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Thin Solid Films 500 (

On-line monitoring of CO2 quality using doped WO3 thin film sensors

M. Stankova a, X. Vilanova a, E. Llobet a,*, J. Calderer b, M. Vinaixa a, I. Gracia c,

C. Cane c, X. Correig a

a Departament d’Enginyeria Electronica, Electrica i Automatica, Universitat Rovira i Virgili, Avda. Paısos Catalans, 26, 43007 Tarragona, Spainb Departament d’Enginyeria Electronica, Universitat Politecnica de Catalunya, Barcelona, Spain

c Departament de Microsistemes i Tecnologies del Silici, Centro Nacional de Microelectronica, Bellaterra, Spain

Received 18 February 2005; received in revised form 12 October 2005; accepted 9 November 2005

Abstract

Thin films of either pure or doped tungsten oxide were grown by radiofrequency (rf) sputtering onto silicon micromachined substrates. Up

to 7 different dopant materials (noble metals or metal oxides) were deposited by rf sputtering or by evaporation onto the tungsten oxide films.

The responsiveness of the resulting micromachined sensors towards sulfur dioxide and hydrogen sulfide was studied. Other pollutants in CO2

such as ethylene and methane were also tested. It was found that Au-doped tungsten oxide sensors were highly sensitive to H2S, poorly

sensitive to SO2 and almost insensitive to hydrocarbons. On the other hand, Pt-doped tungsten oxide was highly sensitive to SO2, poorly

responsive to H2S and nearly insensitive to hydrocarbons. By applying a principal component analysis (PCA), we show that it would be

possible to selectively detect traces of H2S and SO2 in a CO2 stream using doped WO3 microsensors. These sensors could be used in a low-

cost analyzer of beverage-grade CO2.

D 2005 Elsevier B.V. All rights reserved.

Keywords: Tungsten oxide; Gas sensor; Micromachined sensor arrays; Doped oxides

1. Introduction

In recent years, there has been a continuous improvement

towards quality assurance in the beverage industry. The

International Society of Beverage Technologists (ISBT) has

established quality guidelines for carbon dioxide used in beers,

mineral waters and soft drinks [1]. Carbon dioxide can be

produced from a wide variety of processes. Each process has

the potential of leaving residues that reduce the purity of CO2.

Furthermore, contaminants may be introduced from storage or

transport vessels. In the quality control of CO2, the methods

generally used are analytical techniques such as gas chroma-

tography or gas chromatography coupled to mass spectrometry

(GC/MS) [2]. However, these techniques are very expensive

and bulky. That is why, a low cost, small size analyzer to

monitor on-line the quality of CO2 would be of great interest,

especially for breweries or soft drink companies, where

acquiring a GC/MS system is out of question. On this basis,

0040-6090/$ - see front matter D 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.tsf.2005.11.021

* Corresponding author.

E-mail addresses: mariana.stefanova@urv.net (M. Stankova),

eduard.llobet@urv.net (E. Llobet).

equipment based on an array of metal oxide gas sensors would

be a good solution that could be installed in any beverage

production plant.

It is well known that atmospheric oxygen plays an important

role in gas detection by metal oxide gas sensors [3,4]. Kohl [5]

reported some surface processes for the detection of reducing

gases, which did not require the presence of atmospheric

oxygen. In the last years, several groups have investigated the

sensing mechanism of metal oxides operated under low oxygen

concentrations [6] and in streams of inert gases such as argon

[7]. In 2003, Vilanova et al. [8,9] developed a multisensor

system (tin oxide sensors doped with noble metals) to detect

methane, ethylene and sulfur dioxide in a CO2 stream.

However, these sensors (based on SnO2) showed low

sensitivity to sulfur compounds, especially to H2S. That is

why, a system based on WO3 sensors (pure and doped with

platinum) was developed for the detection of sulfur species in

carbon dioxide [10]. In this paper, we further study the

detection of sulfur compounds in CO2 by analyzing the effect

of 7 different dopants on tungsten oxide based sensors. The

objective is the selective detection of these compounds at a low

operating temperature (below 300 -C).

