Sensors and Actuators B 107 (2005) 722–729

Modeling on oxygen chemisorption-induced noise inmetallic oxide gas sensors

Sami Gomri, Jean-Luc Seguin∗, Khalifa AguirLaboratoire Materiaux et Microelectronique de Provence (L2MP, UMR 6137-CNRS), Universit´e Aix-Marseille III, Paul Cezanne,

Faculte des Sciences de St J´erome, service 152, F-13397 Marseille Cedex 20, France

Received 20 October 2004; accepted 2 December 2004Available online 19 January 2005

Abstract

Noise spectroscopy might be highly useful for improving gas sensors selectivity if both theoretical model and sensing devices can beadequately developed. In this paper, we propose, as a first step towards an overall model, a theoretical description of adsorption–desorptionnoise in metal oxide gas sensors. Using Langmuir’s isotherm, we derive an exact expression for the adsorption–desorption noise in the casewhere only one molecular species reacts on the sensor surface. We found that the contribution of adsorption–desorption noise to the noises described.T re discussedi©

K

1

itbt[dtbm

emdt

U

etalof fil-es on

nsorssignalbeen

d as amire-s.iffer-ctrum

e sen-er of

ingnsing

pro-etical

0d

pectra is a Lorentzian component. Application of the proposed model for simulating the oxygen chemisorption-induced noise ishe validity of the proposed method, its limitations, and directions for its elaboration to more general cases such as gas mixtures a

n conclusion.2004 Elsevier B.V. All rights reserved.

eywords:Noise spectroscopy; Adsorption–desorption; Metallic oxide gas sensor

. Introduction

Nowadays, there is a great interest in implementing sens-ng devices in order to improve environmental and safety con-rol of gases or to carry out the optimization of chemical andiological processes. Advances in fabrication and materials

echnology have given simple, robust, and low cost devices1,2] using thin films or porous ceramics of n-type semicon-uctor oxides, such as ZnO, SnO2, WO3, etc. However, this

ype of gas sensor presents an inherent lack of selectivity,ecause the gas detection mechanism is rather unspecific andore or less any type of reducing or oxidizing gas is detected.Intensive research has been going on during the past sev-

ral years to achieve selective gas sensing. In a few cases, aaterial giving a specific interaction with the molecule to beetected has been successfully used to design a highly selec-

ive gas sensor[3]. More often, investigations have been fo-

∗ Corresponding author. Tel.: +334 9128 8973; fax: +334 9128 8970.E-mail address:jlseguin@l2mp.fr (J.-L. Seguin).

RL: http://www.l2mp.fr (J.-L. Seguin).

cused on finding strategies to enhance the selectivity of moxide sensors. One of the possible solutions is the useters or catalysts to discriminate sensitivity between gasthe basis of molecules size[4] or surface reactions[5]. Othertechniques using different measurement strategies (searrays, dynamic measurements, etc.) associated withprocessing algorithms (FFT, neural networks, etc.) havedeveloped (see for example[6]).

More recently, noise spectroscopy has been proposemean of extracting a more selective response from chesistive[7–10]and surface acoustic wave[11,12]gas sensorSome experimental results revealed that exposure to dent gases causes modification in the power density spe(PDS) of the gas sensor’s resistance fluctuations[13,14]. Ithas been shown that, using noise spectroscopy, only onsor can be enough to analyze the composition of a numbdifferent gases[13].

Noise spectroscopy might be highly useful for improvgas sensors selectivity if both theoretical model and sedevices can be adequately developed. In this paper, wepose, as a first step towards an overall model, a theor

925-4005/$ – see front matter © 2004 Elsevier B.V. All rights reserved.oi:10.1016/j.snb.2004.12.003

S. Gomri et al. / Sensors and Actuators B 107 (2005) 722–729 723

description of adsorption–desorption noise in metal oxide gassensors, which is only one component of the measured noisespectra. Application of the proposed model for simulating theoxygen chemisorption-induced noise is described. Conclud-ing this paper is a discussion on the validity of the proposedmethod, its limitations, and directions for its elaboration tomore general cases such as gas mixtures.

