Amperometric sensor array for NOx, CO, O2 and SO2 detection

Sensors and Actuators B 122 (2007) 165–173

Amperometric sensor array for NOx, CO, O2 and SO2 detection Jing-Shan Do ∗ , Po-Jen Chen Department of Chemical Engineering, Tunghai University, Taichung 40704, Taiwan Received 11 December 2005; received in revised form 20 May 2006; accepted 22 May 2006 Available online 23 June 2006

Abstract Using Nafion® /Pt (or Au)/ceramic plate micro-electrodes prepared by microfabricating technologies as the sensing electrodes of a gas sensor array and BPN (backpropagation neural network) as the recognition algorithm, the sensing properties and optimal BPN structure for monitoring gas mixtures containing NO2 , NO, SO2 , CO and O2 were studied. The limiting current potentials for anodic oxidation of NO on Au, SO2 and CO on Pt, and cathodic reduction of NO2 on Au and O2 on Pt were found to be 0–0.5, −0.4 to 0.2, −0.4 to 0.1, 0.2–0.5 and 0.2–0.7 V (versus Pt), respectively. When the potentials were set at the values of mass-transfer control, the mass-transfer resistance in the gas diffusion layer could be neglected for gas flow rates greater than 300 ml min−1 , and the individual sensitivities to NO2 , NO, SO2 , CO and O2 were obtained to be 0.614, 0.445, 0.612, 0.557 and 0.433 nA ppm−1 , respectively. Using the sensor array for monitoring gas mixtures, the optimal learning cycles, learning rate, input nodes, nodes in one hidden layer and output nodes of BPN based on 50 sets of learning data were obtained to be 1000, 0.01, 5, 7 and 5, respectively. The average predicting error for detecting an unknown gas mixture was found to be 8.61% based on the sensor array and optimal BPN. © 2006 Elsevier B.V. All rights reserved. Keywords: Sensor array; Amperometric gas sensor; CO2 ; NOx ; SO2 ; BPN

1. Introduction It is very important for the environmental protection to measure the level of components in the exhaust gases of vehicles, boilers and other industrial applications. The electrochemical gas sensors based on various materials and principles have been widely developed to monitoring the components, e.g. COx , NOx and SOx , in gas phase [1]. Amperometric gas sensors have an advantage of linear responses versus the concentrations of sensing targets to result in a high precision characteristic [2–4]. In general, the selectivity of amperometric gas sensors can be increased by a proper choice of sensing materials (electrodes) and potentials. However, the interferences from other components presented in the sensing systems are not totally obviated for the systems with numerous components. In our previous results indicated that the sensing properties of an amperometric NO gas sensor were significantly interfered by the presence of NO2 and SO2 in gas phase [3].

∗

Corresponding author. E-mail address: [email protected] (J.-S. Do).

0925-4005/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2006.05.030

Recently, a strategy of sensor arrays different from the traditional concept of “lock-and-key” is developed to overcome the interference occurred in the chemical sensors [5]. A number of chemicals may be reacted to produce signals on one of the elements of a sensor array. The high selectivity for a specific component on any specific element of the sensor array is not expected, and hence a pattern recognition method will play an important role for measuring the levels of the components [5]. Electronic noses based on conductometric and potentiometric responses have been widely studied to detect various substances in air which includes volatile organic compounds (VOCs), water vapour, methane, carbon dioxide, ammonia, hydrogen sulphide and other toxic and non-toxic gases [6,7]. Recently, the backpropagation network (BPN) have been used to monitor the organic vapours based on a multi-channel piezoelectric (PZ) quartz crystal detection system [8], NOx based on conductometric sensors [9,10], and NO2 and CO based on a PZ quartz crystal detection system [11]. Amperometric sensor arrays have been fabricated to detect 22 hazardous gases and vapors [12], trinitrotoluene and other nitrated explosives [13], O2 , NO, combustible gases [4], CO2 [14], acetaldehyde, NO and SO2 [15]. Using a tubular cell containing two working electrodes with stabilized zirconia as a

