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(1)AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY FACULTY OF COMPUTER SCIENCE, ELECTRONICS AND TELECOMMUNICATIONS DEPARTMENT OF ELECTRONICS. DOCTORAL THESIS Patryk Gwiżdż. Nanosensor systems for gas detection and recognition Systemy detekcji i rozpoznawania gazów na bazie nanosensorów. Supervisor:. prof. dr hab. inż. Katarzyna Zakrzewska Faculty of Computer Science, Electronics and Telecommunications AGH University of Science and Technology, Cracow. Cracow, 2018. 1.

(2) Declaration of the author of this dissertation: Aware of legal responsibility for making untrue statements I hereby declare that I have written this dissertation myself and all the contents of the dissertation have been obtained by legal means.. date, author signature. Autor był stypendystą projektu „Doctus – Małopolski fundusz stypendialny dla doktorantów” współfinansowanego ze środków Unii Europejskiej w ramach Europejskiego Funduszu Społecznego.. Praca realizowana częściowo w ramach grantu NCN DEC-2011/03/B/ST7/01840 oraz grantów dziekańskich w latach 2012-2015.. 2.

(3) Składam serdeczne podziękowania Pani Profesor Katarzynie Zakrzewskiej, za cenne uwagi i nieocenioną pomoc podczas realizacji pracy. Panu dr hab. inż. Andrzejowi Brudnikowi oraz Panu dr Adamowi Czapli dziękuję za cenne uwagi i pomoc podczas realizacji pracy. Szczególne wyrazy wdzięczności składam mojej rodzinie, w szczególności żonie Ewelinie i moim rodzicom za wsparcie, które otrzymałem w trakcie realizacji rozprawy.. 3.

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(5) Content Streszczenie ................................................................................................................................ 6 Abstract ...................................................................................................................................... 8 1. Introduction ........................................................................................................................ 9 2. Resistive-type metal oxide gas sensor .............................................................................. 14 2.1. Operating principle and basic parameters ................................................................. 14 2.1.1. Static operating mode ......................................................................................... 20 2.1.2. Dynamic operating mode ................................................................................... 26 2.2. Nanomaterials as gas sensors .................................................................................... 30 2.3. Sensor array ............................................................................................................... 33 2.3.1. Definition and functionality ............................................................................... 33 2.3.2. Construction ....................................................................................................... 35 2.3.3. Signal processing................................................................................................ 35 2.3.4. Pattern recognition ............................................................................................. 37 3. Experimental details ......................................................................................................... 44 3.1. Design and construction of sensor arrays .................................................................. 44 3.1.1. Commercial sensors ........................................................................................... 44 3.1.2. Thin films ........................................................................................................... 48 3.1.3. Nanopowders ...................................................................................................... 50 3.2. Experimental setup .................................................................................................... 52 3.2.1. Resistance measuring unit .................................................................................. 57 3.2.2. Temperature control unit .................................................................................... 57 3.2.3. Conditions of measurements .............................................................................. 58 4. Results and discussion ...................................................................................................... 66 4.1. Static operation mode ................................................................................................ 66 4.1.1. Sensor arrays based on commercial gas sensors ................................................ 66 4.1.2. Sensor arrays based on thin films ....................................................................... 71 4.1.3. Sensor arrays based on nanopowders ................................................................. 79 4.1.4. Summary ............................................................................................................ 80 4.2. Dynamic operation mode........................................................................................... 80 4.2.1. Sensor arrays based on commercial gas sensors ................................................ 80 4.2.2. Sensor arrays based on thin films ....................................................................... 96 4.2.3. Sensor array based on nanopowders ................................................................ 100 4.2.4. Power consumption .......................................................................................... 118 4.2.5. Summary .......................................................................................................... 120 5. Conclusions .................................................................................................................... 121 6. References ...................................................................................................................... 125 7. Appendix A – Resistance measuring unit – design ........................................................ 135 8. Appendix B – Temperature control unit – design .......................................................... 140 9. Appendix C - Temperature control unit used with nanomaterials – design ................... 146 Authors publications .............................................................................................................. 151. 5.

(6) Streszczenie Przedmiotem rozprawy są systemy detekcji i rozpoznawania gazów na bazie nanosensorów. Celem pracy było opracowanie systemów detekcji gazów wykorzystujących matryce sensorów gazu na bazie tlenków metali. W pracy wykorzystano korelację pomiędzy temperaturą pracy sensora i jego właściwościami tj. czułością, selektywnością i odpowiedzią na różne gazy. W tym celu temperatura sensora była zmieniania według określnego profilu, co umożliwiło wykorzystanie różnych właściwości sensorowych w różnych zakresach temperaturowych. W celu określenia stężenia gazu i składu atmosfery gazowej, zastosowano cyfrowe metody przetwarzania sygnałów oraz algorytmy rozpoznawania wzorca. W pracy opisano pięć różnych zaprojektowanych i skonstruowanych systemów detekcji gazu opartych na bazie matryc zbudowanych z półprzewodnikowych sensorów gazu. Zostało skonstruowane i przebadane pięć rodzajów matryc składających się z sensorów komercyjnych oraz sensorów na bazie nanokrystalicznych cienkich warstw i nanoproszków. W budowie matryc wykorzystano półprzewodnikowe sensory o typie przewodnictwa n i p. Skonstruowane matryce zbudowane z czujników komercyjnych stanowiły referencję dla badań z wykorzystaniem matryc składających się z nanosensorów. Dzięki tym wynikom, stworzono system na bazie rezystancyjnych nanosensorów gazu umożliwiający analizę składu mieszanin gazowych. W ramach pracy zaprojektowano i skonstruowano elektroniczne układy kontrolnopomiarowe umożliwiające zapewnienie odpowiednich warunków pracy sensora oraz pomiar ich odpowiedzi. Pomiary odpowiedzi sensorów na różne koncentracje gazów zostały wykonane w specjalnie zestawionym stanowisku pomiarowym, które umożliwiało przeprowadzanie badań przy różnych poziomach wilgotności badanej mieszaniny gazów. W pracy badano dwa rodzaje trybu pracy sensora. Pierwszy tryb pracy sensora zwany statycznym polega na tym, że temperatura pracy sensora jest stała przez cały okres wykonywania pomiarów. W drugim trybie pracy sensora zwanym dynamicznym, temperatura pracy czujnika jest zmieniana według ustalonego profilu temperaturowego z zadaną częstotliwością. Celem pomiarów statycznych jest określenie podstawowych właściwości gazoczułych sensora w określonych temperaturach. Wyniki pomiarów statycznych zostały wykorzystane do odpowiedniego dobrania profili temperaturowych wykorzystywanych w pomiarach dynamicznych. W pracy badano odpowiedzi sensorów na różne koncentracje H2, NO, CH4, C3H8 i NH3 a pomiary zostały wykonane przy różnych poziomach wilgotności. Dynamiczne odpowiedzi sensów były analizowane z wykorzystaniem metod przetwarzania sygnałów takich jak Transformata Fouriera oraz algorytmów rozpoznawania wzorca takich jak. 6.

(7) analiza głównych składowych (PCA) czy sieci neuronowe, co umożliwiło określenie składu atmosfery gazowej. Uzyskane wyniki wskazują, że czujniki na bazie nanomateriałów mogą pracować w niższych temperaturach pracy w porównaniu do czujników komercyjnych. Ponadto zastosowane techniki modulacji temperatury i przetwarzania odpowiedzi dynamicznej umożliwiają określenie składu atmosfery gazowej.. 7.