2. Gas sensing mechanism in resistive gas sensor

Basically, the actual gas sensing process consists of twostages: physisorption and chemisorption. Their separationarises because of the different strengths of the binding. Ph-ysisorption is a weak adsorption process, usually associatedwith polarization and van der Waals forces between the ad-sorbate and the adsorbent. The interaction energy does notexceed 1 eV[16]. On the other hand, chemisorption is basedon stronger covalent forces involving a partial electron trans-fer between adsorbent and adsorbate. The interaction energyfor chemisorption can be in the range of 1–10 eV[16].

Gas-adsorption, understood as a chemical interactionbetween the gas molecules and the semiconductor surface,is accompanied by charge exchange creating acceptor- ordonor-like bandgap level, whose occupation probabilityis given by the Fermi–Dirac distribution function[19]. Itsb f thea

ure-d low-t n andc rin-s rinsicp ilar rea (e.g.a ll oft lecu-l xylg ondlyi de-f .

nsor-l eyc rdert edt

oxy-g elec-t ionW thes rfaceT ter-mT s up-w tion

increases until, at equilibrium, the Fermi level of the semi-conductor is aligned with the Fermi level of the adsorbate.Beyond this point, charge no longer leads to a decrease inthe free energy of the system, and chemisoption stops. Theformation of a barrier at the semiconductor surface affectsboth the equilibrium adsorbed amount and the adsorptionrate. Similar considerations apply in the case of donor statesintroduced by electropositive gases such as water vapor. Herean electron is transferred from the approaching molecule tothe semiconductor. The energy of chemisorption isWφ−I,whereI is the ionization energy of the adsorbate. In this case,adsorption of an electropositive molecule leads to a down-ward bending of the energy bands and a reduction in thework function[18].

3. Kinetics of adsorption

In a first approach, we choose to use the simplest adsorp-tion isotherm, which is derived from Langmuir’s theory. Thisis based on the following assumptions:

(i) a uniform surface with equivalent adsorption sites,(ii) only a single adsorbate per site,

(iii) adsorption energy is independent of occupancy of neigh-bouring sites.

D bero fa rites[

w

α

ℵ orp-t edm re.τ

τ

w thest rgy.

t

N

w

τ

F

ehavior as acceptor or donor will depend on the type odsorbed molecule.

Molecular recognition is based on typical temperatependent elementary steps. These steps first involve

emperature surface reactions. Examples are adsorptioatalytic reactions at active sites (the latter involving intic point defects, such as oxygen vacancies, and/or extoint defects, such as segregated metal atoms) and simctions at grain boundaries or at three-phase boundariest metallic contacts or at surface metallic clusters). A

hese reactions involve adsorbed negatively charged moar (O2−) or atomic (O−) oxygen species as well as hydroroups (OH) at different surface sites. These steps sec

nvolve high-temperature bulk reactions between pointects in the SnO2 crystal and oxygen (O2) in the gas phase

Thus, in the case of an n-type semiconductor gas seike SnO2, oxygen and NO2 create acceptor levels, as thapture electrons from the bulk of the sensing layer in ohat the adsorption take place, while H2 and or CO introduconor levels because they give electrons to the SnO2 through

he creation of an oxygen vacancy.Consider a single electronegative molecule, such as

en, approaching the surface of the semiconductor. If itsron affinityA is larger than the semiconductor work functφ, the molecule will tend to pick up an electron from

emiconductor and thereby become adsorbed at the suhe differenceA−Wφ is the adsorption energy and deines the position of the acceptor level in the bandgap[18].he presence of acceptor levels acts to bend the bandards. Thus with increasing adsorption, the work func

-,

.

enotingN(t), the density of adsorbed molecules (the numf adsorbed molecules per unit area) andN* , the number odsorption centre per unit area, the adsorption rate w

16]:

dN

dt= αp(N∗ −N) − βN (1)

here

= ℵ∂√2πmKT

and β = 1

τ(2)

is the probability that a molecule approaching an adsion centre will be fixed,mand∂, respectively the adsorbolecule mass and effective area,p the partial gas pressu

¯denotes the average residence time, given by[17]:

¯= τ0 exp

(Ed

RT

)(3)

hereτ0 is the period of thermal vibrations normal tourface of the adsorbed molecule,R the gas constant,T theemperature, andEd the desorption or the adsorption ene

Integration of Eq.(1) with the initial conditionN= 0 at= 0, yields:

(t) = αp

αp+ βN∗(

1 − exp

(−tτ

))(4)

here

= 1

αp+ β (5)

ig. 1shows the functionN(t) given by Eq.(4).

