14 Air Quality Monitoring And

IEEE SENSORS JOURNAL, VOL. 2, NO. 3, JUNE 2002 179

Air Quality Monitoring and Fire Detection WithThe Karlsruhe Electronic Micronose KAMINA

Christina Arnold, Michael Harms, and Joachim Goschnick

Abstract—An indoor air monitoring device is one of the mostprominent consumer applications of an electronic nose (EN). In-tegral gas analysis similar to biogenic odor perception can be aversatile tool to obtain continuous information about pollutants,odors, and air compositions indicating gaseous precursors of dan-gers such as fires. However, an EN to be used as a common house-hold device has to combine high sensitivity and excellent gas dis-crimination power with inexpensiveness, small size, and low powerconsumption. A special gas sensor microarray of thumbnail sizehas been developed at the Forschungszentrum Karlsruhe basedon metal-oxide technology to meet these requirements. The mi-croarray is produced by simply partitioning a monolithic metal-oxide layer with parallel electrode strips allowing low cost fabrica-tion. A temperature gradient and a membrane thickness gradient(on metal-oxide layer) are responsible for differentiation betweenthe individual sensor segments and thus for the conductivity pat-terns that are accordingly produced. The two membranes form thebasis of gas discrimination power, reliability self checks, and onlinenoise reduction. Model gas exposures show detection limits lowerthan 1 ppm, usually. Successful practical tests are reported on thedetection of overheated wire insulation for fire prevention as wellas on air quality analysis for air conditioning purposes (e.g., airquality control during a meeting).

Index Terms—Electronic nose, gradient technique, indoor airmonitoring, metal–oxide gas sensors, microarray.

I. INTRODUCTION

T HE integral analysis of gas ensembles, as performed by ahuman or a dog’s nose, represents an analytical technique

of universal applicability. Not only the gas phase itself can becharacterized but often also liquid and solid samples, as theseoften release volatile or semi-volatile components into the gasphase. Everybody knows that quite often a product’s quality orthe dynamic state of a process manifests itself in a special kindof odor. This is probably the reason why the nose is the mainchemical sensor system with which human beings are equipped.

Accordingly, an enormously wide market opens up for elec-tronic noses (EN) as condition monitors [1], provided price, spa-tial requirements, and energy consumption are compatible withthe application. The most strict requirements come from con-sumer applications of electronic noses no matter whether house-hold appliances, air quality monitoring, fire detection, privatemedical care products, or automobile applications are the target.Extremely low costs and long-term stability for many years shall

Manuscript received August 14, 2002; revised May 21, 2002. This work wassupported in part by NASA. The associate editor coordinating the review of thispaper and approving it for publication was Dr. H. Troy Nagle.

The authors are with the Institut für Instrumentelle Analytik, Forschungszen-trum Karlsruhe, Karlsruhe, Germany (e-mail: [email protected]).

Publisher Item Identifier 10.1109/JSEN.2002.800681.

be combined with excellent analytical power in terms of gassensitivity, gas discrimination and response speed. Usually, acontinuous output is needed to supply monitoring data into reg-ulation systems.

An indoor air monitoring device is one of the most promi-nent consumer applications of an EN, as on average worldwidepeople spend more than 80% (90% in industrial countries) oftheir time indoors [2]. The quality of indoor air in terms of un-healthy pollutants and the odor inventory (air comfort) is thusof extreme importance. Moreover, indoor air suffers much morefrom gaseous emissions compared to outdoor air because of itslimited space and the variety of gas sources to which it can beexposed. The vast majority of rooms in which people stay have acomparatively small volume and only a moderate air exchangerate. Therefore, considerable concentrations of pollutants canbuild up at low gas emission rates that outdoors would only havea negligible impact. Building materials, furniture and the peoplein a room create a special indoor climate. The necessity to mon-itor the indoor climate will be even more important in the future,as energy conservation (either heating or cooling requirements)will demand reduced air exchange rates. Hence, air quality mon-itoring is desirable to optimize air exchange in a continuous reg-ulation to avoid wasting energy yet maintain air quality at anacceptable level. Additionally, in most cases, the “fresh” air tobe mixed with the indoor air for purification cannot be consid-ered as clean odorless air, e.g., when polluted through exhaustemissions. Hence, the indoor as well as the outdoor air shouldbe monitored continuously with an energy-efficient air condi-tioning system.

