Gas identification by quantitative analysis of conductivity-vs-concentration dependence for SnO2 sensors

Sensors and Actuators B 137 (2009) 456–461

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Sensors and Actuators B: Chemical journal homepage: www.elsevier.com/locate/snb

Gas identification by quantitative analysis of conductivity-vs-concentration dependence for SnO2 sensors V. Simakov a , A. Voroshilov b , A. Grebennikov b , N. Kucherenko b , O. Yakusheva a , V. Kisin b,∗ a b

Saratov State Technical University, 410054 Saratov, Polytechnicheskaya str. 77, Russia Saratov State University, 410012 Saratov, Astrakhanskaya str. 83, Russia

a r t i c l e

i n f o

Article history: Received 14 July 2008 Received in revised form 29 October 2008 Accepted 3 January 2009 Available online 19 January 2009 Keywords: Gas sensor Tin oxide Conductivity Gas identification

a b s t r a c t Dependence of the conductivity of a thin-film gas sensor on the concentration of the ethanol or acetone vapors in air has been studied. We found that power law describes this dependence only in a part of the whole vapor concentration range. Theoretical model of the gas response of thin-film gas sensor has been proposed. The basic model parameters, such as desorption heat of the gas particles from the sensor surface or the separation between the energy levels induced by the gas species and levels induced by the oxygen particles in the energy gap of the sensor film material, can be used as identifying agents. The identification ability of a single-gas sensor based on SnO2 thin film is demonstrated by processing the concentration dependence of the sensor conductivity. © 2009 Elsevier B.V. All rights reserved.

1. Introduction Thermo-stimulated sorption of the adspecies on a thin-film surface affects the thin-film resistance and is widely used in gas sensors [1]. Thin metal oxide films are promising materials for implementation of solid-state gas sensors with improved characteristics [2]. There is also an opportunity of integrating the gas sensors with subsequent signal processing structures on the same substrate which opens a new perspective for development of miniaturized gassensing devices [3]. Usually, the ratio of the change of layer conductivity in the gas mixture to the layer conductivity in air is used as an output signal from a single sensor or from each segment of the sensor array [4]. This parameter, however, depends nonlinear on both the gas type and the gas concentration, which makes it difficult to separate these two dependencies in the total change of the layer conductivity during the identification process. For reliable gas identification, therefore, we need to design and arrange a multiple-sensor system (a sensor array). As a result, a number of calibrations are required for reliable identification of the gas mixtures. In previous papers, we have emphasized that some parameters, which are characteristic of the gas-response of specific types of sensors, can be extracted during the signal preprocessing from the separate segments of the sensor array [5–7]. In particular, the exponent n of the nonlinear part of the I–V characteristic of a sin-

∗ Corresponding author. Tel.: +7 8452 262222; fax: +7 8452 262222. E-mail address: [email protected] (V. Kisin). 0925-4005/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2009.01.005

gle sensor is mostly related to the mobility of the species adsorbed by the sensor and only weakly depends on the gas concentration [5]. The above-mentioned nonlinearity of the I–V characteristics can be observed in high electric field region at temperatures about 250–350 ◦ C [6]. Specific parameter of the conductivity relaxation, detected after the stepwise exposure to the gas probe, was related to the energy distribution of the density of the electron surface states induced by the reduced gas atmosphere. That parameter was shown to depend only on the gas type and did not depend on the gas concentration. Therefore it can be used for identification of the analyzed gas probe [7]. In this work, we demonstrate the identification ability of a single sensor by processing the concentration dependence of the conductivity of SnO2 gas sensor. 2. Experimental equipment and results Fe–Cr electrodes were deposited through a mask on the alumina substrate. The gap between the electrodes was 100 ␮m. Gas-sensitive films were then grown on top of the Fe–Cr electrodes using a radio frequency planar magnetron sputtering. A mixture of 4% CuO powder with 96% SnO2 powder was used to prepare the target. The powder was pressed into a disk shape and sintered in oxygen atmosphere before being placed into the system. The distance between the substrate and the target was about 6 cm. High-purity argon and oxygen was fed into the sputtering chamber through the flow meters. The flows were adjusted to obtain the desired composition of the mixture 3:1. The total gas pressure was approximately 0.5 Pa and the discharge power was about

V. Simakov et al. / Sensors and Actuators B 137 (2009) 456–461

457

Fig. 1. Experimental setup for measurement of conductivity vs concentration dependencies.