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solid electrolyte, the cross-effect for sensing O2 and combustible components can be eliminated by optimizing the conditions of temperature and applied potential [4]. The concentrations of acetaldehyde, NO and SO2 were detected with four individual sensors based on liquid electrolytes [15]. However, amperometric sensor arrays based on polymeric electrolytes for simultaneously detecting NOx , CO, O2 and SO2 are not reported in literature. Microfabrication technologies were used to prepare the present sensing chip with an alumina plate as a substrate. Pt and Au used as the working electrodes of the sensor array were prepared by ion sputtering and covered with Nafion® as the polymeric electrolyte. The sensing characteristics of the sensor array were conducted in three stages: (a) studying the individual sensing properties to NO, NO2 , CO, O2 and SO2 of the individual sensing element, (b) collecting the sensing data to gas mixtures of various compositions on the sensor array, and (c) training the BPN based on the collected data, and predicting the levels of components in an unknown gas mixture.

defined by two masks, one for preparing Pt electrodes and the other for Au, followed by UV exposure and development processes. The Pt and Au electrodes were prepared on the substrate by ion sputtering. Eight sets of electrodes were prepared on one sensor array. Each set of electrodes was composed with Pt or Au as a working electrode, and Pt as counter and reference electrodes. The electric circuits were adhered to the bonding pad of electrodes by silver paste to connect the electrodes with outside world. Finally, a 30 or 20 ␮l Nafion® solution (85%, Aldrich) was cast on the surface of the sensor array (4.0 cm2 ) to act as a polymeric electrolyte with a thickness of 25.0 or 16.7 ␮m. After dried at room temperature, the sensor array was baked in an oven at 120 ◦ C for 1 h. The thickness of Nafion® film was measured by a thickness gauge (Teclock, SM 1201). The configuration of the sensor array is shown in Fig. 1. Eight sets of electrodes were prepared on an Al2 O3 plate with a dimension of 2.5 cm × 2.5 cm. The interval of the working, counter and reference electrodes was 150 ␮m. The surface area of the working electrode was designed to be 19.14 mm2 .

2. Experimental

2.2. Reaction characteristics

2.1. Preparation of the sensor array

The nitrogen gas (background gas) humidified by flowing through a water bath was mixed with a higher concentration of test gas to adjust a desired concentration by means of a flow-rate controller set (Sierra 902C, 840L, and Protec PC500). Two rubber O-rings were placed on both sides of the sensor array to prevent any gas leakage from the gas chamber. I–E (current–potential) curves of various test gases were measured by a potentiostat (EG&G 273). All of the applied potentials shown in this work were measured against the Pt reference electrode illustrated in Fig. 1 in the sensing environment. Five mini potentiostats (BAS LC-3D) were used to supply the suitable potentials for five sets of sensing ele-

An alumina plate (substrate of the sensor array) was immersed in acetone and then methanol for 5 min, respectively, to remove the grease and organic impurities on the surface of substrate, and the residual solvents on the substrate were blew away with N2 . A trace of the solvents and humidity adsorbed on the surface and within the pores of the alumina substrate were evaporated and removed by baking in a 150 ◦ C oven for 1 h. A positive photoresist (PR, Microposit 1818) was immediately coated by a spin coater on the substrate, and baked in an 85 ◦ C oven for 10 min. The pattern of the sensor array electrodes was

Fig. 1. Schematic sensor array with eight sets of sensor elements.

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∂Qnj

167



∂netnj



ments on the sensor array chip to measure the signals of gas mixtures.

∂E = ∂wij

3. BPN architecture and calculation

The following equation could be obtained based on Eqs. (1)–(6):

The structure of neural network contained five input signals obtained from five sensor elements as an input layer, a suitable hidden layer contained some nodes and five output signals in the output layer shown in Fig. 2. The output of the j node in the n layer was a nonlinear function (activation function) of the output in the n − 1 layer: Qnj

=

f (netnj )

(1)

where f is a transfer function, and the summation function netnj was expressed as  netnj = (2) wij Qn−1 − Tj i i

where wij is the weight between the i node in the n − 1 layer and the j node in the n layer, and Tj is the threshold of node j. The learning quality of the network was justified by the following error function: 1 E= (Ij − Qj )2 (3) 2 j

where Ij and Qj are the target output and the output of the network, respectively. The steepest gradient descent was applied, and the weight of change was proportional to ∂E/∂wij : wij = −η

∂E ∂wij

(4)

where η was the learning rate. According to the chain rule, the following expressions were obtained [16–18]:    ∂netnj ∂E ∂E (5) = ∂wij ∂netnj ∂wij