Moreover, monitoring indoor air opens up a wide field forfurther applications, especially in the case of danger recogni-tion. Fire prevention can be achieved by detecting the smell ofoverheated electrical cables and gas explosions can be avoidedif flammable gases (such as natural gas) are detected rapidly be-fore reaching dangerous levels. The detection of spoiled food,the recognition of mould development under wall paper or thedetection of pests are further examples.

Thus, what are the gas analytical requirements for an indoorair monitor? First, the spectrum of detectable gases should beas broad as possible because indoor air contains an enormousvariety of different gases, small inorganic molecules, as well asorganic molecules with considerable molecular weight. Second,a high sensitivity is needed, especially in view of the air comfortbecause the human nose detection thresholds for a number ofhome typical air components are in the lower ppb range. Third,a high discrimination power for gas ensembles is needed to rec-ognize unhealthy gas components, to distinguish odors or to dis-criminate the smell of a cigarette from a burning curtain. Fourth,

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180 IEEE SENSORS JOURNAL, VOL. 2, NO. 3, JUNE 2002

the response time should be in the range of seconds, especiallyregarding the detection of hazards. Surprisingly, accurate quan-tification over a broad concentration range is seldom required.

II. A PPLICABILITY OF GAS SENSORARRAYS

Of course, conventional gas-analytical instrumentation (e.g.,infrared spectroscopy or mass spectrometry combined with gas-chromatography) can provide a rapid and accurate gas analysis[3]. But, the price, energy consumption, and size of these in-struments are completely incompatible with mass product re-quirements. Only gas analytical sensor systems with manufac-turing costs much less than 50 USD (including the electronics)are likely to be applied in consumer products.1 For more thana decade now, small and simple gas sensors, that provide oneoutput signal only, have been commercially available. However,these single output sensors allow only one component to bequantified but a distinction between different gases or gas com-positions expected in indoor air is not possible. Moreover, thesesensors usually more or less suffer from cross sensitivity, i.e.,beside their sensitivity to a particular target gas they also showa certain sensitivity toward other gases. Hence, a single outputsensor cannot be sufficient for gas analysis, even if only onetarget gas has to be detected in a complex mixture of volatiles.

However, the combination of several gas sensors (eachproviding a different sensitivity spectrum), a so-called sensorarray, continuously delivers a number of signals, usually calleda signal pattern, characteristic of the type and quantity ofgases to which the array is exposed. Thus, the multivariatesignal output of such sensor arrays allows an online distinctionbetween individual gases or between different gas ensembles,such as odors. Which gases can be detected or distinguisheddepends on the gas detection principle and the extent ofthe difference in selectivity between the sensors. Now, thecross-sensitivity of the individual sensor, due to a moderateselectivity turns out to be an advantage in this system. A lowerselectivity than the dominant response to one particular gasallows the array to respond to a wider range of gases, whileit would be a draw-back for a single output sensor in an onetarget gas application. However, on the other hand, the gasdiscrimination ability would be lost if the gas sensitivity wascompletely uniform for all gases (zero selectivity). Hence, amedium selectivity of the array’s sensor elements providesa good compromise between a broad spectrum of detectablegases and an appropriate level of gas discrimination power.

