate the process, see Fig. 4. Finally, certain ‘noise’ gases such as hydrogen can produce a very confusing response, see Fig. 6. With the future development of sensor stability in both semiconductor and other types of sensors, the
288
fast Fourier-transform very important.
technique
should
become
Acknowledgements Halitec Industries wishes to acknowledge the financial support of an IRAP Grant from the National Research Council of Canada. We also acknowledge the assistance of Mahin Bahrami in some of the measurements.
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Biographies
W. M. Sears received his B.Sc in physics from Acadia University in Nova Scotia ( 1972) and his Ph.D. from McMaster University in Ontario (1978). Since then he has worked as a researcher at the University of Guelph, the University of Ottawa, and at the Energy Research Institute, Simon Fraser University. He is presently assistant professor of physics at Lakehead University, Thunder Bay. His present interests include surface science and gas sensors. K. Colbow received his Ph.D. in physics from the University of British Columbia in 1963. Following an appointment at Bell Telephone Laboratories ( 1963 -65) he joined the faculty at Simon Fraser University, where he is presently professor of physics and a member of the Energy Research Institute. His main interests are in thin films, surface physics and chemical sensors. Rick Slamka received his B.A.Sc. in engineering physics from the University of British Columbia in 1986. Since that time he has worked on several research projects involving optics, electrooptics and high-speed digital processing systems, and is now working on gas-sensing technology.
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Franc0 Consadori received his Ph.D in physics from the University of Pavia, Italy in 1969. He has held research fellowships at Simon Fraser University (1969-71) and the University of Montreal (1971-72), as well as industrial positions at
EG&G/ORTEC ( 1973-76), CTF Systems (197778), and Newtec Industries (197885). Presently he is president of Halitec Industries, Vancouver, BC, Canada, with wide-ranging interests in gas sensors and sensor systems.