∂E ∂Qnj

∂netnj

∂wij

∂E = −δnj Qn−1 i ∂wij

(6)

(7)

where Qi n−1 is the output of the lower layer connected by wij , and δj n was defined as −∂E/∂netnj [16]. Substituting Eq. (7) into Eq. (4), the following equation was obtained: wij = ηδnj Qn−1 i

(8)

If a sigmoid function was used as the activation function: f (netj ) =

1 1 + e−netj

(9)

the deltas were given by δnj = (Ij − Yj )Yj (1 − Yj )  δnj = Hj (1 − Hj ) δn+1 k wjk

(10) (11)

k

for the output-layer and hidden-layer units, respectively. Yj and Hj are the output in the output- and hidden-layer, respectively. The number of hidden layers, the number of nodes in every hidden layer, the learning cycle and rate were determined by trial and error to minimize the learning error. The nodes in the output-layer were equal to the number of components in gas phase; its value was 5 in this work. A FORTRAN program was written to carry out the calculations. 4. Results and discussion 4.1. Sensing properties of gas components The conductivity of the Nafion® film on the sensor array analyzed by ac impedance revealed that the similar conductivity was obtained for relative humidities (RH) greater than 80%,

Fig. 2. Schematic diagram of BPN structure.

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mass-transfer resistance within the gas film and Nafion® film in series, were proposed in the model for transporting NO2 from the bulk phase to the surface of working electrode [3]. The similar behavior was found on Au by changing the applied potential in the range of −0.2 to 0.7 V (versus Pt) in N2 atmosphere (dotted curve of Fig. 3). Using Au as a working electrode, the net current of the anodic oxidation of NO sharply increased from 8.7 to 40.8 nA with an increase in the potential from −0.2 to 0.0 V (versus Pt), and the potentials of limiting current were located in the range of 0.0–0.5 V (versus Pt). The anodic oxidation of nitric oxide could be expressed as [21,22]: NO + 2H2 O − 3e− → HNO3 + 3H+

(13)

As illustrated by the dotted curves in Fig. 3, the significant increase in anodic current for the potentials greater than 0.5 V (versus Pt) was contributed by the anodic oxidation of water molecules existed in the humid environment. The experimental results also revealed that a serious interference would be expected and caused by the presence of NO (NO2 ) for sensing NO2 (NO) due to the overlap of the potentials for anodic oxidation of NO and cathodic reduction of NO2 .

Fig. 3. I–E curves of NO2 and NO on Au electrode. Working electrode: Au; counter electrode: Pt; reference electrode: Pt; electrolyte: 16.7 ␮m Nafion® ; area of Au = 19.14 mm2 , [NO2 ] = 100 ppm, [NO] = 100 ppm; T = 28 ± 1 ◦ C, 100% RH; gas flow rate = 200 ml min−1 . (䊉, ) I–E in N2 ; () I–E of NO2 ; () I–E of NO; () net I–E of NO2 ; () net I–E of NO.

while the conductivity significantly decreased with a decrease in RH less than 80%.

4.1.1.2. I–E curves based on Pt electrode. The I–E curves of SO2 , CO and O2 using Pt as a working electrode are shown in Fig. 4. The electrochemical behavior on the Pt electrode in N2 atmosphere was similar to that on Au. The oxygen and hydrogen evolutions caused by the anodic oxidation and cathodic reduction of H2 O were found for the potentials greater and less than 0.4 and −0.1 V (versus Pt), respectively (Fig. 4). By increasing the potential from 0.0 to 0.2 V (versus Pt), the anodic current for

4.1.1. I–E relationships 4.1.1.1. I–E curves based on Au electrode. The I–E relationships using Au as a working electrode are shown in Fig. 3. The current was insignificant for the potentials in the range of −0.1 to 0.2 V (versus Pt) in N2 environment, and the anodic and cathodic currents found for the potentials greater than 0.2 V (versus Pt) and less than −0.1 V (versus Pt) were deduced to be the anodic oxidation and cathodic reduction of water existed in the humid gas (100% RH), respectively. For 100 ppm NO2 in N2 with a flow rate of 200 ml min−1 introduced into the gas chamber, a significant reduction current was found at 0.4 V (versus Pt), and the reduction current increased with a decrease in the potential of the working electrode. The reduction current was caused by the cathodic reduction of NO2 [19,20]: NO2 + 2H+ + 2e− → NO + 2H2 O