724 S. Gomri et al. / Sensors and Actuators B 107 (2005) 722–729

Fig. 1. The density of adsorbed moleculesN(t) vs. time.

The densityN0 of adsorbed molecules at equilibrium canbe calculated from the value ofN(t) at t = ∞:

N0 = αp

αp+ βN∗ (6)

In fact, this hyperbolical dependence ofN0 with the par-tial gas pressurep is not always achieved, and the measuredisotherms are often different from Langmuir’s isotherm. ThequantityEd in Eq. (3) seems to be dependent onN, decreas-ing whenN increases, which leads deviation from Langmuir’sisotherm. This difference can be also explained by the interac-tion between adsorbed molecules or by a non-homogeneousadsorption surface[16]. As a first step, we will use this sim-plest adsorption theory to give a method for developing thegas-adsorption-induced noise model. This simplest adsorp-tion theory will be discussed in Section5.

4. Modeling of the adsorption–desorption noise

The exact origin of the resistance fluctuations due tochemical environment is presently not clearly established.It can certainly be associated with fluctuations of the carrierdensity and mobility due to concentration fluctuations andmotion of chemical species originating from the chemicalenvironment. The main noise sources linked to the chemicale ules,d rfacea ntialb Them f, at

least, the contributions of these three noise sources. In orderto develop a complete model for the gas-chemisorption-induced noise, we have to take into account simultaneouslyall these noise sources. Such procedure seems to be furthercomplicated. As a first step towards an overall model, webegin by considering only the effect of the carrier densityfluctuation. In our analysis, we will consider a homogenoussemiconductor, and consequently, the effect of mobility fluc-tuation on the barrier height variation will not be treated. Thecalculation of the adsorption–desorption noise will be doneon the basis of the free electron-concentration fluctuation.After equilibration of the sensor with its environment, thecapturing and releasing of molecules becomes a stochasticprocess[13]. Hence, the electron capturing–releasing fromthe sensing layer is also a stochastic process.

4.1. Theoretical approach

When an oxygen molecule is approaching the surfaceof an n-type semiconducting oxide, such as SnO2, theprobability of trapping a conduction electron from thesemiconductor is the occupation probability of the bandgapacceptor levelEa =A−Wφ, whereA is the electron affinity ofthe gas molecule andWφ the semiconductor work function.This probability is given by the Fermi–Dirac distributionfunction[19]:

f

wd y theb el,t

tionp whicht s theo

n

w rea(

f free e

nvironment are adsorption–desorption of gas moleciffusion of the adsorbed molecules on the sensor sund shot noise of the current flowing through the potearriers at grain boundaries in the sensing layer.easured noise spectra result in the superposition o

Fig. 2. The capturing–releasing o

n(Ea) = 1

1 + (1/ga) exp[(Ea − EF)/KT ](7)

hereEF is the semiconductor’s Fermi level andga theegeneration degree of the impurity state introduced bandgap acceptor levelEa. In the case of an acceptor lev

he degeneration degree isga = 1/2 [19].Fig. 2 shows an illustration of the adsorption–desorp

rocess at the surface of a resistive gas sensor, duringhe capture of free electrons from the semiconductor irigin of changes in the sensor’s conductivity.

The number of free electrons per unit area is

s(t) = nsi − nsa(t) (8)

herensi is the initial number of free electrons per unit abefore the capture by the adsorbed molecules) andnsa the

lectrons during the adsorption process.

S. Gomri et al. / Sensors and Actuators B 107 (2005) 722–729 725

number of trapped electrons per unit area, as explained inFig. 2.