(8) Abstract This Ph.D. thesis is focused on nanosensor systems for gas detection and recognition. The aim was to develop a gas detection systems based on arrays of metal oxide gas sensors. Moreover, the correlation between the gas sensing properties i.e. sensitivity, selectivity and response to various gases and the sensors operating temperature was exploited by inducing a certain operating temperature profile in order to improve the sensors performance. Digital signal processing methods and recognition of reference algorithms were used to process the sensors responses, to determine the target gas concentration and the composition of gas atmosphere. In this work five developed systems, all based on arrays of metal oxide gas sensors, have been described. Five types of sensors including commercial sensors, nanocrystalline thin film sensors and sensors based on nanopowders were used. Moreover, n and p type semiconductors were studied. The arrays based on commercial metal oxide gas sensors served as a reference for further development of systems utilizing thin film and nanopowder sensors. Finally, a gas detection system based on metal oxide nanosensors capable of analyzing composition of a gas mixture, has been constructed. Dedicated electronic measurement and control systems have been designed and constructed in order to provide appropriate operating conditions to enable the measurements of the sensors responses. An experimental setup facilitating measurements at various gas compositions and different humidity levels has been assembled. The sensors performance was studied in two operating modes. In the first mode (static) the sensors operated at a given temperature. In the second mode (dynamic) the operating temperature of each sensor was modulated according to a strictly defined temperature profile. The aim of the measurements in the static mode was to determine the basic sensing properties, which were exploited in the dynamic operating mode in order to ultimately improve the sensor performance. Sensor responses were recorded upon exposure to various concentrations of H2, NO, CH4, C3H8 and NH3. Moreover, the influence of humidity on the sensor responses was studied. The sensor responses have been analyzed by digital signal processing methods which included Fast Fourier Transform. Then, pattern recognition algorithms such as PCA and artificial neural networks have been applied do predict the gas atmosphere composition. The results indicate that sensors based on nanomaterials operate at lower temperatures compared to commercial sensors. The applied methods of temperature modulation and processing of dynamic sensor responses enable determination of gas atmosphere composition.. 8.

(9) 1. Introduction Gas sensors are used for detection and monitoring of wide variety of gases, vapors and odors. The most important application fields of gas sensors are the automotive industry where there is a need to detect hydrocarbons, NOx, O2, NH3, SO2, O3 and other gases [1][2]. Also, an important application field is the food industry, where gas sensor can be used during the production process or to evaluate the freshness and quality of food [3]. Moreover, gas sensors are also used in indoor and outdoor detection of combustible gases [4] and volatile organic compounds [5]. Furthermore, gas sensors are also used in medical applications for detection of bacteria [6] and diseases like diabetes [7]. Finally, gas sensors can also be applied for the detection of explosives [8] and drugs [9]. It is possible to examine selectively and accurately gas components in a laboratory environment with the use of conventional techniques like IR or UV-Vis spectroscopy, mass spectroscopy or gas chromatography. Although, these approaches are limited by instrument complexity, costs, large instrumentation size and laboratory conditions [1]. On the other hand, solid-state gas sensors can be used in low-cost mobile applications. Different transduction principles exist which include: calorimetric sensors, electrochemical cell (e.g. lambda probe), gas-sensitive field-effect transistors, acoustic wave sensors, quartz microbalances and resistive gas sensors based on metal oxides [1][10]. In each case the sensor transforms the chemical information into an analytically measurable signal. Resistive-type gas sensors based on semiconducting metal oxides have attracted attention of many scientific groups and have been studied in the past years [11]. The discovery of the metal oxide – gas reactions that are the basic operating principle of these sensors has been done by Heiland [12], Bielanski et. al. [13] and Seiyema et al. [14]. The first commercial metal oxide gas sensor was constructed by Taguchi [15]. Since then, enormous research has been conducted in the field of metal oxide gas sensors. The main advantages of metal oxide gas sensors are: high sensitivity, wide range of detectable gases, large number of potential applications and simple construction [11]. However, metal oxide sensors still suffer from significant disadvantages that limit their use to rather simple applications. Those drawbacks include lack of selectivity, cross-sensitivity to interfering gases, dependence on environmental humidity and high operating temperatures [11]. Resistive type metal oxide gas sensors have high sensitivity and can detect a wide range of oxidizing and reducing gases but require high operating temperatures [16] which leads to a high power consumption. Currently, major research is directed towards development of gas. 9.

(10) sensors based on nanostructured metal oxide materials operating at room temperature [17]. The main drawback of this type of sensors is lack of selectivity what means that a sensor responds not only to a target gas but also to other interfering gases. Attempts to increase the selectivity of metal oxide gas sensors include the construction of a sensor by applying chemical filters and selecting a suitable operating temperature range. A different approach, is a construction of a sensors array combined with processing of a dynamic sensor response with suitable algorithms [18]. In fact, the lack of selectivity of metal oxide gas sensors is exploited in electronic noses. These devices can detect many chemical species and are used for assessment of food freshness, detection of bacteria, environmental monitoring, detection of explosives, etc. The main advantages of these devices compared to the analytical methods like gas chromatography, is their relatively low price and capabilities to produce portable miniaturized devices. The main goal of the gas detection system is to give the user a quantitate information about the composition of gas atmosphere pertaining the presence of one or several specified chemical components. In this case, the presence of other gases or chemical is relevant from the user perspective. To obtain this quantitative information, the resistance signal from multiple sensors has to be processed. The aim of this thesis is to develop gas detection systems based on arrays of metal oxide gas sensors. Five systems, all based on metal oxide gas sensors, have been developed. Sensor arrays were based on commercial gas sensors, nanocrystalline thin film sensors and nanopowders. Two sensor arrays based on commercial gas sensors served as references for further development of systems based on thin film and nanopowder sensors. To facilitate a proper operation of the sensor arrays, several measuring chambers have been designed and constructed. To enable measurements of the sensors responses and provide appropriate operating conditions for the metal oxide gas sensors, dedicated electronic measurement and control embedded systems have been designed and constructed. Finally, an experimental setup for measurements at various gas compositions and different humidity levels has been assembled. The measurements of the sensors responses have been carried out in a static operating mode and upon temperature modulation. The dynamic sensor responses have been processed by digital signal processing methods and pattern recognition algorithms have been applied to predict the composition of gas atmosphere. Finally, a gas detection system based on metal oxide nanosensors capable of analyzing composition of a gas mixture has been constructed. The results indicate that sensors based on nanomaterials operate at lower temperatures compared to commercial sensors. The applied methods of temperature modulation and processing of dynamic sensor responses enable a gas detection. 10.

(11) The aims of the PhD thesis have been formulated as: . The design and development of a gas detection analysis system based on resistive-type semiconductor metal oxide gas sensors.. . Highlighting the correlation between the operating temperature of a metal oxide gas sensing layer and the gas sensing properties i.e. sensitivity, selectivity and response to various gases.. . The use of digital signal processing methods and algorithms in order to determine the target gas concentration and the composition of gas atmosphere.. . The main hypothesis of this work was to prove that array of gas sensors based on nanomaterials can operate at lower temperatures compared to commercial gas sensors. The scope of work is graphically illustrated in Fig. 1.. Scope of this work gas sensor arrays. measuring chambers. electronics & experimental setup. data processing & analysis. commercial. nanomaterials Fig. 1. Block diagram indicating the scope of the PhD thesis.. The first part of this work was the design and construction of the gas sensor arrays. Five types of arrays were constructed comprised of commercial gas sensors and nanostructured metal oxide gas sensors based on thin films and nanopowders. Each sensor array had to operate inside a specially designed and constructed measuring chamber. To enable measurements of the sensor responses and provide appropriate operating conditions for the sensors, dedicated interfacing electronic devices were designed and constructed. Also, a whole experimental setup facilitating the measurements at various gas atmosphere compositions had to be constructed. Finally the measured responses and the aquired data had to be analyzed and processed. This was done in Matlab software with the use of scripts written by the Author of this PhD thesis.. 11.