(12)

The net reduction current, which was obtained by subtracting the background current obtained in N2 from the current occurred for 100 ppm NO2 , increased from 21.5 to 47.0 nA with the decrease in potential from 0.4 to 0.2 V (versus Pt) (Fig. 3). Further decreasing the potential to less than 0.2 V (versus Pt) slightly changed the cathodic current for reducing NO2 . The experimental results revealed that the cathodic reduction of NO2 on the Au electrode was controlled by mass-transfer for potentials less than 0.2 V (versus Pt). Two kinds of mass-transfer resistances, e.g.

Fig. 4. I–E curves of SO2 , CO and O2 on Pt electrode. Working electrode: Pt; counter electrode: Pt; reference electrode: Pt; electrolyte: 16.7 ␮m Nafion® ; area of Pt = 19.14 mm2 , [SO2 ] = 100 ppm, [CO] = 100 ppm, [O2 ] = 100 ppm; T = 28 ± 1 ◦ C, 100% RH; gas flow rate = 200 ml min−1 . () I–E in N2 ; () I–E of SO2 ; (䊉) net I–E of SO2 ; () I–E of CO; () net I–E of CO; (♦) I–E of O2 ; () net I–E of O2 .

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oxidizing 100 ppm SO2 in 90% RH N2 significantly increased from 21 to 64 nA. The limiting current was obtained for the potentials greater than 0.2 V (versus Pt) as illustrated by the solid line in Fig. 4. Sulfur dioxide was first combined with H2 O to form SO3 2− , and then oxidized on the Pt anode [23]: 2SO3 2− → S2 O6 2− + 2e−

(14)

The anodic oxidation of CO on Pt in the presence of humidity in sensing environment could be expressed as [24]: CO + H2 O → CO2 + 2H+ + 2e−

(15)

The anodic current for oxidizing 100 ppm CO on the Pt electrode increased from 22 to 58 nA with an increase in anodic potential from 0.0 to 0.2 V (versus Pt), and the limiting current was found for potentials in the range of 0.2–0.7 V (versus Pt) (dashed lines in Fig. 4). The slight decrease in anodic current for the potentials greater than 0.4 V (versus Pt) might be due to the irreversible adsorption of CO on the Pt surface. The experimental results revealed that the serious interference for monitoring SO2 (CO) in the presence of CO (SO2 ) was expected for an individual amperometric gas sensor. The limiting current for the cathodic reduction of oxygen on Pt was found when the potential was in the range of −0.4 to 0.1 V (versus Pt) (dotted lines in Fig. 4). 4.1.2. Response time When −0.1 V (versus Pt) was applied to Au working electrode to make the cathodic reduction of NO2 in the mass-transfer region, the response current against the time was obtained by changing the concentration between 0 and 100 ppm, as shown

Fig. 5. Response curves for sensing NO2 and NO on Au electrode. Experimental conditions are the same as those given in Fig. 3. Potential for NO2 = −0.1 V (vs. Pt); potential for NO = 0.3 V (vs. Pt).

169

Table 1 The response and recovery times of various sensing targets Sensing target

t90 (min)

t10 (min)

NO2 NO SO2 CO O2

13.0 10.0 12.0 7.0 12.0

<1.0 2.0 1.0 1.0 1.0

Working electrode: Au (for NO2 and NO), Pt (SO2 , CO and O2 ); counter electrode: Pt; reference electrode: Pt; electrolyte: 25.0 ␮m Nafion® ; area of working electrode = 19.14 mm2 ; T = 28 ± 1 ◦ C; 100% RH (90% RH for SO2 ); gas flow rate = 200 ml min−1 ; potentials for NO2 , NO, SO2 , CO and O2 = −0.1, 0.3, 0.4, 0.3 and −0.4 V (vs. Pt), respectively.