The number of trapped electrons per unit area is the prod-uct of the capture probability given by Eq.(7)and the numberof adsorbed molecules per unit areaN(t):

nsa(t) = fn(Ea)N(t). (9)

Neglecting intrinsic surface or interface traps acting as accep-tors or donors, and considering only the effect of the inter-bandgap levels introduced by the gas-adsorption, then thefluctuation of the free electrons density writes:

�n(t) = −�nsa(t)

d= −fn(Ea)�N(t)

d(10)

whered is the thickness of the sensing layer.In order to calculate the expression of the fluctuation

�N(t) around its valueN0 at the adsorption–desorptionequilibrium, we use an analogy with the theory ofgeneration–recombination noise in semiconductor[20]. Theprocesses of adsorption and desorption correspond to the pro-cesses of generation and recombination, respectively.

The differential equation (1) can be rewritten as:

dN

dt= a(N) − d(N) (11)

w

a

a .-

r ev

U le

w

τ

T

�

where �N0 is the value of the fluctuation�N(t) at theadsorption–desorption equilibrium.Using Eq.(10), we canexpress the PDS of the free electron’s density fluctuations:

S�n(f ) = (fn(Ea))2S�N (f ) (17)

where,S�N(f ) is the PDS of the fluctuation�N.Using a unilateral spectral representation,S�N(f ) can be

expressed as:

S�N (f ) = 2|T�N (f )|2 for f > 0 (18)

whereT�N (f ) is the Fourier transform of�N(t).A simple calculus gives:

S�N (f ) = 2�N20

τ2

1 + 4π2f 2τ2= 4

(�N2

0τ

2

) τ

1 + 4π2f 2τ2

(19)

As the quantity�N20τ2 is the mean square value of the ran-

dom variable�N, the PDS of the fluctuation of the adsorbedmolecules number writes:

S�N (f ) = 4�N2 τ

1 + 4π2f 2τ2(20)

O tion–d duc-t

�

C sityfl

S

A or’sc

G

w ,a h oft hea

thes

�

here

(N) = αp(N∗ −N) and d(N) = βN (12)

re, respectively, the adsorption and the desorption rateConsidering small fluctuations ofN around the equilib

ium valueN0, a(N) andd(N) expand in Taylor series in thicinity of N0:

a(N) = a(N0) + (N −N0)

(da

dN

)N0

,

d(N) = d(N0) + (N −N0)

(dd

dN

)N0

,

N(t) = N0 + �N(t) (13)

sing Eqs.(11)–(13), we obtain the following differentiaquation:

d�N

dt= −�N

τ(14)

here

=[(

dd(N)

dN− da(N)

dN

)N0

]−1

= 1

αp+ β (15)

he solution of Eq.(14) is given by:

N(t) = �N0 exp(− tτ

)(16)

n the other hand, using the analogy between adsorpesorption and generation–recombination in semicon

ors, the mean square value�N2is given by[15,20]:

N2 = a(N0)

((dd/dN) − (da/dN))N0

= a(N0)τ

= βN0τ = βαp

αp+ βN∗τ (21)

onsequently, the PDS of the free electron’s denuctuations is:

�n(f ) = 4(fn(A−Wφ))2

d2β

αp

αp+ βN∗ τ2

1 + 4π2f 2τ2(22)

t the adsorption–desorption equilibrium, the whole sensonductance is expressed by:

= eµnns

L= eµn

s

L(n0 + �n) (23)

here e and µn are the electron charge and mobilitysnd L, respectively the cross-section area and lengt

he sensing layer,n0 is the density of free electrons at tdsorption–desorption equilibrium.

From Eq.(24), the fluctuation of the conductance ofensing layer writes:

G =(eµn

S

L

)�n (24)

726 S. Gomri et al. / Sensors and Actuators B 107 (2005) 722–729

Then, the PDS of the fluctuation of the electrical conductanceis:

S�G(f ) =(eµn

s

L

)2S�n(f ) =

(eµn

s

L

)24

(fn(A−Wφ))2

d2

×β αp

αp+ βN∗ τ2

1 + 4π2f 2τ2

=(eµn

s

Ld

)2 · H0

1 + f 2/f 2c

(25)

where

H0 = 4(fn(A−Wφ))2βαp

αp+ βN∗τ2 (26)

and

fc = 1

2πτ= αp+ β

2π

= 1

2π

[τ−1

0 exp

(−Ed

RgT

)+ ℵ∂p√

2πmKT

](27)

Assumingµn is not significantly affected by adsorption, andfor fixed sensor geometry, the term

(eµn

sLd

)2is a constant.So, Eq.(25)shows that

S�G(f ) ∝ S�n(f ) (28)

and both PDS will have the same shape.