(12) Chapter 2 presents the state of the art on metal oxide gas. The operating principles of those sensors as well as the basic parameters and operating modes are described in section 2.1. The nanomaterials used for resistive-type metal oxide gas sensors are presented in section 2.2. Finally, the idea of the sensor array as well as the signal processing methods and patter recognition algorithms used to analyze the data from the sensor array are described in section 2.3. Chapter 3 describes the experimental setup used in this work. The details about the constructed arrays are given in section 3.1. The constructed electronic measuring and control systems and the assembled experimental setup are presented in section 3.2. Moreover, this section presents also the conditions upon which the sensors responses were measured. The results of the measurements are presented in chapter 4. Sensor responses at constant temperature for arrays based on commercial gas sensors and sensors based on thin films and nanopowders are presented in section 4.1. Next, the section 4.2 presents the dynamic sensor responses upon various temperature profiles and methods used to determine the gas atmosphere composition. Finally, the details about the design and of the resistance measuring and temperature control units are given in Appendix A-C.. 12.

(13) State of the art. 13.

(14) 2. Resistive-type metal oxide gas sensor Resistive type metal oxide gas sensors are a group of chemical sensors which can detect reducing and oxidizing gases [19]. For this type of sensors, the resistance changes upon exposure to a gas compose the output signal. Details about the resistive type metal oxide gas sensors will be provided in the following sections.. 2.1.. Operating principle and basic parameters. To get an insight into metal oxide gas sensor properties and response to gas species one has to understand the sensor working principle. The basic sensor output signal is resistance change which is a result of an ionosorption process and is explained as a transfer of the free charge carriers (electrons) from the semiconductor to the species adsorbed at the surface [11]. This adsorption process is strongly influenced by the presence of the pre-adsorbed species which include ionosorbed oxygen, hydroxyl groups, carbonates, etc [11]. Therefore, measurement of the sensor resistance upon exposure to the target gas delivers only the overall electrical effect of complex surface reactions. Resistive metal oxide gas sensors usually operate at elevated temperatures (200 – 500°C) in the atmospheric air, in the presence of environmental humidity and interfering gas species [11]. Depending on the conducting type, metal oxide materials can be classified into two groups based on n-type and p-type semiconductors. Various metal oxides gas sensors have been used to detect oxidizing and reducing gases. However, the most representative and widely explored sensor materials include SnO2, TiO2, ZnO, WO3, In2O3, Fe2O3 which exhibit n-type semiconductivity [20]. On the other hand, metal oxide materials of p-type semiconductivity such as NiO, CuO, Co3O4, Cr2O3 and Mn3O4 have recently receive more attention [20]. The gas molecules interact with the sensor by physisorption and chemisorptions. Physisorption is a process in which gas molecules interact with the metal oxide surface by van der Waals forces. During this process, there is no chemical process involved resulting in the electrone exchange between the gas molecule and the sensor surface. Therefore, this proces does not change the conductivity of the metal oxide material. Physisorption occurs at low temperatures and is strongly dependent on temperature, partial pressure and the gas concentration. The change in any of these factors may lead to the desorption of the gas molecules from the surface of the metal oxide material. Physisorption is a relatively fast and reversible process. Chemisorption is a process in which electrons are exchanged as a result of a chemical reaction between the gas molecule and the metal oxide material. This leads to the change of the conductivity of the sensing material. Chemisorption requires elevated. 14.

(15) temperatures bacause it is a proces with a high activation energy. Chemisorption is process which forms foundations of the operation principle of gas sensors based on metal oxides. The gas interaction with the sensor surface can be divided into two steps which include the adsorption of an oxidizing gas and its desorption upon exposure to a reducing gas. Any ntype metal oxide sensing material exhibits an increase in its electrical resistance upon exposure to oxidizing gases such as O2 and a decrease upon exposure to reducing gases such as H2, CH4, C3H8, CO etc. [21]. In order to understand the process, one has to realize that at elevated temperatures the chemisorption of oxygen species including O2-, O-, O2- on the metal oxide material surface occurs. The process of oxygen chemisorption can be described by the following reactions [22]:. O2 ( gas)  O2 (ads) . O2 ( ads )  e  O.  2. O2  e   2 O  . . (1) (2) (3). 2. O e  O (4) Depending on the temperature the oxygen ions O2 are stable below 100°C, O ions are stable within a temperature range between 100°C and 300°C, while the O2- ions are stable above 300°C [23]. Desorption of the oxygen ions occurs at temperatures of 80, 130, and 250°C respectively [22]. The gases which can be detected by the metal oxide gas sensors are divided into two groups based on their oxidizing or reducing properties. When the n-type metal oxide sensing material is exposed to reducing gases such as H2, CH4, C3H8 or NH3 the adsorbed oxygen ions are donated to the metal oxide material according to the reactions presented in Table 1.. 15.

(16) Table 1. Reactions occurring at the surface of the metal oxide for reducing gases [22]. Detected gas. H2. CH4. C3H8. NH3. Preadsorbed oxygen species. Reaction. O2-. 2 H 2  O2 ( ads )  2 H 2O  e . O-. H 2  O  (ads)  H 2 O  e . O2-. H 2  O 2 (ads)  H 2 O  2e . O2-. CH 4  2O2 ( ads )  2 H 2O  CO2  2e . O-. CH 4  4O  (ads)  2H 2 O  CO2  4e . O2-. CH 4  4O 2 (ads)  2 H 2 O  CO2  8e . O2-. C3 H 8  5O2 ( ads )  4 H 2 O  3CO2  5e . O-. C 3 H 8  10O  ( ads )  4 H 2 O  3CO 2  10e . O2-. C3H8 10O2 (ads)  4H2O  3CO2  20e. O2-. 4NH3  3O2 (ads) 2N2  6H2O3e. O-. 2NH3  3O (ads)  N2  3H2O  3e. O2-. 2NH3  3O2 (ads)  N2  3H2O  6e. As results from the reactions given in Table 1, a decrease in the sensor electrical resistance is observed. A p-type metal oxide material reacts in an opposite way, i.e. it will decrease its electrical resistance upon exposure to oxidizing gases and increase its resistance when interacting with the reducing gases. A metal oxide material at a microscopic level is comprised of a lot of small grains. It is generally accepted that the chemical reactions can occur at the grain surface, in the bulk, at grain boundaries and at electrode-material interface. The overall effect of all those chemical reactions will result in the change of the sensor electrical resistance. The rate and the number of this reactions affects the response time and signal. Therefore, the sensing material plays a receptor and transduction function by interacting with the target gas analyte and transforming this chemical information into a measurable signal in a form of resistance changes (Fig. 2) [24].. 16.

(17) H2 -. O. -. O. H2O O. (a) Grain surface (Receptor function). (b) Material microstructure (Transducer function). (c) Sensing element (Output resistance change). Fig. 2. Receptor and transducer function of a semiconductor gas sensor (adopted from [24]).. As depicted in Fig. 2, the surface of the metal oxide interacts with the gas molecules performing a receptor function (Fig. 2 a). Chemical reactions at the surface lead to the conduction change of the whole sensing material and therefore, performing the transducer function changing one physical value i.e. gas concentration to a conductance (Fig. 2 b). The sensing layer with the electrode enables the output signal redout as changes in the sensor resistance (Fig. 2 c). For a semiconductor with an n-type conductivity when oxygen is ionosorbed at the surface it acts as an electron acceptor extracting electrons from the conduction band EC. As a result an electron-depleted region (Λair) is formed at the surface as presented in Fig. 3. The negative charge trapped in these oxygen species leads to an upward band bending and thus a reduction of the material conductivity [25].. bulk. gas. surface. eVsurface. O2. EC EF. O-surface Λair. EV Fig. 3. Representation of the flat band and band bending model illustrating adsorption at the surface of n-type semiconductor (adapted from [1]).. 17.