2006) 302 – 308

ww

Table 2

Typical contaminants with their maximum concentrations allowed in 99.95%

purity CO2 (after ISTB [1])

Pollutant Maximum allowed

concentration

Sulfur dioxide 2 ppm

Hydrogen sulfide 500 ppb

Other sulfur compounds 500 ppb

Nitrogen dioxide 2.5 ppm

Nitric oxide 2.5 ppm

Ammonia 2 ppm

Nitrogen 40 ppm

Carbon monoxide 2 ppm

Benzene 20 ppb

Methane 30 ppm

Heavy hydrocarbons 1 ppm

Volatile hydrocarbons 20 ppm

Total aldehydes 200 ppb

Oxygen 9 ppm

Water 8 ppm

M. Stankova et al. / Thin Solid Films 500 (2006) 302–308 303

2. Experimental details

2.1. Sensor fabrication

Four element microhotplate sensor arrays were fabricated.

Details on the design and fabrication can be found in a previous

article [10]. The active layer of WO3 was radiofrequency (rf)

sputtered and patterned by lift-off. The deposition was made

using a target of W (99.95% of purity) that was fixed at 70 mm

from the substrate. The substrate was kept at room temperature

during the process. The sputtering atmosphere consisted of 1:1

Ar/O2. The forward input power was maintained at 200 W with

zero reflected power. The pressure in the chamber during

deposition was 0.5 Pa. After the deposition of tungsten

trioxide, the active layer was doped. As doping materials, the

following compounds were used: Pt, Au, Ag, Ti, SnO2, ZnO

and ITO (indium tin oxide). Most of them (Pt, Au, SnO2, ZnO

and ITO) were deposited by rf magnetron sputtering. The

conditions for these processes are summarized in Table 1. For

the deposition of Ag and Ti, an e-gun evaporation was

employed. In this last case, the target was placed 17 cm away

from the sample. The thickness of each dopant was below 10

nm (layer thickness was estimated by a 4-probe resistance

measurement system). As a final step, the active layer was

annealed in dry air for 2 h at 400 -C.

2.2. Structural analysis

The morphology of platinum-doped WO3 active layers was

studied by transmission electron microscopy (TEM). The aim of

this analysiswas to study the surface distribution of dopants and to

estimate the thickness of the layer. For this purpose, a film of Pt-

doped WO3 was deposited directly on a Si wafer. A Si wafer was

used instead of a micromachined sensor substrate because the

latter would have broken during the measurement process. The

film was annealed at 400 -C in air for 2 h. Two samples were

prepared for TEM analysis: one of them was cross-sectional and

the other was planar. Using the first one, we could estimate the

thickness of the layer, while in the second, the surface distribution

of the dopingmaterial could be observed.Micrographswere taken

using a Hitachi H-800-MT microscope equipped with a Gatan

Multiscan camera, working at 200 kV. The same equipment was

applied to make Selected Area Electron Diffraction (SAED).

The WO3 phase was determined also by using X-ray

diffraction (XRD). XRD measurements were made using a

Siemens D5000 diffractometer (Bragg–Brentano parafocusing

Table 1

Deposition conditions used to sputter some of the dopant materials

Dopant Target Ar/O2 (sccm) Power (W) Time

Pt Pt (99.99%) 14.6/0 50 10 s

Au Au (99.99%) 14.6/0 100 30 s

SnO2 Sn (99.95%) 7/7 100 2 min 30 s

ZnO Zn (99.99%) 7/7 100 10 min

ITO ITO (99.99%) 14/0 50 4 min

The sputtering pressure was 5 Pa for all the processes. sccm – standard cubic

centimetre per minute.

geometry and vertical h–h goniometer) fitted with a grazing

incidence (x =0.52-) attachment for thin film analysis and

scintillation counter as a detector. The angular 2h diffraction

range was between 21.0- and 70.0-. The data were collected

with an angular step of 0.05- at 3 s per step and sample

rotation. CuKa radiation was obtained from a copper X-ray tube

operated at 40 kV and 30 mA.