in Fig. 5. The reproducibility was quiet good when the concentration of NO2 was switched between 0 and 100 ppm. The response time (t90 ) was defined to be the time for reaching the 90% steady response current (100 ppm NO2 ) by switching the gas inflow from N2 to 100 ppm NO2 . The 10% steady response current to 100 ppm NO2 was defined to be the recovery time (t10 ) when the concentration of NO2 in the gas inlet was switched from 100 to 0 ppm. The response and recovery times were found to be 13.0 and 1.0 min, respectively (Table 1). A good reproducibility of the response curve for sensing NO on Au by switching the concentration of NO between 0 and 100 ppm was found in the dotted line of Fig. 5. When the gas flow rate and the RH were set at 200 ml min−1 and 100%, respectively, the response and recovery times for sensing NO on Au with an applied potential of 0.3 V (versus Pt) was found to be 10.0 and 2.0 min (Table 1), respectively. When Pt was used as a sensing electrode, the sensing curves of SO2 , CO and O2 also exhibited good reproducible responses, as shown in Fig. 6. The steady sensing currents of SO2 , CO and O2 were obtained from the sensing curves to be 72.3, 73.3 and −43.9 nA, respectively. The minimum response time was found to be 7.0 min for analyzing CO, and the response times to SO2 and O2 were found to be both 12.0 min (Table 1). The recovery times by changing the concentrations of the sensing targets from 100 to 0 ppm found from Fig. 6 were all 1.0 min for SO2 , CO and O2 . Because the Nafion® film was a dense polymer, the mechanisms for transportation of sensing components from the bulk phase to the electrode surface included the dissolution into the polymer phase and the diffusion through the polymer phase to the interface of the electrode and the polymer. Hence, the relatively longer response time was obtained in the sensing system. On the other hand, the sensing component presented in the interface of the electrode and polymer phases was immediately consumed by the reaction on the working electrode due to the lower mass-transfer rate, and the sensing current decreased dramatically when the gas inlet was switched to the background gas (N2 ). Hence, the recovery time was much less than the response time. 4.1.3. Effect of gas flow rate on sensitivity When the sensing potentials for various components were set in the limiting current ranges (obtained in Figs. 3 and 4), the electrochemical reactions of the components on the sensing

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Fig. 6. Response curves for sensing SO2 , CO and O2 on Pt electrode. Experimental conditions are the same as those given in Fig. 4 except for 90% RH for sensing SO2 . Potentials for SO2 , CO and O2 = 0.4, 0.3 and −0.4 V (vs. Pt).

electrodes were controlled by mass transfer. Two mass-transfer layers would be observed for transferring the sensing targets from the bulk phase to the electrode surface, i.e. the gas diffusion layer between the bulk phase and the surface of the Nafion® film and within the Naion® film on the top of the sensing electrodes. With increasing the gas flow rate the gas diffusion layer decreased, resulting in a decrease in mass-transfer resistance in the gas diffusion layer and an increase in sensing current. With further increasing the gas flow rate the mass-transfer resistance in the gas diffusion layer would be eventually neglected compared with the mass-transfer resistance within the Nafion® film [3]. For sensing NO2 with Au as a working electrode, when the applied potential was set at −0.1 V, the sensing current was controlled by mass transfer of NO2 from the gas bulk phase to the anodic surface (Au) (Fig. 3). The sensitivities of NO2 gas sensors for various gas flow rates were calculated from the linear relationships of the sensing current and the concentration of

Fig. 7. Effect of the concentration of NO2 on the sensing current. Experimental conditions are the same as those given in Fig. 3 except for the gas flow rate in ml min−1 : () 50; () 100; () 150; () 200; (䊉) 300; () 400.

NO2 illustrated in Fig. 7. The sensitivity increased from 0.185 to 0.612 nA ppm−1 with an increase in gas flow rate from 50 to 300 ml min−1 due to a decrease in mass-transfer resistance in the gas diffusion layer (Table 2). Compared with the mass-transfer resistance within the Nafion® film the transport resistance in the gas diffusion layer was insignificant when the gas flow rate was greater than 300 ml min−1 . The results were demonstrated by the same sensitivity values of the NO2 gas sensor upon increasing the gas flow rate from 300 to 400 ml min−1 . Similar behaviors were found for sensing NO, SO2 , CO and O2 based on Au or Pt as the sensing electrode. The gas flow rates to neglect the mass-transfer resistance in the gas diffusion layer in the measurement of NO, SO2 , CO and O2 were obtained to be greater than 150, 200, 200 and 200 ml min−1 , respectively (Table 2). The experimental results revealed that the mass-transfer resistances for sensing the various targets in the gas diffusion layer and within the Nafion® film were different