(18) Variations in the band bending caused by reactions with oxidizing gases (e.g. O2) and reducing gases (e.g. H2) lead to changes in the overall resistance of the metal oxide sensing layer. As presented in Fig. 3 as a result of adsorption of oxygen and extraction of electrons from the conduction band, an electron-depleted region is formed at the material surface [1]. This electron-depleted region is so called space-charge layer and its thickness (Λair) is related to the length of band bending region [16] what is schematically presented in Fig. 4.. space-charge layer. H2. H2O. structural model. O2. band model. Schottky barrier. Fig. 4. Structural and band models of a polycrystalline metal oxide semiconductor at the initial state (a) and effect of H2 for large grains (b) (adapted from [1]).. As shown in Fig. 4, in a polycrystalline metal oxide material, grain to grain contacts form percolation paths affecting the material conductivity. Therefore, the conductivity depends on the height of the Schottky barrier formed between grains. Upon exposure to reducing gases such as hydrogen, the adsorbed oxygen ions are removed from the surface. As a result, the trapped electrons are released back to the bulk what leads to the reduction in the thickness of space-charge layer. Then, the Schottky barriers between grains are lowered and it is easier for electrons to conduct through different grains, what leads to a higher conductivity of the sensing material. As presented so far, the response of a metal oxide gas sensor is highly dependent on the surface reactions. Therefore, the material with a higher surface to volume ratio should present better response properties. Assuming that the gas sensing layer is comprised of spheroidal grains, each with a radius r, the surface area A and the volume V of a grain are given as: A  4 r 2. V . 18. 4 3 r 3. (5) (6).

(19) Therefore, based on Eq. (5) and (6) the surface to volume ratio (A/V) is: A 1  V r. (7). As one can see from equation (7) as the grain radius decreases the surface to volume ratio (A/V) increases. Therefore, if the material has a higher surface to volume ratio, it enables to enhance the adsorption of gas molecules at the surface of metal oxide what should improve the gas sensing performance. Nanomaterials are a group of materials which offer a decreased particle size and an increased specific surface. As presented in Fig. 4, the decrease in the grain radius r will lead to a flattening of the Schottky barrier when the grain radius is comparable with the space-charge layer [1]. As a result, the depleted zones will overlap and the material electrical properties will be predominantly determined by the surface states [1]. In nanomaterials, when the radius of the grain is smaller than the depleted zone the whole grain is depleted (Fig. 5). Thus, metal oxide nanomaterials are expected to exhibit better sensing properties compared to microcrystalline materials. The application of nanomaterials as gas sensors is described in section 0.. O2. b). H2. H2O. structural model. a). band model. percolation path. Fig. 5. Structural and band model for the so-called “flat-band” state in nanomaterials, initial state (a) and effect of H2 on the conduction band EC position (b) (adapted from [1]).. The so-called flat band state in nanomaterials is when Schottky barriers fall below thermal energy (eVs ≤kBT) [1]. As a result, the conductance is now proportional to the difference between the Fermi level EF and conduction band bottom Ec [1]. The operating temperature of the gas sensing layer of a metal oxide gas sensor is a key factor influencing the chemical reactions occurring at the sensor surface. Sensor resistance change and response are highly dependent on the gas type and are different at each temperature.. 19.

(20) Examples of conductance changes upon exposure to various target gases are presented in Fig. 6 [26].. H2. H2. H2. C3H8 C3H8 CO H2. C3H8. C3H8. CH4 CO. CH4. CO. CH4. CH4 CO. ` Fig. 6. Examples of conductance changes of metal oxides upon exposure to different gases where: G0 is the conductance of the sensor in the reference atmosphere (air), G is conductance upon reaction with a target gas and k is the scaling factor. Gas concentrations are 0,8% H2, 0.5% CH4, 0.2% C3H8, 0.02% CO in air. Figure adapted from [26]. As presented in Fig. 6 all the sensors respond the studied gases. As one can observe in Fig. 6 the maximum conductance change for certain gases is located at specific temperatures. Moreover, by adding dopants like Pt, Pd and Ag the selectivity and sensitivity of a metal oxide gas sensor can be adjusted as depicted in Fig. 6. During the operation of a metal oxide gas sensors, its operating temperature can be either kept constant or change in time. Therefore, in terms of the operating temperature two types of operating modes exist [27]. In the first operating mode, called the static operating mode, the gas sensing layer operates at a constant temperature. In the second operating mode, the temperature of a metal oxide gas sensing layer is changed in a given way, periodically during the sensor operation. The second type of operating mode is called dynamic mode or just temperature modulation. The following sections will provide details about each operating mode.. 2.1.1. Static operating mode In a static mode of operation, a metal oxide gas sensor operates at a constant temperature. This is often achieved by applying a constant voltage to the sensor heater [28] [29] (for example for the Figaro TGS26xx sensor series it is 5V [30]). In this case, the sensor temperature may change a little due to fluctuations of ambient temperature or changes in the gas flow rate around the sensors. The method ensuring that the sensor always operates at a given constant temperature is to measure the sensor operating temperature and adjust the heater supply voltage [31][32]. This approach is more complex but provides that ambient temperature. 20.

(21) or gas flow rate changes have no influence on the sensors operating temperature. Under such conditions the sensor resistance is only dependent on the composition of the gas atmosphere. Therefore, changes in the concentration of the target gas lead to changes in sensor resistance. Example of the changes of the sensor resistance upon various hydrogen concentrations are presented in Fig. 7. The main problem in this approach is that similar changes in the sensor resistance can be induced by several factors such as: target gas concentration, change in the concentration of other interfering gas or change in the humidity level. Therefore, the operating temperature of a sensor is selected to maximize the resistance change to the target gas and to reduce the influence of the interfering factors on the sensor response.. 2.1.1.1.. Response. In general, the response ξ of a gas sensor is defined [33] as a function of all variables comprising the pressure pj of each component of a gas mixture and temperature T:.  ξ dξ =    j  p j.  ξ dT  dp j + T  Pi j. (8). In the case of resistive-type metal oxide gas sensors, operating under a constant temperature, introduction of a target gas results in the sensor resistance change. Typical resistance changes for an n-type semiconducting metal oxide upon exposure to target gas are presented Fig. 7.. 21.

(22) air. air. target gas. R0. R0. resistance. τ90% Rec. ΔR 90%ΔR. 90%ΔR. R. τ90% Res gas concentration. time. time Fig. 7 Example of resistance changes of a metal oxide gas sensor upon exposure to a target gas.. Based on such resistance changes as presented in Fig. 7, the response of a metal oxide gas sensor can be defined. However, there is no generally accepted definition of gas sensor response and several definitions are used [34]. Usually, the response S of a n-type metal oxide gas sensor is defined as [31][34]:. S. R R  R0  R0 R0. (9). where R0 is the resistance of the sensor in the reference atmosphere (air), ΔR is the absolute resistance change and R is the sensor resistance upon exposure to the target gas. R0 is also called the sensor baseline resistance. For p-type sensors the response S is defined as:. S. R R  R0  R R. (10). Another definition of response S of a metal oxide gas sensor used in the literature [32] [34] for n-type sensors is:. S. R R0. (11). S. R0 R. (12). and for p-type sensors is:. 22.