4

wep tion-i mostg activea perat-i hicha -typem hus,n oisei sen-s

sityw of aSfi ec-t ientb -t utn werefTT ea ise

n-e is aL r fre-q

Fig. 3. PDS of the fluctuation of the free electrons density calculated foroxygen adsorbed on the surface of a SnO2 sensor at 673 K.

Eqs. (26) and (27)show that noise spectra parameters(fc andH0) depend on the molecular massm, the adsorbedmolecule effective area∂, and the desorption energyEd. Thus,the noise spectrum is a function of the detected gas.

However,H0 andfc are not only functions of the detectedgas, but they are also temperature and pressure dependants.Fig. 4 shows the temperature dependence offc andH0 foran oxygen partial pressure fixed at 0.2 atm. In the operating

Fig. 4. Temperature dependence of the corner frequencyfc (a) and low fre-quency magnitudeH0 (b) of the PDS of the fluctuation of the free electronsdensity for oxygen adsorbed on SnO2.

.2. Numerical evaluation

To demonstrate the utility of the proposed model,resent numerical simulations of the oxygen chemisorp

nduced noise. Oxygen is the predominant gas species inas sensing application, since it is the most abundant regent in air. The surface of a semiconductor gas sensor o

ng in air is covered with chemisorbed oxygen species, wre known to introduce acceptor-like surface states on netal oxides due to the high electron affinity of oxygen. Tumerical evaluation of oxygen chemisorption-induced n

s the first step for noise modeling in semiconductor gasors.

The PDS of the fluctuation of the free electrons denas calculated for oxygen chemisorption on the surfacenO2 sensor at 673 K (Fig. 3). The gas partial pressurepwasxed at 0.2 atm. (2× 104 Pa). The adsorbed molecule effive area∂ was evaluated using the van der Waals coefficderived from the gas triple point[21]. For metal oxide sys

ems, and in particular for SnO2, the present knowledge aboumerical values of desorption energy is rather poor. We

orced to use estimation for adsorption energyEd ∼ 2 eV[27].he chemisorption probability value was fixed at 1 (ℵ = 1).he number of chemisorption sites per unit surface arstimated toN* = 1015 cm−2 [26].

Eq.(25)andFig. 3show that oxygen chemisorption gerates a fluctuation in the sensor’s conductance, whichorentzian-like noise spectra with a characteristic corneuencyfc and low frequency magnitudeH0.

S. Gomri et al. / Sensors and Actuators B 107 (2005) 722–729 727

temperature range of SnO2 gas sensors (450–700 K),fc val-ues are in the order of 106 Hz. In the same temperature range,H0 values are distributed from 10−8 to 5× 10−2, which haveto be multiplied by the gas sensor geometry-dependent fac-tor (eµnS/Ld)2 in order to obtain the conductance fluctuation.In addition, increasing the temperature decreases the cornerfrequency, while the low frequency magnitudeH0 increases.That means, that we can set the temperature in order to getmaximum adsorption–desorption noise for a specific desorp-tion energy, and consequently, for a specific detected gas.However, the operating temperature range of the gas sensor(450–700 K for the SnO2) must be respected.

An analysis of Eq.(27) shows that the corner frequencyfc, at constant gas partial pressure, has a minimum, which de-pends on the desorption energy and the physical parametersof the detected gas (m and∂). Fig. 4 shows the temperaturedependence of the corner frequencyfc(T), in the tempera-ture range 100–1000 K; before reaching the minimum of thecorner frequency.

In our numerical simulation, the oxygen partial pressurewas fixed at 0.2 atm., since it represents 20% of the total atmo-spheric air. However, if the oxygen partial pressure varies, thelow frequency magnitudeH0 varies significantly. An analy-sis of (26) shows thatH0 at a constant temperature has amaximum for the pressurepmax that can be determined from:

(

w

p

A sm

H

F e-pn r-t eda V

F nitudeH gena

[27], the low frequency magnitudeH0 reaches its maximum(1016 m−4 s/Hz) at an oxygen partial pressure of 10−5 Pa.