Table 2 Effect of gas flow rate on the sensitivity of various sensing targets Gas flow rate (ml min−1 )

50 100 150 200 300 400

Sensitivity (nA ppm−1 ) NO2

NO

SO2

CO

O2

0.185 0.225 0.380 0.501 0.612 0.614

0.312 0.376 0.469 0.442 0.445 –

0.204 0.352 0.471 0.500 0.483 –

– 0.331 0.387 0.542 0.577 –

0.266 0.311 0.352 0.422 0.423 –

Experimental conditions are the same as those given in Table 1 except for the gas flow rates.

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due to varying characteristics of the components. The different solubilities and diffusivities resulted in the different permeabilities of the Nafion® film to the different components. Therefore, the different response times and flow rate dependences for various sensing targets were found on the sensing electrodes. 4.2. Monitoring gas mixture based on sensor array and BPN A five-channels sensor array consisted of two sensor elements based on Au as working electrodes and three sensor elements with Pt as working electrodes was chosen to measure the levels of the components in gas mixtures. Based on the results in Figs. 3 and 4, the sensing potentials of −0.1 and 0.3 V (versus Pt) were set on the Au electrode for principally monitoring NO2 and NO, and 0.4, −0.4 and 0.3 V (versus Pt) were chosen on Pt electrodes for principally sensing SO2 , O2 and CO, respectively. Furthermore, the total gas flow rate was adjusted to be greater than 400 ml min−1 to ensure that the effect of the gas flow rate on the sensing signals of the sensor array was insignificant, i.e. to neglect the mass-transfer resistance in the gas film (Table 2). Fifty sets of input gas mixtures with the concentrations of five components (NO2 , NO, SO2 , O2 and CO) in the range of 15.38–126.76 ppm were conducted into the gas chamber of the sensor array, and the 50 sets of the signals obtained from five sensor elements of the sensor array were recorded and normalized by the sigmoid function (Eq. (9)) to transfer the signals into the range of 0–1. Five normalized sensing signals for every 50 sets data with various compositions were set as the five inputs of the input layer in BPN architecture (Fig. 2). The random and small values of the thresholds (Tj ) and weights (wij ) were initially chosen to training the BPN neural network by the 50 sets of the normalized signals. The weights and thresholds were adjusted in the BPN algorithm to meet the convergence criteria for minimizing the learning errors. The parameters of learning rate (η), learning cycles, number of hidden layers and nodes in the hidden layer were changed to obtain the optimal BPN. 4.2.1. Optimization of BPN structure When the learning cycles and learning rate were set at 1000 and 0.01 with five input and output nodes, the learning error against the nodes in one hidden layer was illustrated in the solid line of Fig. 8. The structure of BPN was expressed as (5, x, 5), in which the first and last five were the number of nodes in the input and output layers, and x was the number of nodes in the hidden layer. The minimum learning error was obtained to be 4.19% when the nodes in the hidden layer were equal to 7. The average learning error illustrated in the dotted line of Fig. 8 indicated that the minimum learning error was obtained for the learning cycles of 1000 by setting the number of nodes in the hidden layer of 7, i.e. the BPN structure of (5, 7, 5). In the same BPN structure and parameters the minimum learning error was obtained for the learning rate of 0.01, as shown in the solid line of Fig. 9. When the number of the hidden layer was increased to 2, i.e. a BPN structure of (5, x, x, 5), the minimum learning error of 4.35% was obtained for 15 nodes in each hidden layer BPN structure

Fig. 8. Effect of the number of nodes in hidden layer and learning cycles on the learning error. BPN structure: (5, x, 5), learning rate = 0.01, number of hidden layer = 1, number of learning data = 50 sets.

Fig. 9. Effect of the learning rates and the number of nodes in two hidden layers on the learning error. One hidden layer BPN structure: (5, 7, 5), two hidden layers BPN structure: (5, x, x, 5), learning cycle = 1000, number of learning data = 50 sets.

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Table 3 Selective data from 50 sets of learning data No.