100 W/sm2 . The substrate temperature was kept at around 300 ◦ C to avoid the crystallization of a tin oxide into the crystallographic structure other than the rutile type due to SnO disproportionation into Sn and SnO2 . The deposition rate was about 24 nm/min. The film thickness was measured by TENCOR P-10 Surface Profiler (“KLA-Tencor Co.”, USA) and was 1 ␮m. Due to low substrate temperature and high deposition rate, the growth of the film was mainly affected by the sputtering conditions. Effect of the substrate was, therefore, minimized [8]. X-ray diffraction analysis and electron micrograph showed that tin dioxide films had a single-phase SnO2 polycrystalline structure and a texture oriented in [1 1 0] direction that is perpendicular to the substrate surface. The average grain size was in the range from 40 to 100 nm. Electrical measurements were carried out by using digital multimeter Keithley-2000/20 (Keithley Instruments, Inc., USA) and power source INSTEK GW PST-3201 (Good Will Instrument Group, Taiwan). The operation voltage was 25 V. The conductance of sensor has been measured under three different conditions: (1) in synthetic air and in the mixture of synthetic air with either (2) ethanol or (3) acetone vapors. The humidity level was controlled by hygrometer VT-3535 (AN-DER Products GmbH, Austria) and was kept at the level of 50% in all the experiments. The measurement set-up is schematically shown in Fig. 1. The samples were placed into the thermostatic holder maintaining ambient temperature at the level of 400 ◦ C. The thermostatic holder with the samples was placed into the 3-l measuring chamber, where atmosphere could be deliberately changed. For measurements, the chamber could be filled with synthetic air or with the air-based mixture containing the doping vapor with concentration C varying from 10 to 100,000 ppm. The temperature of the measuring chamber was kept constant at 30 ◦ C. Some transition time was required to establish a homogenous atmosphere in the chamber. When the sensor was brought into the chamber, the sensor conductivity G was changed according to the law:

G = Gmax · (1 − e−t/ )

(1)

Here, G is the conductivity change, Gmax is its maximum value, t and  are, correspondingly, time and conductivity relaxation times. When the doping gas was put into the chamber with preinstalled sensor the sensor conductivity was changed as shown in Fig. 2. Plotting of these data in coordinates ln(1 − G/Gmax ) vs ln(t) resulted in straight line at Gmax = 0.56 1/M. The characteristic transition time for the steady-state signal  = 15 s was determined from the slope of this dependence. Therefore, to avoid the transition effects during the measurement of the concentration dependence of the sensor conductivity, the multimeter readings were taken 1 min after filling the chamber with the doping gas.

Fig. 2. Relaxation of the sensor conductivity, after the doping gas enters the chamber with preinstalled sensor.

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independently, each by its own type of non-interacting identical centers characterized by surface density NS . The adsorbed particles can be in either weak (electrically neutral) or strong (electrically charged) states. According to the theory of electronic absorption [9], the desorbed particles are usually in electrically neutral states because of the low coupling to the surface. On the grain boundaries of the polycrystalline film in atmosphere containing gas mixture of KOx gases of acceptor types (oxidant gases) and KRed gases of donor types (reduced gases), the steady-state conditions can be written as a system of KOx + KRed equations [9]:



˛i · Pi ·

NS −

KOx 



i=1

⎛ ˛j · Pj · ⎝NS −

KRed 

 W i

= i · Ni · (1 − fi ) · exp −

Ni

kT

,

i ≤ KOx

⎞

 Wj ⎠ Nj = j · Nj · (1 − fj ) · exp − , j ≤ KRed kT

j=1

(3) Fig. 3. Conductivity vs concentration dependencies measured on the sensors under study.