5. Discussion

In this paper, we have given, as a first step towards an over-all model, a theoretical description of adsorption–desorptionnoise in metal oxide gas sensors and showed that this noiseproduces a Lorentzian contribution to the total noise spec-trum. The noise spectrum in solid-state chemical sensors re-sults in the superposition of, at least, the contributions ofthree components: white noise, Lorentzian noise, and the1/f noise[15]. In the above analysis, we have calculated thenoise induced by the chemisorption of a single gas speciesin the atmospheric air. We found that the contribution ofadsorption–desorption noise to the total noise spectra is aLorentzian component. Bearing in mind that a single acti-vation energy produces a Lorentzian component[20], theadsorption–desorption noise can give a Lorentzian contribu-tion.

Noise measurements on SnO2 sensors[7] revealed a 1/f�

frequency dependence. As discussed by the authors of Ref.[7], this flicker noise is a function of sensor resistance and ofother unknown parameters. The Lorentzian noise spectrumg noti eens tiono tors[ net uredn

ucedb tmo-s e (a d-e r gass nt int leakd

of as oratedf COd Os

C

( st ngiw

αpmax + β)3 − 3pmax(αpmax + β)2α = 0 (30)

hich gives

max = β

2α(31)

t this pressure the low frequency magnitudeH0 reaches itaximum

0 max = 1627(f (A−W�))2N∗β−1 (32)

rom Eqs.(2) and (3), we can see that this maximum dends on the gas-desorption energyEd and the maximumoise is higher for higherEd. Fig. 5 shows the oxygen pa

ial pressure dependence ofH0 for a sensor temperature fixt 673◦K. For a desorption energy,Ed assumed to be 2 e

ig. 5. Gas partial pressure dependence of the low frequency mag

0 of the PDS of the fluctuation of the free electrons density for oxydsorbed on SnO2 at 673 K.

iven by our model of adsorption–desorption noise isnconsistent with this experimental result, since it has bhown that 1/f� spectrum can result from the superposif a finite number of Lorentzian spectra in thin film resis

29] or in MOS field effect transistors[30]. Furthermore, axperimental work on WO3 semiconductor in air[28] showshe existence of a Lorentzian contribution to the measoise spectra.

The above analysis enables calculating the noise indy the chemisorption of a single gas species in the apheric air and its variation with the ambient gas pressurp)nd temperature (T). Therefore, it can be applied for moling the chemisorption-induced noise of semiconductoensors in applications, where only a single reactive agehe ambient atmosphere is of concern, e.g. for oxygenetection in vacuum systems.

While in the present paper we considered the caseingle chemisorbed gas species, the model can be elabor multi-analyte gas mixtures. For example, consideretection in dry clean-air. In this case, CO reacts with−pecies,

O(gas)+ O− kCO−→ CO2 + e−

the reverse reaction is negligible[16]), restoring electrono the conduction band of SnO2 and consequently increasits conductivity. Therefore, instead of Eq.(1) we should nowrite:

dNo

dt= αOpO2(N∗ −NO) − βONO − kCOpCONO

728 S. Gomri et al. / Sensors and Actuators B 107 (2005) 722–729

whereαO andβO have the same definitions as these givenby (Eq. (2)), Po2 andPCO the partial pressures of O2 andCO, respectively, in the ambient atmosphere, andkCO therate constant for CO oxidation to CO2 by O− anions.

The calculation of the noise spectrum was done on the ba-sis of the free electron-concentration approach. We assumedthat the generated noise is attributed to the fluctuation ofthe free electron concentration. Indeed, the mobility is de-termined by the scattering of carriers and there are severalkinds of scattering mechanisms operating in solids[22,23].Among them, chemisorption may produce changes in the sur-face mobility, as ionosorbed molecules play an important rolein electronic conduction as charged scattering centres at thesurface[24]. Besides, if we consider a polycrystalline mate-rial, the barrier height variations can be associated with themobility fluctuation. Thus, the chemisorption-induced noisecan be also explained by a fluctuation of the carriers mobility.Consequently, a complete model for the gas-chemisorption-induced noise might be developed taking into account simul-taneous fluctuation of carrier density and mobility.