2.3. Gas sensitivity measurements

The sensors were introduced in a temperature controlled,

16-ml chamber. First, pure CO2 (90 ml/min) flowed thought the

chamber and the sensor baseline was established. Then the

stream was switched (by the means of electrovalves) to CO2

with a given contaminant (from calibrated bottles). During all

the measurements, the sensor resistance was acquired and

stored for further processing. Four contaminants were tested,

namely, 1 ppm SO2, 100 ppb H2S, 30 ppm CH4 and 20 ppm

C2H4. These concentrations were chosen based on the

maximum allowed in beverage-grade CO2 according to ISBT

(see Table 2). The sensors were operated at 110, 200 and 260

-C. Each pollutant was measured at the different operating

temperatures. Every measurement was replicated ten times.

3. Results and discussion

3.1. Active layer characterization

First, the cross-sectional sample was studied (Fig. 1(a)). From

this image, the thickness of the Pt film was estimated to be 3–4

nm. This thickness was less than the expected value (about 10

nm). The expected value was extrapolated based on previous

depositions on Si wafers and four-point measurements of layer

resistance. It is well known that growth rate is not a linear

function of time. Our deposition process took 10 s and, in such a

short interval, it is very difficult to control the growth process.

We assume that this is the reason to obtain a layer thinner than

expected. However, we were interested in obtaining a very thin

Fig. 2. Mean diameter for the Pt grains from Fig. 1(a).

Fig. 3. Diffraction pattern for WO3 doped with Pt.

Fig. 1. (a) Cross-section and (b) planar TEM images of the Pt/WO3/Si sample.

M. Stankova et al. / Thin Solid Films 500 (2006) 302–308304

layer, because the deposition of a superficial and porous Pt film

onto tungsten trioxide was sought (to allow gases to react with

the tungsten oxide underneath). From Fig. 1(a), it is obvious that

the desired catalytic layer was obtained. Moreover, if the

resulting Pt film were not so thin, then adhesion problems

would have appeared, since platinum is prone to suffer from bad

adhesion. A very thin Pt layer is appropriate for catalyzing

reactions at the surface of the metal oxide film.

In the second step, the planar sample was analyzed (Fig.

1(b)). The aim, in this case, was to study the distribution of

platinum on the active layer surface. In this figure, the Pt grains

can be observed. A statistical study on grain size distribution was

conducted. To do so, the manual measurement of 100 grains

within each TEM micrograph was performed using an appro-

priate program tool from Digital Micrograph (Gatan Inc.). An

automatic particle analysis using standard image analysis

software was not considered here because the edges of some

particles were not sharply defined, which resulted in over-

estimating grain size. The grain size measurements performed,

i.e. 100 measurements in total, underwent a standard statistical

analysis. Considering that d is the mean diameter of the grains,

S.D. is the standard deviation and S.E.M. is the standard error of

the mean, the values obtained from the statistical analysis were

as follows: mean diameter d =2.63625 nm; S.D.=0.38718 and

S.E.M.=0.03872. Its can be seen that the mean diameter of

grains is very close to the layer thickness. The grain size

distribution is shown in Fig. 2.

SAED gave us a diffraction pattern formed by the elastic

scattering of an electron beam by the atoms in the specimen.

We applied this technique to obtain information about the

crystalline phase of the films. From the diffraction patterns

and by identifying the spots and rings, two different states can

be distinguished (see Fig. 3). The first one corresponds to

monoclinic WO3 and the second-to cubic Pt. In Table 3, the

spacing distances obtained from the diffraction patterns (the

spots marked in Fig. 3) and the ones found in the databases

(Joint Committee on Power Diffraction Standards: XRD

tables of the International Center for Diffraction Data) [11]

are compared. We used the database for triclinic and

monoclinic WO3, platinum and platinum tungsten (Pt2W).