Two separate identical chambers were used for studying the effects of ethanol and acetone doping in air-based mixtures. Gas response of the thin-film is defined as: S=

G − G0 G0

(2)

where G0 , G are the conductivities in synthetic air and in sample gas mixture, respectively. Fig. 3 shows the concentration dependence of tin dioxide thin-film response S in gas atmospheres containing ethanol and acetone vapors. The identification of gas mixtures was performed by using eight concentration dependencies of the sensor conductivity measured on different days. Dependence presented in Fig. 3 is nonlinear with noticeable saturation in the low- and high-concentration regions. In the concentration range around 100 ppm the dependence can be described by power law with exponent 0.61 ± 0.01 for acetone and 0.66 ± 0.01 for ethanol. From Fig. 3 it is readily seen that the sensor conductivity is affected both by the type of the admixture gas in air and by admixture gas concentration. On the other hand, it is also seen that the power law cannot correctly represent the conductivityvs-concentration dependence over the whole concentration range studied. 3. Theory In this section, we describe some parameters which are more suitable for gas identification than the resistance of the individual sensor or any single array segment. Firstly, using the condition of the adsorption–desorption equilibrium, we obtain the density of species adsorbed on the surface of the gas-sensitive film. Then, from electrical equilibrium condition, we find how many of them are in electrically charged states. The obtained surface charge defines the electrical potential distribution in the sensor layer. In practically important case of low concentration of the reduced gas (low-signal limit), we get an analytical dependence between the concentration of the free charge carriers in the gas-sensitive material and reduced gas concentration in oxidizing atmosphere. The type of this dependence defines a numerical procedure most suitable for the purpose of the current work and reveals model parameters most suitable for the gas identification procedure. Steady-state adsorption and electron equilibrium on the surface are described by balance equations for particles arriving to the surface and particles leaving the adsorption centers. In Langmuir’s theory, the particles are adsorbed only by empty adsorption centers. We will assume that oxidant gases and reduced gases are adsorbed

where i and j are integers varying from 1 to KOx or from 1 to KRed for oxygen and reduced gases correspondingly; ˛i , ˛j are the kinetic coefficients of the Langmuir’s isotherms: ˛i =

i A

˛j =

,

2mi kTgas

j A 2mj kTgas

(4)

 are the sticking coefficients; A is the effective cross-section area of the adsorption center; mi , mj are particle masses of gases i or j with partial pressures Pi or Pj ; T and Tgas are the temperatures of the film and the gas mixture, correspondingly; Ni , Nj are the surface densities of the adsorbed species of the i- or j-type gases; i , fi , Wi , j , fj , Wj are, correspondingly, the characteristic frequencies of the phonons, the ionization probabilities of the absorbed species, and desorption energies for the i- or j-type particles. The filling of the localized surface states induced by the adsorbed gas species (acceptor-like for oxygen gas and donor-like for reduced gas) leads to the buildup of the electric charge, QS , on the grain surfaces. QS = −q ·

KOx 

Ni · fi + q ·

KRed 

Nj · fj

(5)

j=1

i=1

q > 0 is the elementary charge. The resulting surface charge QS leads to the depleting or enriching of the grain boundaries with majority carriers on the scale of Debye length. As a result of the depletion, a space charge builds up in the material bulk near the surface thus inducing the electric field and, hence, the electrostatic potential with spatial distribution ϕ(r). In cylinder grains of a wide-gap non-degenerated n-type material with only one type of bulk donors of concentration ND the potential distribution ϕ(r) is determined by the Poisson equation [10]:

  1 ∂ϕ(r) q ∂2 ϕ(r) + · · ND+ − n(r) = r ε · ε0 ∂r ∂r 2

(6)

where coordinate r is changed from zero on the grain axis to R on the grain surface, and ε and ε0 are the dielectric constant and vacuum dielectric permittivity, respectively. The electron concentration in the conduction band n(r) at the temperature of complete ionization (ND+ = ND = n0 ) is:

 q · ϕ(r) 

n(r) = ND · exp −

kT

(7)

where n0 is the original concentration of the free charge carriers (electrons) in the material. As a result of the adsorption of oxidant reference gas (pure oxygen, for instance) and low concentration of analyzable reduced gases, the band bending ϕ(r) in n-type material increases so that

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in layers with small-radius grains most part of the electrons from the bulk will be localized on the acceptor levels of particles of the oxidant gas. If the grain is completely depleted of the free charge carriers, n(r)  ND , the space charge is determined by the concentration of the ionized donors ND in the bulk:

where nAi and nDi are the characteristic parameters of the centers. The physical meaning of these parameters is related to the characteristic electron concentrations in the grain bulk, when the Fermi level approaches the position of the localized level induced by the particle adsorbed on the grain surface:

∂2 ϕ(r) 1 ∂ϕ(r) q · ND + · = r ε · ε0 ∂r ∂r 2

nAi = gi · Nc · exp −

(8)

Condition n(r)  ND is relaxed by absorption of reduced species on the grain surface, however, if the original doping was performed by shallow donors, with levels more shallow than the donor levels induced by the absorbed species, then this condition could hold until the dependence of the conductivity vs concentrations saturates. The Fermi level pinning will be discussed in more details in Section 4. The solution of the Poisson equation (8) with boundary conditions ∂ϕ/∂r(0) = 0 in the center of the grain and condition ϕ(R) = ϕS on the grain surface can be obtained in analytical form [11]: ϕS − ϕ(r) =

kT R2 − r 2 · q (2 · LD )2

(9)

where LD is the Debye length:



LD =

ε · ε0 · kT q2 · ND

(10)

From Eq. (9) it follows that the electron thermal energy kT at R < 2·LD greatly exceeds the value of the potential barrier between the grain bulk and surface kT  ϕS − ϕ(r). This diffusion then efficiently uniforms free electron concentration over the grain surface and grain bulk, n(r) = n. In fact, for polycrystalline layers we obtain the flatband condition ϕ(r) = ϕS = const. In the following, we will consider only this situation, taking into account that efficient work of the sensor active layer is achieved under the condition R < LD [12–16]. The concentration of the charge carriers in the grain should be determined from the electrical neutrality condition:





q · (ND − n) · dV + V

QS · ds = 0

(11)

S

Here V and s are the volume and the surface area of the grain correspondingly. The surface charge density is determined by Ni , Nj и fi , fj (5). The numbers Ni , Nj of the adsorbed species of each gas type per unit surface area can be proved by direct substitution (12) into (3) in the form: Ni = NS · Nj = NS ·

p /(1 − fi )

1+

Ki Ox i=1

(pi /(1 − fi ))

(12)

p /(1 − fj )

1+

KjRed j=1

(pj /(1 − fj ))

Here, pi , pj are the partial pressures in the mixture normalized by the desorption rate: ˛i · Pi pi = , i · exp(−(Wi /kT ))

pj =

˛j · Pj j · exp(−(Wj /kT ))



nDj = gj · Nc · exp



(15)

kT

KRed  KOx p /(1 − fj ) · fj pi /(1 − fi ) · fi j=1 j i=1 KOx KRed (16) QS = −q · NS · − 1+

p /(1 − fi ) i=1 i

1+

j=1

pj /(1 − fj )

Introducing the concentration-like parameters of the gas mixture na and nd : 1 = na

KOx

p i=1 i

1+

· (1/nAi )

KOx

pi

KRed i=1 p · nDj j=1 j KRed nd = 1+

j=1

(17)

pj

allows us to determine the charge on the grain surface. In the limit ND  n, the electrical neutrality equation (11) in small grains is: 1 n nd = − n + na n + nd ı

(18)