(23) In the dynamic operating mode, the resistance change of the sensor over a certain period of time is called the sensor response. However, for the sensors operating in a dynamic mode no generally accepted response definition exists.. 2.1.1.2.. Response and recovery time. The response and recovery times are defined as the time needed for the electrical resistance of a sensor to reach a certain value upon an abrupt change in gas concentration [27]. This is usually 90% of the final resistance [27]. The graphic illustration on response τ90%Res and recovery time τ90%Rec is presented in Fig. 7. As shown in Fig. 7, the sensor resistance in the reference atmosphere (air) R0 (baseline) after exposure to a certain target gas changes to R. Time needed for the sensor resistance to reach the value of 90% of R is the response time (τ90%Res). Similarly, after removing the target gas, the time needed for the sensor resistance to change from R to 90% of R0 is called the recovery time (τ90%Res).. 2.1.1.3.. Sensitivity. For a metal oxide gas sensor, the sensitivity ξ to a particular target gas χ is defined as [35]: S (13)  where: S is the sensor response and χ is the target gas concentration. Fig. 8 presents a typical. . response S. response of a metal oxide gas sensor.. almost linear nonlinear. saturation. gas concentration Fig. 8. Example of a metal oxide gas sensor response characteristics.. As presented in Fig. 8, the typical gas sensor characteristic is divided into following regions: . Almost linear – in this region the relation between the sensor response and the gas concentration can be approximated by a linear function with a relatively small. 23.

(24) approximation error. This is the region of relatively high and constant sensitivity ξ. The accuracy of the sensor is the best because small changes in the gas concentration result in significant changes in the sensor response. . Nonlinear – in this region the sensor response cannot be approximated by linear function. Therefore, the sensitivity ξ changes and is rather small.. . Saturation – in this region a large increase in the target gas concentration results in a relatively small increase in the sensors response. Therefore, the sensitivity ξ is low and the accuracy of the sensor is very small. 2.1.1.4.. Selectivity. The ability of a sensor to detect a gas without being affected by other interfering factors, e.g., other gases, humidity etc., is called selectivity. According to Eq. (8) selectivity indicates that only one of different partial sensitivities. ξ pj. is large as compared with others. The sensor. which is specific (selective) enables to determine quantitatively the concentration or the partial pressure pj of a target gas j in the presence of others. It is well-known that metal oxide gas sensors are non-specific in general [36]. Upon exposure to a mixture of a target gas and interfering gases metal oxide gas sensors exhibit a strong cross-sensitivity resulting from the lack of specificity to a certain gas component. However, the selectivity can be increased to a certain extent through a variety of techniques [37] which can be divided into two groups [38]. The first group is focused on the sensing material properties and the sensor construction. The techniques included in the first group are: . suitable structure and doping of the metal oxide material,. . application of chemical filters,. . adjusting the temperature of operation (Fig. 6).. The second group represents a completely different approach which exploits the complexity of gas-solid interactions and includes techniques of: . temperature modulation,. . transient response analysis,. . building of the sensor arrays.. Metal oxide gas sensors usually operate in atmospheric air containing various amounts of water vapor [11]. The relative humidity of the surrounding atmosphere varies from 10% to 100% [39] what can substantially influence the sensor response. Therefore, the environmental. 24.

(25) humidity is considered to be an important factor affecting the gas sensing performance of metal oxide gas sensors [40]. The results of the extended research on the adsorption of water on metal oxide surfaces indicating the importance of humidity influence on the sensor parameters, can be found in [41][42][43][44][45][46][47]. The hydroxyl groups forming at the surface can decrease or increase the sensors resistance depending on the electron transfer reactions. In general, the influence of water vapor on sensing properties of metal oxide gas sensors depends on the sensing material, target gas, doping and results mostly in inhibition and sometimes in enhancement of sensing. In most cases, water molecules adsorbed at the metal oxide material surface will lower the sensor sensitivity and its baseline resistance as explained in [48][49]. Moreover, the adsorption of water molecules decreases the chemisorption of oxygen on the metal oxide material surface [16]. In other words, there is a competitive adsorption between O2 and H2O related surface species, what results in different sensing properties in dry and wet atmospheres as shown in [50][51]. Also, adsorbed water molecules act as a barrier against adsorption of other reducing and oxidizing gases what leads to a decrease of sensitivity and response as well as an increase in the recovery times. In the literature one can find three types of mechanisms explaining the increase in the surface conductivity in the presence of water vapor [52][53]: 1) The first mechanism takes into account dissociation of H2O molecule and bonding with lattice oxygen OO as well as titanium at lattice site TiTi according to the following reaction [52]: . H 2O  TiTi  OO   OH  TiTi    OH O  e As a result, an additional conduction electron e- is created.. (14). 2) The second mechanism considers dissociation of H2O molecule into OH group and a proton. The proton reacts with lattice oxygen forming another OH group and creating an oxygen vacancy VO. Both OH groups bind to titanium lattice site and the oxygen vacancy. The additional electrons 2e- will be produced as a result oxygen vacancy VO2+ ionization [54] [55]. This mechanism can be summarized by the following reaction [52]:. H 2O  2TiTi  OO  2  OH  TiTi   VO2  2e  (15) 3) The last mechanism considers an indirect effect which consists in the interaction between hydroxyl group or the hydrogen atom originating from the water molecule and basic or acidic groups present at the surface. As a result of these mechanisms the creation of quasi - free electrons is observed. In the case of n – type sensors, the increase in the electron concentration improves the electrical 25.

(26) conductivity. Moreover, due to the adsorption of water molecules, the effective sensing area decreases what leads to the response and sensitivity decrease. Furthermore, the formation of hydroxyl groups as well as reaction with the reducing gas both lead to the decrease in the sensors resistance. It has been shown in [16][56] that environmental humidity is an important factor affecting the performance of metal oxide gas sensors. In some applications, such as detection of formaldehyde [57] the unavoidable interference of water vapor can be reduced by an array consisting of two sensors only [57]. In such an array the chosen sensors should have significantly different responses to the analyte and the same responses to interfering water molecules [57]. Similar results described in [58] indicate that identification of solvent vapors in spite of the interfering humidity can be achieved by means of commercial two-sensor array operating under temperature modulation. 2.1.1.5.. Stability and repeatability. The ability of a gas sensor to provide the same results and retaining the basic parameters (response, sensitivity, selectivity, response and recovery times) over a certain period of time is called stability [59]. On the other hand, the ability to reproduce the results of measurements (sensor response) upon the exposure to a defined gas concentrations is called repeatability. A good sensor should be stable and reproducible over a long period of time. Two types of stability can be distinguished [59]. The first type of stability is related to retaining the sensor gas sensing properties over a defined period of time under certain operating conditions (e.g. given operating temperature) [59]. Second type of stability can be related to retaining the gas sensing properties of a sensor over a certain period of time under defined storing conditions (e.g. room temperature and ambient humidity) [59]. These, stabilities are called active and conservative stabilities respectively [59].. 2.1.2. Dynamic operating mode It is well known that resistive-type metal oxide gas sensors are only partially selective as a consequence of their sensing mechanism [11]. Their response is affected by many chemical compounds present in the gas atmosphere [11]. Therefore, it is better for metal oxide gas sensor to operate in a so called dynamic mode [61]. In this mode, the operating conditions of the sensor are not constant but change in a defined way. Two basic approaches include a change in the gas flow rate [62] or a change of the sensors operating temperature [60]. The first approach is focused on exploiting the dynamic features of a transient response [62] of a sensor operating at a constant temperature but exposed to a change in the gas atmosphere composition. 26.