The last one was used because the possibility of this

compound being present was not discarded. A good

accordance between the data for monoclinic (Pc) tungsten

oxide and cubic platinum was obtained. It is well known that

tungsten trioxide experiments several phase transitions with

temperature [12,13]. It has been reported that WO3 is

tetragonal above 720 -C, orthorhombic from 320 to 720 -C,monoclinic from 17 to 320 -C, and triclinic below 17 -C. Acoexistence of triclinic and monoclinic phase is also possible.

For example, the spot labeled 4 could belong to the triclinic

or monoclinic phase. The experimental d-spacing value (see

Table 3) is close to the value for the triclinic or the

monoclinic phase (1.8222 and 1.8195, respectively).

Fig. 4 shows half rings at the positions where the

diffraction rings were identified by the software ProcessDif-

Table 3

Comparison of experimentally obtained d-spacing with those collected by

International Centre for Diffraction Data (ICDD) (h k l are the Miller indexes)

Spot no. d-spacing

experimental (A)

d-spacing

ICDD (A)

h k l Phase

1 3.665 3.6892 1 1 0 WO3 Monoclinic

2 3.052 3.0595 1 0 2 WO3 Monoclinic

3 2.304 2.27 1 1 1 Pt Cubic

4 1.823 1.8195 1 0 4 WO3 Monoclinic

5 1.512 1.5116 3 1 2 WO3 Monoclinic

1.5116 �2 2 3

6 1.398 1.39 2 2 0 Pt Cubic

7 1.205 1.18 3 1 1 Pt Cubic

The spots listed correspond to the ones marked on Fig. 3.

Fig. 5. XRD spectrum for WO3. The diffractograms for monoclinic (Pc) and

triclinic (P-1) are shown for comparison.

Table 4

Sensor response of the different sensors to the gases studied

Active layer T (-C) 100 ppb

H2S

1 ppm

SO2

30 ppm

CH4

20 ppm

C2H4

WO3 110 3.06 1.29 1.11 1

200 3.08 1.38 1.11 1.19

260 3.11 1.37 1.18 1.16

WO3/Pt 110 1.20 1.64 1 1.12

200 1.79 7.71 1 1.13

260 1.89 5.23 1 1.19

WO3/Au 110 4.10 1.33 1 1

200 11.14 1.50 1 1

260 13.11 1.28 1 1

WO3/Ag 110 3.75 1.05 1 1

M. Stankova et al. / Thin Solid Films 500 (2006) 302–308 305

fraction [14]. Only diffraction rings with sharp definition are

identified and analyzed. The presence of well defined spots

and spots which forms arcs is due to the fact that there were

two different compounds (monoclinic WO3 and cubic Pt). For

some of the spots, two different planes were possible (see in

Table 3, for example, the data for spot number 5). From the

SAED analysis it can be concluded that the active layer

consists of WO3 and a superficial doping layer, which

confirms the expected result.

Because of the possible presence of two phases for WO3, an

XRD was performed. The result of this analysis is shown in Fig.

5. The peaks labeled on this figure correspond to monoclinic

tungsten oxide. However, some of the picks characteristic of the

monoclinic phase are absent and other peaks, which are

characteristic of triclinic WO3, are present. This allows us to

conclude that the main phase of the tungsten oxide is the

monoclinic one, but some triclinic tungsten oxide coexists.

3.2. Sensor response

The possibility to detect pollution by sulfur compounds

(SO2 and H2S) below the maximum permissible concentrations

for beverage-grade CO2 using the different sensors fabricated

was investigated. The possible interference caused by other

pollutants such as hydrocarbons, which are permitted at higher

concentrations in CO2, was also studied. The results obtained

for carbon dioxide polluted by hydrocarbons showed that most

of the tungsten oxide based sensors were almost insensitive to

Fig. 4. Measured SEAD with detected circles.

the presence of hydrocarbons (see Table 4). On the other hand,

the sensors were very sensitive to sulfur compounds. Fig. 6

presents some typical responses to the gases studied.