On such a basis, the first electronic noses (ENs) came up inthe early 1990s. These conventional ENs, however, are equippedwith arrays assembled of separate sensors. They are still domi-nating the market and consist of individually manufactured sen-sors equipped with sockets to be fitted in plugs on a separate car-rier of several centimeters in size. Clearly, high manufacturingcosts are the consequence. A gas supply system, with the ne-

1Numerous discussions with representatives of companies marketing con-sumer products in the household appliance sector have shown that 50 USDare the highest production cost margin, and this cost margin only applies tohigh-price luxury products. For ordinary consumer products, the margin is muchlower. For instance, not long ago, a gas sensor unit at 20 USD meant for auto-mated ventilation flap control in luxury cars was accepted by the automobileindustry. Nowadays, for the same equipment of middle or lower class automo-biles automakers accept a price of 10 USD only.

Fig. 1. KAMINA chip mounted in its housing. The gas sensor gradientmicroarray is based on a segmented metal-oxide film of which the electricalconductivity is dependent on the ambient gas composition. The rear side ofthe chip (upper right) carries four meander-shaped heating elements to allowcontrolled inhomogeneous heating of the chip. The resulting temperaturegradient in combination with a thickness gradient of a nm thin SiOmembraneon top of the metal-oxide layer cause the sensor properties of the segmentsto gradually change over the array. Two thermoresistor meander beside themetal-oxide film serve as temperature sensors to feed the heating control.Electrical contacts are provided by gold wire bonds.

cessity to split the analyte gas into identical fractions for eachsensor, adds to the high production costs. Hence, there is no wayto reduce the manufacturing costs of a gas sensor array of thiskind below the above-mentioned limit of 50 USD (this figureincludes the cost of electronic periphery and packaging). More-over, the spatial dimensions of instruments based on conven-tional arrays with separate sensors and their energy consumptionare completely incompatible with the requirements of a con-sumer product.

However, a microsystem approach combining an arrange-ment of gas sensors, the higher the integration the better, withmicroelectronics perfectly meets the requirements for ENmodules in mass products as, first, a microsystem with itsbatch based fabrication style is well suited to remain withinthe cost margins. Second, small size, low energy consumption,and long-term stability can achieved be more easily. Finally,microfabrication is expected to enhance the reliability andreproducibility as well as the mechanical robustness of thesensor array system [4]. In order to demonstrate the advantagesoffered by an EN based on microsystem technology, the devel-opment of gas sensor microarrays has already been pursued atthe Forschungszentrum Karlsruhe (Karlsruhe Research Center)for many years. Embedded in the strategy project electronicmicronoses (ELMINA) of the Hermann-von-Helmholtz As-sociation of German Research Centers the development ofan indoor air monitor is one of the primary goals. Of course,other applications beyond consumer products will benefit fromthe microsystem approach, since the improvements are alsorelevant for instruments used in industrial applications or inenvironmental monitoring.

III. GRADIENT MICROARRAY OF HIGH-LEVEL

INTEGRATED GAS SENSORS

The Karlsruhe Micronose (Karlsruher mikronase[KAMINA]) is based on a novel type of gas sensor mi-croarray developed at the Institute of Instrumental Analysisof the Forschungszentrum Karlsruhe (see Fig. 1). The simple

ARNOLD et al.: AIR QUALITY MONITORING AND FIRE DETECTION 181

Fig. 2. Gas sensor array structures.

construction and the high-level integration of the sensor ele-ments allow very low production costs and at the same timeoffer gas analytical advantages (see below). The dependenceof the electrical conductivity of semiconducting metal-oxides,such as tin dioxide, on the ambient gas composition has beenchosen as the measuring principle for the following reasons:The high sensitivity of the conductivity response to changes inthe gas composition [5], [6], the broad spectrum of detectablegases (only passive gases such as rare gases and nitrogen giveno response), and the easy fabrication of micro-structuredmetal-oxide films. The influence of gases on the electricalproperties of certain metal-oxides has been well known foryears [7]. The strong impact of gases on the electrical conduc-tivity is caused by the adsorption of the gases at the surfaceof the metal-oxides which is often followed by a catalyticoxidation (organic gases, H, NO, CO) increasing the electrondensity and hence the conductivity within the surface layer [8].In other cases, the mere adsorption causes a charge transferfrom the substrate to the adsorption complex which, e.g., forNO , results in an immobilization of electrons at the surfaceand thus consequently in reduced conductivity.