1 2 3 4

Concentrations of gas components (ppm)

Response current (nA)

O2

NO

CO

SO2

NO2

S1

S2

S3

S4

S5

69.0 53.3 34.0 104.8

80.5 80.0 74.8 104.8

23.0 53.3 102.0 104.8

57.5 26.7 74.3 104.8

80.5 120.0 34.0 104.8

−24.5 −13.5 −9.5 −44.2

20.6 11.2 11.6 27.9

23.7 27.3 11.6 65.2

33.2 22.1 36.3 71.9

−30.1 −40.3 −14.7 −47.7

Counter electrode: Pt; reference electrode: Pt; electrolyte: 25.0 ␮m Nafion® ; area of working electrode = 19.14 mm2 ; T = 28 ± 1 ◦ C, 100% RH. Working electrode/applied potential—S1 : Pt/−0.4 V (vs. Pt); S2 : Au/0.3 V (vs. Pt); Pt/0.3 V (vs. Pt); Pt/0.3 V (vs. Pt); Au/−0.1 V (vs. Pt).

(dotted line in Fig. 9). However, the learning error with the BPN structure of (5, 15, 15, 5) was still greater than the structure of (5, 7, 5) (single hidden layer), and the calculation time based on the BPN structure of (5, 15, 15, 5) was also greater than that of (5, 7, 5). Therefore, the optimal BPN network structure was chosen as (5, 7, 5), and the learning rate and cycles were obtained to be 0.01 and 1000, respectively. 4.2.2. Identifying the concentration of components in gas mixture based on sensor array and optimized BPN The potential for the cathodic reduction of NO2 on Au in the range of limiting current was seriously overlaid with the limiting current potentials for the anodic oxidation of NO on the same electrode material (Fig. 3). It could be deduced that the response signals of NO and NO2 would be seriously interfered each other. For sensor element 2 (response current S2 in Table 3) set at 0.3 V (versus Pt) for monitoring NO in the gas mixtures, the response currents to NO of almost the same concentrations (i.e. 80.5 and 80.0 ppm) were significantly different (i.e. 20.6 and 11.2 nA) when the concentration of NO2 changed from 80.5 to 120 ppm as illustrated in Nos. 1 and 2 of Table 3. Obviously the decrease in the response current on Au to NO with the same concentration of NO was caused by the interference of NO2 with an increase in the concentration. Similarly the limiting current potentials of CO and SO2 on Pt electrode were almost in the same ranges (Fig. 4). Hence, the sensing current to CO (S3 in Table 3) was seriously interfered by the presence of SO2 . The response current to CO significantly increased from 36.3 to 65.2 nA with a slight change in the concentration of CO from 102.0 to 104.8 ppm, while the concentration of SO2 was increased from 74.3 to 104.8 ppm. The significant increase in the response current was mainly contributed by an increase in the concentration of SO2 . The serious interference for an individual sensing element to measure a single component in gas mixtures would cause an error signal in the sensing process. Using BPN trained by 50 sets of sensing data, a set of training data was chosen to compare them with the sensing results predicted in terms of BPN shown in Table 4. The maximum and minimum predicting errors were obtained to be 9.26 and 2.15% for monitoring SO2 and CO, and the average predicting error was obtained to be 5.64%, which was close to the learning error (4.19%) discussed in the above. After the sensor array was operated in other sensing experiments for 10 days, the true and predicting concentrations of the components other than the 50 sets of learning data were shown in Table 5. The predicting

Table 4 Predicting error of gas mixture in the learning data bank Component

True concentration (ppm)

Predicting concentration (ppm)

Error (%)

NO2 SO2 NO CO O2

135.0 135.0 135.0 135.0 135.0

140.6 122.5 124.5 132.1 141.7

4.15 9.26 7.70 2.15 4.96

BPN structure: (5, 7, 5) (learning rate = 0.01, learning cycle = 1000, 50 sets of learning data). Other experimental conditions are the same as those given in Table 3. Table 5 Predicting error of unknown gas mixture other than the learning data bank Component

True concentration (ppm)

Predicting concentration (ppm)

Error (%)

NO2 SO2 NO CO O2

74.8 92.6 41.1 58.8 41.1

72.8 85.3 45.2 54.6 45.6

7.18 7.88 9.98 7.14 10.94

Experimental conditions are the same as those given in Table 4.

errors were located in the range of 7.14–10.94%, and the average predicting error was 8.61%. Compared with the predicting results for data located in the 50 sets learning data (Table 4), the slight increase in the average predicting error might be caused by the decay in the electroactivities of the sensor elements. Table 6 Effect of the number of learning data on the learning and predicting errors Learning data set

Average learning error (%)

Average predicting error (%)

5 10 15 20 25 30 35 40 45 50

11.35 12.37 9.45 9.37 8.33 8.83 7.46 5.79 4.83 4.19

23.50 19.66 18.37 17.54 12.79 12.54 10.31 8.96 8.93 8.61

Experimental conditions are the same as those given in Table 4 except for the learning data set.