(27) [63][64][65][66]. On the other hand, the second approach exploits different sensing properties of a metal oxide gas sensor at various operating temperatures and is called temperature modulation [60]. The advantage of these two techniques is that they do not involve any modifications in the sensor construction and can be applied to any metal oxide material. Also, a significant advantage of temperature modulation is that the average temperature of a sensor is lower as compared to a sensor operating at a constant temperature what results in an overall reduction of power consumption [60][67]. This temperature modulation technique has been used in this thesis. Several advantages of modulating the temperature of a metal oxide gas sensor have been suggested in the 80ties [68][69]. A unique signature for each gas can be obtained by cyclic temperature variation due to various rates of reaction of different analyte gases. Also, periodic temperature changes from low to high, lead to removing the surface contaminants accumulated at low operating temperatures. Moreover, cycling the operating temperature exploits a specific response pattern within a given temperature range as presented in Fig. 6 [38]. Usually, the analysis of a sensor response obtained upon temperature modulation is carried out with signal processing techniques and pattern recognition techniques to achieve gas identification, classification and quantification [60]. In general, temperature modulation exploits the fact that the gas sensitivity characteristics of metal oxide semiconductor gas sensor and the response times to different gases are different under various working temperatures [60][38]. Moreover it was presented in [70][71] that the kinetics of the gas–surface interactions i.e. adsorption and desorption are strongly temperature dependent. Therefore, even small operating temperature variations can lead to considerable variation in sensitivity and response time of a metal oxide gas sensor [39]. As an example, the sensor response dependence on temperature and hydrogen concentration is illustrated in Fig. 9.. 27.

(28) Fig. 9. Response of a TGS2600 gas sensor as a function of hydrogen concentration and operating temperature.. As one can see in Fig. 9, the sensor response to a target gas is different at each operating temperature. Therefore, the application of the temperature modulation of a single sensor corresponds to a virtual array of sensors, each with different sensing properties [72]. As a result of modulation of the operating temperature, the response of a metal oxide gas sensor is dependent on: . the temperature modulation profile,. . various reaction rates at various temperatures,. . different chemical reactions that occur at the sensor surface which are dependent on operating temperature.. As presented in [38] a significant aspect of the temperature modulation is the sensor response hysteresis when the sensor operating temperature is modulated [73]. However, it was reported [74] that the sensor response hysteresis is reduced when the rate of temperature change is slower. Research on identification of the physical mechanisms responsible for the dynamic metal oxide sensor response upon temperature modulation was carried out by Clifford and Tuma [74]. Later, Nakata et al. added a chemical reaction mechanism to the model and discussed the chemical adsorption and reaction mechanism on the material surface [75]. The simulation of the sensor dynamic response upon sinusoidal temperature modulation was performed by Ionescu et al. [76]. More details concerning gas-solid interactions occurring between the metal oxide material and the gas atmosphere upon temperature modulation can be found in [77][78]. So far, the responses of sensors operating in the dynamic mode have been successfully utilized in [18][79][80][81][82] to improve the metal oxide sensors performance.. 28.

(29) The temperature modulation can be achieved by cycling of a heater voltage with a defined voltage profile [68][69] or by measuring, controlling and then imposing a strictly defined temperature profile [83][91]. The second method is more accurate and reproducible. Also, the sensor operating temperature can be measured by an external temperature sensor (e.g. thermocouple or infrared thermometer) [38] or it can be derived from the resistance of the sensor integrated heater [83]. A precise and repeatable temperature modulation is an important issue especially when more sophisticated temperature modulation profiles are used. Several different profiles of temperature modulation can be found in literature, the most typical profiles are depicted in Fig. 10. a). temperature. temperature. b). time. c). time temperature. temperature. d). time. e). time temperature. temperature. f). time. time. Fig. 10. The most common temperature profiles used in the temperature modulation.. As presented in Fig. 10, the following profiles are the most frequently used: . Rectangular (Fig. 10 a) – to achieve this temperature modulation, the power supply of the sensors has to be just turned on and off [84]. Therefore, this profile is the simplest to impose upon the sensor. Obviously, one has to keep in mind that temperature of the sensor does not change in an ideal rectangular shape due to the temperature constant of the sensors. When keeping a sensor at low temperature is much longer then for the high temperature this temperature profile is may be called a pulsed mode [80] (Fig. 10 b).. . Step changes (Fig. 10 c) – the operating temperature is changed is steps. Usually several temperatures are selected. This operating mode is similar to measuring the sensors static. 29.

(30) response at several selected temperatures. Obviously, it is hard to achieve ideal rectangular step changes in the sensors operating temperature and often only the heater voltage is changed in a step way [85][86][87]. However, to achieve the temperature changes resembling the ideal step changes a very short spike in the heater voltage on the beginning of each step enables the sensor to increase its temperature faster [88]. . Sinusoidal (Fig. 10 d) – the temperature of the sensor is changed periodically in a sinusoidal way. This type of modulation is often used because the sensor temperature can closely follow the heater voltage [38].. . Saw tooth (Fig. 10 e) [89] or triangular (Fig. 10 f) [90] – in this type of temperature modulation periodic linear changes in the temperature are applied in either saw tooth of triangular waveform.. . Other temperature profiles – also other more complex temperature profiles have been studied [67][83]. Although, it is more difficult to generate such temperature profiles and a dedicated control system has to be constructed to induce such profiles [91].. So far, the studies proved the usefulness of thermally modulated responses to qualitative and quantitative analysis of gases and gas mixtures. It has been demonstrated in [79] that combining temperature modulation with feature extraction from an array of metal oxide gas sensors enabled identification and prediction of pollutant species such as acetaldehyde, ethylene and ammonia. It has been presented in [92] that by processing the temperature modulated responses of nanoparticle gas sensors by Fast Fourier Transform (FFT) or by Discrete Wavelet Transform (DFT), the detection of ethanol, H2S, NO2 and their mixtures could be performed. Also, modulating the temperature of four tungsten oxide gas microsensors with a multisinusoidal signal, enabled detection of ammonia, nitrogen dioxide and their binary mixtures at different concentrations [93].. 2.2.. Nanomaterials as gas sensors. Nanostructured metal oxides form a relatively new group of materials used for gas sensing. They offer a decreased particle size and an increased specific surface area, what results in many new chemical and physical phenomena that are not encountered in the case of micrometer-scaled materials [21]. Nanomaterials provide an improved sensor response [24] due to the increased density of centers active for chemisorption [94], a decreased operating temperature [17], a shorter response time and an enhanced sensitivity resulting from the fact that the majority of conduction electrons are trapped at the surface states [95].. 30.

(31) The first improvement in the gas sensing performance of metal oxides by reduction of crystallite size was demonstrated by Yamazoe [96] in 1991. Since then, substantial efforts have been undertaken in order to develop and investigate nanomaterials as gas sensors. Typically, a nanomaterial is a material which has at least one dimension in a naonometric scale i.e. bellow 100 nm [97]. Another definition of a nanomaterial, is when at least one physical property is dependent on the grain size. Nanomaterials can be classified into: zero dimensional, one dimensional, two dimensional and three dimensional [97]. Much work has been performed in order to investigate the effect of reduced particle size on the sensor response. As presented in [24], for sensors based on SnO2, a significant increase in the sensor response to carbon monoxide and hydrogen was observed when the particle size. R0/R. decreased below 10 nm, as presented in Fig. 11.. 800 ppm H2. 800 ppm CO. particle size [nm] Fig. 11. Influence of SnO2 grain size on the sensor response to 800 ppm H2 and 800 ppm CO in air at 300°C adapted from [24].. It was assumed in [24] that when the grain size was below a critical value (below 6 nm), the whole part of each grain was depleted of electrons. Similar, grain size effect was presented in [98] for detection of CO, H2 and CH4 by sensors based on SnO2 powders prepared by sol-gel process. The results indicated that the sensor sensitivity increased with the particle size reduction and that the best performance in terms of fast response and recovery was obtained for the particle size of 8–10 nm [98]. The grain size effect on the sensitivity of metal oxide gas sensors was described in [99]. According to the proposed model [99], presented in Fig. 12, the sensor consists of partially sintered crystallites.. 31.