200 5.82 1.07 1 1

260 6.92 1.10 1 1

WO3/Ti 110 1.38 1.77 1.26 1.10

200 1.48 1.73 1 1.26

260 1.63 1.64 1 1.33

WO3/SnO2 110 1.10 1.29 1 1

200 1.13 1.26 1 1

260 1.21 1.25 1 1

WO3/ZnO 110 1.25 1 1 2.27

200 1.05 1 1 1.03

260 1.02 1.60 1 1.02

WO3/ITO 110 1 1 1 1.15

200 1.05 1 1 1.17

260 1.07 1.19 1 1.05

For reducing gases (H2S, CH4 and C2H4), the sensor response was defined as

R0/Rg and for oxidizing (SO2) as Rg/R0. In this way, the sensor response is

always higher or equal to unity. For each type of sensor, the higher responses to

gases appear in bold.

Fig. 6. Some typical responses to the gases studied. (A) Response of an Au-

doped WO3 sensor operated at 200 -C to 100 ppb of H2S; (B) response of a

ZnO-doped sensor operated at 110 -C to 20 ppm of C2H4.

M. Stankova et al. / Thin Solid Films 500 (2006) 302–308306

Table 4 shows the values of sensor response, which was

defined as R0/Rg (for H2S, CH4 and C2H4) and Rg/R0 (for SO2),

where R0 is the baseline resistance in pure CO2 and Rg is the

sensor resistance in the presence of the polluted carbon dioxide.

It has been shown [15] that when WO3 is exposed to SO2, an

oxidizing gas for this temperature range, the electrical resistance

of the sensor increases. This is why the sensor response was

defined differently for this gas. The ratio between the standard

deviation of the response and its mean (over 10 replicate

measurements) was below 1.59�10�2 for each gas and

operating temperature. This shows the good repeatability that

was observed. These results demonstrate that the sensors studied

make possible the detection of sulfur compounds in a CO2

stream and identify them even in the presence of hydrocarbons at

higher concentrations. The sensors were insensitive to methane.

Tin oxide-based sensors should be used to detect this gas [9]. In

the detection of ethylene, sensor responses were very low,

exception made of the ZnO-doped layers operated at 110 -C (R0/

Rg=2.27). These sensors could be used for sensing C2H4,

although, again, it is advisable to use tin oxide sensors.

All the sensors were sensitive to H2S and SO2. However,

important differences in responsiveness were found among

them. In the case of hydrogen sulfide, the responsiveness was

the highest for sensors doped with Au. Sensor resistance

changed up to 13 times when these devices were operated at

260 -C. For Au-doped tungsten oxide, sensor response

increased noticeably when the operating temperature was

increased. This highest increase in responsiveness (3 times) is

obtained when the temperature is increased from 110 -C to 200

-C. Responsiveness was also very good for sensors doped with

Ag (see Table 4). In this case, the maximum value was about 7,

and this is roughly two times less than for WO3/Au films.

Again, sensor response increased with working temperature.

Pure tungsten oxide layers also presented a very good response.

It was three times less than that for WO3/Au and two times less

than for WO3/Ag, which shows the positive role of doping

materials. Just as before, pure tungsten oxide sensors increased

their response when their operating temperature was increased.

The responsiveness of sensors doped with Pt, Ti and SnO2 can

be considered as moderate. Only ZnO-doped sensors showed a

different behavior because their responsiveness decreased

when their working temperature was increased. The response

of ZnO-doped sensors was moderate at 110 -C and very low at

the other temperatures studied. Finally, the sensors doped with

ITO were almost insensitive to H2S. At the lowest temperature,

they did not respond at all and at higher temperatures their

response was very low (1.05–1.07).