Here ı = (s·NS )/(V·ND ) is characteristic of the dispersion of the gassensitive layer. This parameter increases with the decreasing film grain size. Physicochemical meaning of ı is the ratio of the maximum number of adsorption sites on the surface to the number of the free electrons in the bulk of the sensor. The capture of the electrons from the bulk allows adsorbing the charged species at the surface. For instance, ı = 100 means small surface population of the charged species. At high oxygen gas pressure, p  1, and in the absence of reduced gas (nd = 0), the electron concentration nref in a gas-sensitive material depends only on the material parameters:

(13)

(14)

−

kT Ec − EDj

Here Nc is the density of states near the conduction band bottom Ec ; Ec − EAi and Ec − EDi are the energy separations of the acceptor and donor states from the conduction band bottom, and gi and gj are the spin degeneracy factors for these levels. Apparently, the density of the gas particles, Ni , Nj , adsorbed on the grain surface depends on the thin film material properties (through n0 , Nc ), on the grain surface properties (through NS ), on the adsorbed particle characteristics (through nAi , nDj , Wi , Wj ), and on the gas concentrations or partial pressures Pi , Pj in the mixture (through pi , pj ). Substituting the expression (12) for the number of the adsorption centers Ni and Nj , filled with each gas type in Eq. (5) allows us to determine the charge on the surface of the grain layer:

nref =

The localized surface states related to the adsorbed gas particles are populated depending on the state energy level depth with respect to the Fermi level position. In monopolar non-degenerate ntype material, for the donor-like and acceptor-like levels induced by the particles of reduced gas and oxidant gases the probabilities fi of being in the ionized state, are by analogy with [8] (if n(r) = const = n): n fi = nAi + n nDj fj = nDj + n

 E −E  c Ai

na ı−1

(19)

The response S of the sensor to the reduced gas (2) at the constant charge carrier mobility is determined by the relative change of the charge carrier bulk concentration in the grain under the influence of the gas probe with respect to the concentration in the “reference” gas. This response depends nonlinearly on the reduced gas concentrations: S=

n − nref n · (ı − 1) − na = nref na

(20)

If nA is found from the measurements in the base gas atmosphere, ı – from film characterization, and n – from measurement results, Eq. (18) then defines an integral characteristic of the gas

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mixture, nd , which, according to (17), is determined by the number of gas components, their types, and concentrations nd = na ·

S · (S + 1) S + ı2

(21)

In the case of single reduced gas (pj = p), single oxidant gas (pj = pOx ), and highly dispersed layers (ı > S) we obtain



1 gD E · exp − · ı2 · gA S · (S + 1) kT



=

1 +1 ˇ·C

(22)

where C = 106 ·P/Pox is the concentrations (ppm) of the reduced gas in the gas mixture; Pox is the pressure of the oxidant gas (oxygen); E = (ED – EA ) is the energy separation of the donor level of the reduced gas from the acceptor level of oxygen; EA , ED are the positions of the acceptor (donor) levels of the oxygen (reduced) gases in the energy gap; gD and gA are spin degeneracy coefficients for donor and acceptor surface levels, correspondingly; WD is the desorption heat for reduced gas; ˇ is coefficient determined by the reduced gas type: ˛ · Pox ˇ=  · exp(−(WD /kT ))

(23)

Concentration dependence of the response in the coordinates y−1

= S · (S + 1) x−1 = C

(24)

is linear, y = a + b·x, and the parameters of this linear relationship depend on the type of the gas and on the material properties of the gas-sensitive layer:

b−1





gD E · exp − gA kT ˛ · Pox · a−1 =  · exp(−(WD /kT ))

a−1 = ı2 ·

Fig. 4. Calculation results for the parameters E and WD for two different types of reduced gas. The ellipses represent 95% of probability of gas identification.