(32) a) D >> 2L. Neck. Neck. Core region (high conductivity). Neck. D Potential barrier. Grain size. Grain Neck boundary. Adsorbed oxygen ions Conduction channel. b) D ≥ 2L. Potential barrier. Depletion layer L (low conductivity). Potential barrier. c) D < 2L. Fig. 12. Model presenting the effect of the crystallite size on the sensitivity of metal oxide gas sensor for the: grain boundary control (a), neck control (b), grain control (c) adapted from [95].. As presented in Fig. 12, the crystallites are connected with each other by necks and form larger aggregates. This aggregates are connected to their neighbors by grain boundaries. Based on the relationship between the grain size (D) and the width of the depletion layer (L) three different cases can be distinguished [99]. In the case of large grains (D >> 2L) the depletion layer is relatively thin and most of the volume of the crystallites is unaffected by the interactions with the gas occurring at the surface (Fig. 12 a). Therefore, the sensor conductivity is mostly affected by the interactions occurring at the grain boundary barriers between the grain agglomerates. Then, the electrical conductivity depends exponentially on the barrier height [95]. Moreover, the grain boundary. 32.

(33) barriers are independent of the grain size. Therefore, the grain size does not influence the sensitivity [95]. As a result of the grain size decrease, the depletion region extends deeper into the grain core region. When D ≥ 2L a constricted conduction channel is formed in each aggregate by the depleted layer around each neck (Fig. 12 b). The cross section area of the path though the grains is sensitive to the ambient gas composition [21]. As a result, the conductivity depends on the cross section area of those channels and also on grain boundaries. Therefore, the gas sensitivity is enhanced compared to the previous case (Fig. 12 a). Moreover, the grain size affects the sensitivity and the decrease in the grain size (D) results in the sensitivity increase [95]. When D < 2L depleted layer dominates the whole grain and the crystallites are almost fully depleted of mobile charge carriers (Fig. 12 c) [21]. As a result, the energy bands are nearly flat throughout the whole structure of the interconnected grains and there are no significant barriers for intercrystallite charge transport [21]. Therefore, the conductivity is essentially controlled by the intercrystallite conductivity. In this case, only a few charges acquired from surface reactions will lead to large changes of conductivity of the whole structure [21]. As a result the metal oxide material becomes highly sensitive to ambient gas atmosphere.. 2.3.. Sensor array. This section will present the basic aspect and applications of sensor arrays based on metal oxide gas sensor. Array comprised of nonselective gas sensor is the basic part of a device called an electronic nose. Moreover, the signal processing methods and pattern recognition algorithms utilized in electronic noses will be described in this section.. 2.3.1. Definition and functionality The idea of an electronic nose was introduced by Dodd and Persaud as a device mimicking the human olfaction [100]. Significant research in this field has been done by Gardner who proposed a definition of an electronic nose as follows [101]: “An electronic nose is an instrument, which comprises an array of electronic chemical sensors with partial specificity and an appropriate pattern – recognition system, capable of recognising simple or complex odours”. Currently, the electronic nose has become a powerful tool for discrimination of volatile organic compounds (VOCs). However, the commonly used term “electronic nose” is now being disputed by many authors as misleading and inadequate to the real capabilities of those systems and a term “application – specific sensor system” had been proposed as well as a new definition [102]: “An attempt to mimic the principles of smelling that gives another view on the whole scene of volatiles compared to its biological inspiration”. The human olfaction 33.

(34) has developed during a long period of time by evolution. Because of that, it is specific to some odors like spoiled food and rotten meat and is insensitive to chemical substances like toxic gases which did not pose any threat till now. The electronic nose approach is in most cases faster, easier and cheaper than standard measuring methods or human test panels. An increase in interest on electronic noses approach to detect explosives, toxic chemicals and microorganisms has emerged with the global terrorism. There is a wide area of applications where electronic noses can be used, for example to: . classify nature and quality of food and beverages [103][104],. . discrimination of compounds and quality of products (e.g. perfumes [105]),. . classification of fuel and oil for automotive industry [106][107][108],. . direct identification of bacteria [109] and pathogens [110] in medicine,. . detection of explosives materials [8] and toxic substances [111],. . detection and identification of drugs [9]. Typically an electronic nose device consists of: samples processing/extraction part,. control system, sensor array, signal processing and pattern recognition system [103]. In general, the construction of an electronic nose is similar to its biological counterpart what is presented in Fig. 13. The artificial electronic nose system comprises a chemical sensor array with an interfacing electronic circuits, signal processing and pattern-recognition algorithms.. recognition. brain processing olfactory receptors. chemical substance. sensor array. signal processing. pattern recognition. Fig. 13. Block diagram presenting analogies between biological olfaction and artificial electronic nose system. The most important part of the whole system is a sensor array composed of several nonselective gas sensors with nonspecific responses. The sensors used in the array may be 34.

(35) surface or bulk acoustic wave devices (SAW, BAW), metal oxide field effect transistors (MOSFETs), conducting polymers (CP) or standard metal oxide sensors (MOX) [102]. Although many gas sensing principles exist, the most common sensors used in electronic noses are resistive type semiconducting gas sensors based on metal oxides (MOX) [102]. The use of metal oxide sensors in development of electronic noses seems to be attractive because of their high sensitivity, low selectivity and low cost.. 2.3.2. Construction The sensor array can be defined as a set of sensors used for obtaining information in aform of a set of electrical signals about the tested gas sample [112]. In the gas sensor array, several sensors with different broad and partially overlapping sensitivities are typically used [112]. The array converts the information about a gas mixture composed of multiple chemical components to a set of electrical output signals forming a multivariate sensor array response [112]. Metal oxide gas sensor array can be used to classify different complex gas mixture samples. In the design and construction of a sensor array several aspects have to be taken into account: . chemical components which are to be classified,. . sensing properties of the sensors,. . physical dimensions of the array,. . sampling method and processing,. . sensors operating mode,. . signal processing, acquisition and data analysis.. The increase in the number of sensors in the array in some situations may improve the detection capabilities. On the other hand, sensor with no sensitivity to the target gases do not contribute to the discrimination task, increase noise and degrade the overall detection capabilities of the sensor array [112]. However, the redundancy of sensors with the same sensing characteristics leads to the improvements of the detection properties and also reduces the degradation of the sensing in time [113]. Although, redundant sensors do not provide information useful in the discrimination process [112].. 2.3.3. Signal processing Processing of the gas sensor response is one of the key features of any gas detection system. Signal processing of a metal oxide gas sensors can be divided into several steps. The first includes the processing of the measured resistance signal and may include electrical. 35.