For the detection of sulfur dioxide, the best results were

obtained for sensors doped with Pt (see Table 4). The maximum

responsiveness (7.71) was at the intermediate temperature (200

-C). Only these sensors showed a high response to this pollutant.The response of the others can be considered as moderate. If

sensors are sorted according to their responsiveness, the result is

as follows: (1) Titanium-doped films, which can be used at low

temperature, because their response at 110 -C was 1.77 (a little

higher than the one for WO3/Pt at the same operating

temperature). The response decreased slightly with operating

temperature. (2) ZnO-doped layers, whose responsiveness was

1.60 at 260 -C, but showed no response at lower temperatures.

(3) Au-doped sensors showed a maximal responsiveness of 1.50

at T=200 -C. (4) Pure tungsten oxide sensors showed the

maximal responsiveness at 200 -C. However, the response at

lower and higher operating temperatures was close to the

maximum values. (5) The WO3/SnO2 devices show a respon-

siveness that decreased with operating temperature. The

maximum was 1.29 at 110 -C. (6) ITO-doped films showed

responsiveness when operated at 260 -C only. (7) Ag-doped

sensors showed very low response to SO2. Shimizu et al. [15]

had reported that silver improved the sensitivity to SO2. A

possible explanation for this different result is as follows: 1. The

concentration of the doping material is different. 2. While the

paper by Shimidzu reported the detection of SO2 in air, here the

balance gas was CO2. 3. Finally, the sensor operating tempera-

tures were different (from 110 to 260 -C here, and from 300 to

600 -C in Shimizu et al. [15]).

3.3. Selectivity

As it has been shown, the sensors can detect sulfur

compounds in the presence of hydrocarbons. They can also

selectively detect H2S and SO2 (a reducing and an oxidizing

gas, respectively). The dependence of sensor response on the

operating temperature is shown in Fig. 7. By comparing Figs.

7(A) and (B), it can be derived that the combination of Pt

doped sensors with some of the WO3/Au, WO3/Ag or pure

WO3 leads to the selective detection of the two sulfur

compounds considered. For example, if an Au-doped film

was chosen as active layer to detect H2S, there will be no cross

sensitivity to SO2, because the response to the latter is very

low. The situation is similar for Ag-doped films, which can also

be applied to H2S sensing. Pt-doped layers are the best for

detecting SO2. What is more, their response to H2S is low, so

the selectivity is quite high. The best working temperature for

this case is 200 -C (see Fig. 7(B)), although at 260 -C the

response was also high. All this leads to the conclusion that if

an array of sensors with different doping materials were

Fig. 7. Dependence of the sensor response on the operating temperature for the

doping materials studied: (A) for 100 ppb H2S; (B) for 1 ppm SO2.

Fig. 8. Results from the PCA analysis performed on sensor response (biplot of

the scores and loadings). The scores are the projection of measurements in the

orthogonal base of PCs and the loadings are the contribution of each sensor to

the PCs.

M. Stankova et al. / Thin Solid Films 500 (2006) 302–308 307

fabricated, it would be possible to selectively detect sulfur

compounds in a CO2 stream. What is more, if a combination of

doped WO3 and SnO2 sensors were considered, then it would

be possible to selectively detect not only H2S and SO2 but also

traces of hydrocarbons such as ethylene and methane in CO2.

3.4. Gas analysis by principal component analysis (PCA)

The gases studied were qualitatively analyzed using PCA.

PCA is a linear, unsupervised pattern recognition method. PCA

involves a mathematical procedure that transforms a number of

correlated variables into a (smaller) number of uncorrelated

variables called principal components. The first two principal

components (PCs) are often employed, since they capture the

most data variance, and therefore theoretically best describe the

data. The most frequent application of PCA is in cases where the

sensor response matrix, R, is expected to contain variables with

some degree of collinearity. This collinearity means that R will

have some dominating types of variability that carry most of the

available information. The objective of PCA is to express the

information in the variables ofR ={rk, k =1, 2, . . .,K} – K is the

number of columns in the response matrix – by a lower number

of variables P={ p1, p2, . . ., pn} (n <K) called principal

components. The PCs are chosen to contain the maximum data

variance and to be orthogonal. The response matrix is decom-

posed into a product of two matrices (scores and loadings).