The technological novelty is the special structure of theKAMINA gas sensor array that is patented worldwide [9].Fig. 2 shows that contrary to conventional macro-arrays andalso other gas sensor microsystems one single monolithicmetal-oxide film alone forms the basis of the whole array. Thisfilm is separated into sensor segments by parallel electrodestrips to measure the electrical conductivity of the segments.A simple gradient technique differentiates the gas detectionselectivity of the individual sensor segments. The thickness ofan ultrathin gas-permeable membrane layer deposited on topof the metal-oxide film varies across the array. Additionally,a controlled temperature gradient is maintained across thearray by four platinum heating meanders located on the rearside of the chip. As both thickness and temperature have agas dependant influence on the diffusion through the mem-brane and the temperature influence on the gas reaction at

the metal-oxide interface also depends on the nature of thegases, gas detection selectivity is gradually modified fromsensor segment to sensor segment. Therefore, the exposure tosingle gases or gas ensembles (like odors) causes characteristicconductivity patterns to occur at the gradient microarray. Thedependence of the conductivity pattern on type and quantityof ambient gases allows gas discrimination and quantificationfor a limited number of components in a mixture. Hence, thisgradient microarray can be applied to realize a sensitive ENsystem.

IV. FABRICATION OF THE GRADIENT MICROARRAY

A small series production has been set up to develop the basicfeatures of mass production. The present fabrication of theKAMINA chip can be subdivided into four phases (see Fig. 3):The wafer-based formation of the fundamental structure, theseparation of the chips including carrier assembly, the mem-brane deposition, and the final annealing treatment [10], [11].The first phase applies high frequency sputtering to depositthe metal-oxide film and the electrode structure including twomeander-shaped thermoresistor strips on the front side, andthe heating structure consisting of four platinum meanderson the rear side. A simple shadow mask technique is used toperform the lateral structuring. Presently, microarray chipswith the dimensions 10 11 mm and 4 4 mm are beingmanufactured, bearing 38 or 16 sensor segments respectively.The 3" or 6" silicon wafers (oxidized on both sides) serve asthe basic material on which either 26 or 122 microarray chipswith 38 segments—or 159 or 658 microarray chips with 16segments—are simultaneously processed in a batch. Currently,tin dioxide (SnO) and tungsten trioxide (WO) are used forthe gas sensing metal-oxide layers. For the deposition of thesefilms, a 20% oxygen contribution to the argon sputter gasis added in order to obtain the proper stoichiometry of theoxides. The sputter targets are sintered metal-oxide bodies.Pure argon is used for sputter deposition of the electrode strips,the thermoresistor strips and the heating meanders which areall are made of platinum.

The second phase is the separation of the chips from the waferfollowed by the assembly within the carrier housing (see Fig. 1).Finally, the electrical connections are provided using ultrasonicbonding with gold wires.

The subsequent third phase is dedicated to the depositionof the gradient membrane [12]. The latter is a gas permeableSiO membrane of only a few nanometers thickness. It coversthe whole array structure, including the metal-oxide film andthe electrode strips. The thickness, however, varies across thearray which gives each sensor segment a membrane of indi-vidual thickness gradually differing from the neighboring sensorelements. As it is the membrane that controls access of the gasmolecules to the metal-oxide, gas detection selectivity differsfrom one sensor segment to the next, according to the change inthickness. However, it is not only the gas transport through themembrane that governs gas detection selectivity. The entrancegate of the sensor structure is the surface of the membrane. Eachgas has to adsorb there first before the detection process can


Fig. 3. Two main manufacturing techniques.

proceed. Hence, the dependence of the adsorption process onthe kind of gas also adds to the selectivity of the sensor seg-ments. Ion beam assisted deposition (IBAD) has been chosenfor fabrication of the membrane because of its ability to depositultrathin layers of high gas permeability with laterally continu-ously changing thickness in the range of only a few nanometers.Moreover, the simplicity of the process and therefore cost effec-tiveness are further advantages, as well as high flexibility, al-lowing the material of the membrane to be changed quite easilyby changing the precursor gas.