The proposed model was developed using the theory ofLangmuir’s isotherm, in which we assume a constant bindingenergy between adsorbate and adsorbent[16]. However,in the case of chemisorption on semiconductors wherecharge transfer is involved, the binding energy (adsorptionh rbeds eena ntp ach,t eoryo thee y oft

6

d onL en-a ptiono nd itsv e( genc tiono tra isa lowf d gas( ises tiono

face,w ulti-a vel-o hicht h thes

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[ sur-002)

[ sur-

[ mes and

[ , J.ugh. Ac-

[ lid-.

[ rfaces

[ gy,

[ The

[[ dro-

8 (7)

[ Hill,

[ rog.

[ oise

[ ther,

[ ens.

eat) varies with the degree of coverage of chemisopecies due to the strong electronic interaction betwdsorbate and adsorbent[25]. Therefore, while in the preseaper we used the simplistic Langmuir-based appro

he proposed method should equally work using the thf Wolkenstein’s isotherm, which takes into accountlectronic interactions and their effect on the adsorptivit

he semiconductor substrate[26].

. Conclusion

A simple model of adsorption–desorption noise, baseangmuir adsorption theory is presented. This modelbles calculating the noise induced by the chemisorf a single gas species in the ambient atmosphere aariation with the ambient gas pressure (p) and temperaturT). Using the proposed model for simulating the oxyhemisorption-induced noise, we found that the contribuf oxygen adsorption–desorption noise to the noise specLorentzian component having a corner frequency and

requency magnitude which are specifics of the adsorbeEqs.(26) and (27)). This result confirms that resistance nopectroscopy could be a useful tool for extracting informan the nature of the detected gas.

Considering the catalytic reactions on the sensing sure suggested that the model could be elaborated for mnalyte gas mixtures. This will be done in future work deping a global model using the Wolkenstein approach w

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Biographies

Sami Gomri was born in Sfax, Tunisia, in 1977. He obtained the Elec-trical Engineering Diploma from Ecole Nationale d’Ingenieurs de Sfax(ENIS), Tunisia, in 2001. He received the DEA (post-graduate diploma)in Micro and Nano electronics in 2002 from the University of Provence(Marseille, France). He is currently preparing his PhD degree in micro-electronics in the L2MP laboratory at the University of Provence. Hisresearch interest is the enhancement of gas sensors selectivity by the useof noise spectroscopy.

Jean-Luc Seguinwas born in 1958. He is a senior researcher at the PaulCEZANNE - Aix-Marseille III University (France). He is also senior lec-turer in Electronics at the Institute of Technology of Marseille. He wasawarded his PhD degree from the University of Aix-Marseille II in 1983with a thesis on adsorption and wetting on graphite. He received the Ha-bilitation a Diriger des Recherches from the University of Aix-MarseilleIII in 2000. He is specialized in thin films preparation and characterizationfor applications in microsystems. Since 1997, he is interested in gas mi-crosensors and he developed a selective ammonia sensor based on CuBrmixed ionic conductor. He currently works at Laboratoire Materiaux &Microelectronique de Provence (L2MP – CNRS) Marseille (France), onWO3 gas sensors and selectivity enhancement strategies including noisespectroscopy.

Khalifa Aguir was born in 1953. He is Professor at Paul CEZANNE -Aix-Marseille III University (France). He was awarded his Doctorat d’Etates Sciences degree from the Paul Sabatier University Toulouse (France)in 1987. He is currently Head of Sensors Group at Laboratoire Materiaux& Microelectronique (L2MP – CNRS) Marseille (France). His scientificinterests are thin films preparation and characterization for microsystems.Since 1998, he is interested in gas microsensors and selectivity by signaltreatment strategies, and electronic noses, physical and chemical proper-ties of metal and oxides thin films, and applications in microelectronics.He currently works on WO3 gas sensors and selectivity enhancementstrategies including PCA analysis, noise spectroscopy and modelling ofsensor responses.