While the loadings matrix, L, contains the contribution of the

original response vectors to the new response vectors or PCs, the

scores matrix contains the response vectors projected onto the

space defined by the PCs. Further details on this method can be

found elsewhere [16]. Prior to perform the principal component

analysis, the response matrix was autoscaled. The PCA was

performed using the responses of all sensors measured. The

response matrix was formed from the conductance change and

had 24 columns (8 sensors with different active layers�3

operating temperatures) and 80 rows, which corresponded to

the number of measurements (4 gases�10 replicates per

measurement�2 sensors of each type).

Scores and loadings biplots provide a useful tool of data

analysis and allow the visual appraisal of the structure of large

data matrices. It is specially revealing in PCA, where the biplot

can show inter-unit distances and indicate clustering of units as

well as display variances and correlation of the variables [17].

The relationship between the objects (i.e. measurements) and

the variables (i.e. sensors) are often best displayed on a biplot.

This is a plot with axes scaled as to include the scores-scores

coordinates as well as the loading values. A vector drawn from

the origin to a set of loadings coordinates represents the size of

the loadings on the respective PCs, and, in addition, indicates

the objects with which it is particularly associated [18]. If we

draw the graph of sensor loadings and the biplot between this

M. Stankova et al. / Thin Solid Films 500 (2006) 302–308308

graph and the scores plot (Fig. 8), a combination of sensors

with different active layers can be proposed to detect sulfur

compounds. The different gases cluster together in the scores

plot and there is a very good separation between them. Since

the sensors were almost insensitive to methane (see Fig. 8), this

component appears nearly at the (0,0) point of the plot. For

ethylene, the results show a higher dispersion because only

some of the sensors responded to this species and their

responses were poor. The loadings present the distribution of

the different sensors in relation to the different operating

temperature. That is why some dispersion for sensors of the

same type can be observed. To detect H2S the best solution is to

use sensors doped with Ag and Au. The devices doped with

SnO2 are selective to SO2. Sensors doped with ITO and the

pure tungsten oxide ones are selective to ethylene.

4. Conclusions

Microhotplate sensor arrays based on pure and doped

tungsten oxide thin films were fabricated. A morphological

study of the Pt doped layer was made using TEM. The

thickness of the doping layer was found to be about 3–4 nm.

The dopant material was distributed on the surface and no

diffusion into the WO3 was detected. By SAED and XRD, the

phase of the compounds was determined: WO3 was present in

monoclinic phase and the Pt in cubic phase.

The ability of the sensors based on pure and doped WO3 to

detect the presence of sulfur compounds as pollutants in CO2

has been investigated. The sensors showed high and reversible

responses to the presence of H2S and SO2 diluted in CO2, in

absence of oxygen (O2 concentration below 15 ppm). The

sensors doped with Au and Ag responded very well to H2S

(100 ppb in CO2) and poorly to SO2 (1 ppm in CO2), while the

sensors doped with Pt showed an opposite behavior. Besides,

H2S behaves as a reducing gas (decreasing sensor resistance),

while SO2 behaves as an oxidizing species (increasing sensor

resistance). The responsiveness for H2S and SO2 were highest

when the sensors were operated at 260 -C and 200 -C,respectively. Taking into account these results, an array

combining active layers of WO3/Au and WO3/Pt is a promising

system for detecting the pollution of CO2 by sulfur compounds.

As it was confirmed by PCA, the detection of sulfur

compounds was not influenced by the presence of hydro-

carbons such as methane and ethylene. However, the interfer-

ence from other gases such as ammonia or nitrogen oxides

(with maximum allowed concentration in CO2 of 2 and 2.5

ppm, respectively [1]), has to be analyzed prior to ensure that

these materials are the best for this application.

Acknowledgements

This work has been funded by the Spanish Commission for

Science and Technology (CICYT) under grant no. TIC2003-

06301.

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