(25)

The mathematical model developed in this section is used to explain the nonlinear behavior of the dependence of thin-film tin dioxide response on the reduced gas concentration in the analyzed gas probe. Additionally, analysis of the concentration dependencies performed in the coordinates (24) allows using Eq. (25) and finding parameters E and WD characterizing the reduced gas. 4. Discussion Solid lines in Fig. 3 show the concentration dependencies of the conductivity calculated according to the discussed model in a wide range of the concentrations of propanol or acetone vapors. The calculation parameters are kT = 0.058 eV, ı = 102 . The shapes of the curves agree with our experimental data. The correlation coefficient between the experimental data and calculation results is 0.998. Saturation in the low-concentration region is related to the Fermi level pinning at the acceptor level of oxygen. Adsorption of the reduced gas results in the emergence of additional charge carriers in the bulk due to the injection of the electrons from the donor levels of the reduced gas. The increase of the bulk carrier concentration, in turn, leads to additional absorption of the oxidant gas, so that the Fermi level position does not change noticeably. Therefore, in the low-concentration region the conductivity of the n-type semiconductor does not depend basically on the reduced gas concentration in the gas probe. In the region of high-concentration of the reduced gas, the Fermi level is pinned at the position of the surface donor level of the reduced gas, because the increase of the number of adsorbed particles of reduced gas on the surface does not lead to any significant change of the charge carrier concentration in the grain. The emergence of the additional electrons in the grain bulk induces the localization of electrons in the dopant donor levels of the reduced

gas, which leads to the transition of the adsorbed particles of the reduced gas into a neutral state and their subsequent desorption. The approximation of the concentration dependencies of the conductivity of the gas-sensitive layers in coordinates (24) by least square method allows determination of the parameters E and WD of the dopant reduced gas in the analyzed probe if the layer characteristics ı and temperature are known. The relative position of the reduced gas donor level E and its adsorption heat WD are the gas characteristics and do not depend on the gas concentration which allows the gas identification exclusively by numerical values of E and WD . Fig. 4 presents the results of the calculation of the parameters E and WD for two different reduced gases. The parameters, which are the characteristics of the analyzed gases, differ significantly, allowing identification of the gases in the gas probes. Concluding, we have proposed a method of gas identification based on the concentration-independent parameters of the gases. The characteristic parameters of the gases, E and WD , can be considered as a natural coordinate system for the gas identification. We have demonstrated that mathematical analysis of the response vs concentration dependencies allows identification of the bicomponent gas mixtures in a wide range of concentrations using single gas sensor. We need to note, however, that the model has been tested in experiments carried out under special conditions, i.e. using single analytes and controlling the gas humidity. In real-life conditions, both environmental conditions and analytes can vary; there can be mixtures of gases presented and, additionally, the sensor characteristics can also deviate. As a next step of the research, it would be interesting to study the effects of competing analytes in order to find out if the concentration-independent parameters identified in the model are still valid. Eq. (17) imply that qualitative and quantitative analysis of complex gas mixtures in the framework of suggested model can be ambiguous and problematic in case of using single-sensor approach. This concerns also the case of gas mixture with unknown concentration of water vapors. In single-sensor case, Eq. (17) cannot be solved even for three-component gas mixture. The way to obtain a linear independent equation system is to formulate Eq. (17) for different temperatures using large temperature variations. The temperature variation can be of the order of energy separation between the levels induced by the different analytes. However, the real sensors operate in relatively narrow temperature range. Therefore, in the case of single sensor, two-component gas mix-