(36) conditioning circuits. The aim of this step, often called a signal conditioning is usually buffering, amplification and filtering of the sensor response [114]. Those functions are achieved by electronic circuits based on operational amplifiers. The role of buffering is isolating of different electronic stages and avoiding impedance-loading errors [114]. The role of amplification is to increase the signal to fit the dynamic range of an electronic circuit for example an analog to digital converter. The last stage is filtering of the measured signal in order to remove unwanted frequency components [114]. Electronic circuits can also realize other functions on the processed signal including linearization, integration, differentiation, temperature compensation etc. [114]. The signal processing mentioned so far is performed on the analog electrical signal. Once the signal is processed by an analog to digital converter, the next stage of signal processing can start and all operations are made on a digital signal. Sometimes, besides analog filters, digital filters can used to further process the sensor response signal. In [115] various techniques including Savitzky–Golay smoothing, moving average, local regression and robust local regression filters were evaluated for processing sensor responses. Since the sensor response signal is digitalized, baseline manipulation, compression, and normalization is performed [116]. Three commonly used techniques of baseline manipulation are: difference which subtracts a given baseline from the sensor response, relative in which the sensor response is divided by the baseline and fractional in which baseline is subtracted from and then divided by the sensor response [114][116]. The next stage is compressing the sensor-array response down to a few descriptors to form a feature vector [114]. This part of the processing is often called feature extraction. In general, the feature extraction methods can be divided into three groups [117]. The first group consists of methods extracting specific signal features from the sensor responses. These features are usually maximum values, integrals, differences, derivatives etc. [117]. Second group uses curve fitting to the sensor response and the fitting parameters form a features set. Third group includes methods based on mathematical transforms like Fourier transform (FFT), discrete wavelet transform (DWT). Besides the already mentioned methods, the use of many other feature extraction can be found in the literature [117]. These method are: phase space (PS), dynamic moments (DM), parallel factor analysis (PARAFAC), energy vector (EV), power density spectrum (PSD) [38] and window functions including windowed time slicing (WTS) [117], etc. So far, a single universal data processing and feature extraction method has not been found. Therefore, one has to select the feature extraction method individually for each designed and developed system. 36.

(37) [117]. The decision may be influenced by sensor type, its parameters, target analytes, demands of specific application, etc. [117]. Final step in the signal processing is normalization which prepares the feature vector for analysis of pattern recognition. Normalization methods can be classified as local and global [114]. Local methods like vector normalization operate across the sensor array on each individual sample measurement to compensate for variations between different samples and sensor drifts. On the other hand, global methods like sensor autoscaling or sensor normalization operate across the entire database for a single sensor in order to compensate for differences in sensor scaling [114][116]. Once the sensor signal has been processed and a so called distinct pattern or “a finger print” of a chemical substance has been obtained, it can be analyzed by pattern recognition techniques in order to achieve information about the analyzed substance.. 2.3.4. Pattern recognition This section presents pattern recognition techniques used in the electronic noses and gas detection systems based on nonspecific gas sensors [114]. The response of an array of gas sensors can be processed by various techniques presented so far and then analyzed by pattern recognition algorithms in order to achieve classification and quantitative information about detected gas sample. Pattern recognition techniques can be classified as parametric or nonparametric and based on the learning method as supervised or unsupervised [114]. Parametric techniques are based on the assumption that the scatter of the sensor data can be described by a probability density function [114]. Often, the assumption made is that the data follow a normal distribution with a constant mean and variance [114]. Parametric techniques attempt to find an underlying mathematical relationship between gas samples, system inputs and outputs, classes or descriptors [114]. The nonparametric methods do not assume any probability density function for the analyzed sensor data [114]. These methods are more general and include artificial neural networks and expert systems [114]. In the supervised pattern recognition learning, a calibrating set of known gas samples is introduced to the electronic nose, which classifies them according to known descriptors or classes [114]. This constitutes the initial calibration and learning process. Then, unknown samples are analyzed using relationships found a priori from a set of known gas samples used in an initial learning process [114]. In the unsupervised pattern recognition learning, discriminating between unknown gas samples is done routinely without any prior corresponding descriptors [114].. 37.

(38) Widely used statistical methods include [114]: . principal components analysis (PCA),. . partial least squares (PLS),. . multiple linear regression (MLR),. . principal component regression (PCR),. . discriminant function analysis (DFA) including linear discriminant analysis (LDA),. . cluster analysis (CA) including nearest neighbor (NN).. Commonly used non-parametric methods are [114]: . artificial neural networks (ANN) including multi-layer perceptron (MLP),. . fuzzy inference systems (FIS),. . self-organizing map (SOM),. . radial basis function (RBF),. . genetic algorithms (GAS),. . neuro-fuzzy systems (NFS). . adaptive resonance theory (ART).. The following sections will describe the pattern recognition methods used in this Ph.D. thesis.. 2.3.4.1.. Principal components analysis (PCA). Principal components analysis is an unsupervised multivariate method successfully used for linear data compression and feature extraction for processing responses of gas sensor arrays [118]. The method is based on Karhunen-Loéve expansion and expresses response vectors in terms of linear combinations of orthogonal vectors along a new set of coordinate axes [114]. The method enables reducing dimensionality of a multivariate data set retaining the most significant information by eliminating the lowest ranking variables [118]. Data processing involving PCA can be divided into five steps described below [119]. 1.. The first step in the PCA is to determine the initial input data set. Usually for gas. sensor arrays it is a set of sensor responses or values derived from the sensor responses upon exposure to a certain target gas. This data set consisting of responses S of m sensors to n gas samples can be defined as:.  S11 S12 DataSet      Sn1 Sn 2.  S1m  Sij    Snm . where Sij is the response to j-th gas sensors to a i-th gas sample.. 38. (16).

(39) 2. The second step it to subtract the mean from each of the data dimensions. This mean is calculated individually for each dimension (column) and then subtracted from each data value in that column:.  S11  S1 S12  S2  DataSetAdjusted      Sn1  S1 Sn 2  S2 .  Sij  S j . S1m  Sm     Snm  Sm . (17). where S j is the mean value of the j-th column. As a result the data set has a mean value of zero. 3. The third step is to calculate the covariance matrix, eigenvectors and eigenvalues of the covariance matrix. The eigenvalues indicate which eigenvectors and respective principal components associated with them contain the most important information. 4. The fourth step is to choose the components and form the data set. The eigenvectors calculated in the previous step, are ordered by eigenvalue from the highest to the lowest. As a result, the components are now ordered by significance and the eigenvector with the highest eigenvalue is the principle component. Therefore, one can decide to ignore the less significant components what results of course in the loss of information. If the eigenvalues of the ignored components are small, the loss of information is negligible but the final data set has less dimensions than the original one. The feature vector is constructed as a matrix eigenvectors columns:. FeatureVector   eig1, eig 2, ..., eign (18) where eigenvectors eign are selected from the whole list of eigenvectors calculated in the step 3. 5. The last step is to derive the new data set of principal components. This is done by multiplying the transposed feature vector by the transposed adjusted data vector:. NewData  FeatureVector T  DataSetAdjusted T (19) where the NewData is the matrix containing the principal components. Because, the eigenvectors in the FeatureVector are ordered by eigenvalues from the highest to the lowest, the first principal component always contains the most significant information. Therefore, a multivariate data can be transformed to two or three dimensions [114]. An example of the results of the PCA in a form of a two dimensional plot is shown in Fig. 14.. 39.

(40) group 4 group 3. group 1 group 2. Fig. 14 The example of the results of PCA analysis in a two dimensional plot.. As one can observe, the data presented in Fig. 14 can be separated into four distinct groups. As, the first principal component PC1 carries the most important information, it is the best when the groups are separated along PC1 axis. In general, PCA is a technique of reducing the dimensions to a correlated multidimensional data set and can be presented in a form of a two or three dimensional plot [114]. 2.3.4.2.. Artificial neural networks (ANN). Neural networks have often been used in the electronic nose systems due to their nonlinear mapping capabilities [118]. The most important parameters of an artificial neural network are its topology, the activation function used within the neurons and the applied learning algorithm. Various types of neural network exist [120], however the most important include: . feed-forward neural networks – in which the information is propagated only in one direction through several neuron layers, e.g., a single layer perceptron (SLP), a multilayer perceptron (MLP), etc.. . radial basis function network - information is propagated in one direction and utilizes radial basis functions. . recurrent networks – in which a bi-directional data flow from the later stages to the earlier stages is applied, e.g., simple recurrent network (SRN), Hopfield network. . 40. stochastic networks - introduces random variations into the network.