J.-S. Do, P.-J. Chen / Sensors and Actuators B 122 (2007) 165–173

Changing the data sets for training BPN, the average learning and predicting errors are illustrated in Table 6. The learning and predicting errors decreased from 11.35 and 23.5% to 4.19 and 8.61%, respectively, by increasing the learning data from 5 to 50 sets. These results indicated that increasing the learning data was a valid method to decrease the learning and predicting errors. 5. Conclusions Using Au as a sensing electrode, the potentials of the limiting currents for cathodic reduction of NO2 and anodic oxidation of NO were obtained to be in the ranges of 0.2 to −0.4 V (versus Pt) and 0.0–0.5 V (versus Pt), respectively. The potentials located in the limiting current range for anodic oxidation of SO2 and CO, and cathdodic reduction of O2 on the Pt electrode were found to be in the ranges of 0.2–0.5 V (versus Pt), 0.2–0.7 V (versus Pt), and 0.1 to −0.4 V (versus Pt), respectively. When the potentials for sensing NO2 , NO, SO2 , CO and O2 were set at the values of mass-transfer control, i.e. −0.1, 0.3, 0.4, 0.3 and −0.4 V (versus Pt), the response times obtained from the response curves were 13, 10, 12, 7 and 12 min, respectively. The recovery times were significantly less than the response times. The mass-transfer resistance in the gas diffusion layer for monitoring the gas mixture on the sensor array could be neglected when the gas flow rate was greater than 300 ml min−1 . Using the sensor array with five sensing elements consisted of two Au and three Pt working electrodes, the optimal BPN structure was obtained to be five input nodes, five output nodes, and seven nodes in one hidden layer. The minimum average learning error was found to be 4.19% based on 50 sets of learning data with a learning rate and cycle of 0.01 and 1000, respectively. If a gas mixture was one of the 50 learning data, the average predicting error based on the optimal BPN structure was found to be 5.64%. The slight increase in the average predicting error for gas mixtures other than the learning data to 8.61% might be due to de-activation of the sensing electrodes. Both of the learning and predicting errors decreased with an increase in learning data sets. Acknowledgments The financial support of Ministry of Education of Republic of China (Project number: EX-91-E-FA09-5-4) and Tunghai University is acknowledged. References [1] E. Bakker, M. Telting-Diaz, Electrochemical sensors, Anal. Chem. 74 (2002) 2781–2800. [2] J.S. Do, W.B. Chang, Amperometric nitrogen dioxide gas sensor based on PAn/Au/Nafion® prepared by constant current and cyclic voltammetry methods, Sens. Actuators B 101 (2004) 97–106. [3] J.S. Do, K.J. Wu, M.L. Tsai, Amperometric NO gas sensor in the presence of diffusion barrier: selectivity, mass transfer of NO and effect of temperature, Sens. Actuators B 86 (2002) 98–105. [4] S.I. Somov, G. Reinhardt, U. Guth, W. G¨opel, Multi-electrode zirconia electrolyte amperometric sensors, Solid State Ionics 136–137 (2000) 543–547.

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Biographies Jing-Shan Do received BS, MS and PhD degrees in Chemical Engineering from National Cheng Kung University, Tainan, Taiwan, in 1982, 1984 and 1990, respectively. From 1988 to 1990 he was a teaching assistant at the same institution. In 1990, he became an associate professor and in 1995 a professor at the Department of Chemical Engineering, Tunghai University, Taiwan. His current research interests are focused on the electrochemical gas and bio-sensors, anodic materials of lithium batteries, and cathodic materials of fuel cells. Po-Jen Chen received his BS and MS degrees in Chemical Engineering from Tunghai University, Taichung, Taiwan, in 1996 and 1998, respectively.