V. Simakov et al. / Sensors and Actuators B 137 (2009) 456–461

tures limit the model applicability. To complete testing the model and expand its applicability to arbitrary gas compositions a special experiment should be planned and carried out. For instance, we plan to develop a sensor array with characteristics of each sensor changeable according to a certain program. The combined response of such an array to a change in environmental composition would allow solving the system and subsequent determination of the gas parameters. 5. Conclusions The identification ability of a single SnO2 -based gas sensor and reliability of the classification of the gas mixtures can be significantly enhanced by using the concentration-independent quantities, which account for gas response of the resistive sensor layers and depend only on the type of the gas, as input parameters. We present an example of a choice of such parameters for gas mixture identification, which is based on the study of the mechanisms of gas response. Acknowledgments This work was supported by the research grant MD-3092.2008.8 from the President of the Russian Federation and the grant A/08/08585 from the German Academic Exchange Service (DAAD). A. Voroshilov would like to thank the New Perspectives Foundation for the grant NPF 5/LT. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.snb.2009.01.005. References [1] C.Q. Sun, in: J.A. Rodriguez, M. Fernández-García (Eds.), Synthesis, Properties, and Applications of Oxide Nanomaterials, 1st ed., Wiley–VCH, New Jersey, 2007, pp. 9–48 (Chapter 1). [2] D. Kotsikau, M. Ivanovskaya, in: J.W. Gardner, J. Yinon (Eds.), Electronic Noses & Sensors for the Detection of Explosives, vol. 159, Kluwer, Dordrecht, 2004, pp. 93–115. [3] T.C. Pearce, S.S. Schiffman, H.T. Nagle, J.W. Gardner, Handbook of Machine Olfaction: Electronic Nose Technology, Wiley–VCH, Weinheim, 2003, p. 624. [4] P. Gründler, Chemical Sensors: An Introduction for Scientists and Engineers, Springer, Berlin, 2007, p. 273. [5] V. Simakov, O. Yakusheva, A. Grebennikov, V. Kisin, I–V characteristics of gassensitive structures based on tin oxide thin films, Sens. Actuators B: Chem. 116 (2006) 221–225. [6] V. Simakov, O. Yakusheva, A. Grebennikov, V. Kisin, Temperature variation of the current–voltage characteristics of thin-film gas sensors, Tech. Phys. Lett. 32 (2006) 48–50. [7] V. Simakov, O. Yakusheva, A. Voroshilov, A. Grebennikov, V. Kisin, Variation of the conductivity of a thin film of tin dioxide in response to stepwise gas sampling, Tech. Phys. Lett. 32 (2006) 725–728.

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Biographies Dr. Vyacheslav Simakov is currently a Professor at the Chemistry Department of Saratov State Technical University (Russia). He graduated at Saratov State University in 1995 with Honors Diploma of Engineer-Physicist in Microelectronics and Semiconductor Device major. In 1996–1999 he worked on his postgraduate degree at Saratov State Technical University (Russia), and in 1999 he received his Candidate of Engineering Sciences degree in Electrochemistry. In 2002 he joined the Saratov State Technical University where in 2006 he completed his Doctor of Science degree in Electrochemical Engineering. His present scientific interests are chemical gas sensors and nanosensors for gas recognition systems. Alexander Voroshilov graduated from Saratov State University in 2006, with a major in Technology. Now he is taking postgraduate courses at Saratov State University. His present scientific interests are Solid State Electronics. Alexander Grebennikov graduated from Saratov State University in 1971, with a major in Physics and Electronics. Now he holds the position of Leading Engineer at Nano- and Biomedical Technology Department at Saratov State University. His scientific interests are SnO thin films and their applications. Nicholay Kucherenko graduated from Saratov State University in 2005, with a major in Technology. His major research interests cover the synthesis of gas sensitive nanomaterials. Now he is taking postgraduate courses at Saratov State University. Olga Yakusheva graduated from Saratov State University in 1991, with major in Semiconductor Physics. In 2006 she started her postgraduate project under the supervision of Prof. V. Simakov at Saratov State Technical University. She is holding the position of the Head of the Technology Group. Her major interests lay in thin film technology. Dr. Vladimir Kisin is currently a Professor at Nano- and Biomedical Technology Department at Saratov State University. In 1974 he graduated from Saratov State University with Diploma of Physicist, major in Semiconductor Physics. In 1979 he received his Candidate of Physical & Mathematical Sciences degree with a major in Semiconductor Physics from Gorkyi State University (Russia). In 1994 he joined the Saratov State Technical University, where in 2000 he completed his Doctor of Science degree in Electrical Engineering. His present research interests include solid-state gas sensors and thin-film technologies.