The IBAD process is performed with an argon ion beam toinitiate the SiO polymerization from the gaseous precursorphenyl-trimethyl-silane. The standard process works at roomtemperature for the substrate as well as the process gas. Initially,no genuine SiOis formed. The condensate of the precursor onthe already prepared segmented metal-oxide film (including theelectrodes) is decomposed to form a loose network of silicon,oxygen and carbon atoms. During this process, the formationof an ordinary SiO lattice is substantially disturbed. Becausethe rate of decomposition depends on the current density ofthe ions, the ion beam profile is employed as a template forthe thickness gradient of the SiOmembrane layer. The beamaxis is positioned right above one edge of the oxide film whichresults in a continuous decline of the ion current density acrossthe array. Thus, with an increasing distance from the beamaxis a gradually decreasing thickness of the Si, O, C- film isobtained. Accordingly, the thickness gradient is an image ofhalf of the beam profile.

Within the last production step (phase 4), annealing threedays at 400C closes the fabrication process. During this treat-ment the carbon is completely removed from the membranelayer which considerably shrinks. The annealed film now con-tains stoichiometric SiO, however, with a high gas permeabilitydue to the remaining stacking faults in the SiOstructure. TheSiO films usually have a minimum thickness of 2 nm and athickness variation of up to 20 nm across a 9 mm wide stan-dard chip metal-oxide field. The heat treatment, of course, alsostabilizes the micromorphology of the interface region and themetal-oxide itself.

The first advantage of the gradient microarray over arraysmade of separate sensors (regardless whether macro- or mi-crostructure) manifests itself in its much simpler fabrication.The entire basic structure of the microarray is created in onlythree production steps. Neither the individual mounting of sen-

sors is necessary, as with macroarrays, nor does a change of tar-gets and sputter masks have to be carried out as it is usuallythe case for microsystems which consist of an array of separatepatches of different metal-oxides [13], [14]. Other depositiontechniques, for instance, CVD techniques for the deposition ofseparate sensor patches of different materials, all need a kindof material reservoir. Either the deposition of the sensor pads ismade in one chamber, one pad after the other with intermediatecleaning phases, or it is performed in several chambers, eachmeant to deposit only one sensor material. The latter saves thecleaning times but the in-and-out from one deposition chamberto the next is laborious [15]. Consequently, much lower produc-tion costs can be achieved with a segmented metal-oxide layerand the low number of required process steps allows high yields.In a recent joint project with industrial partners, the fabricationprocess for routine mass production was worked out on the basisof the already existing small series production. Test batches on6" wafers underwent further processing carried out by industrialpartners. Each partner performed one step of the fabrication se-quence of which the costs had to be estimated. From these data,the total production costs of the gradient microarray chip werefound to be lower than 5 USD/chip in high volume production[10].

V. GAS ANALYTICAL PERFORMANCE ANDDATA PROCESSING

OF THE GRADIENT MICROARRAY

The response of gradient microarrays to a variety of gaseshas been tested which could be relevant for air quality and firedetection. Pulsed exposure series have been performed to de-termine the key data of gas analytical performance. In orderto examine the stationary as well as the dynamic behavior ofthe chip, it was periodically exposed to humid air (60%) con-taining certain concentrations of the test gas in alternation withclean air of the same humidity (see Fig. 4). The pulsed exposureswere carried out with a computer-controlled gas mixing systemequipped with mass flow controllers and pneumatic valves toallow an exact setting of gas concentration and humidity. Themodel atmospheres were at room temperature. The operatingtemperature range of the microarray was 250–300C. A quickand sensitive response was found for both types of microarraychips (SnO or WO equipped) to the changing composition atthe test pulse transitions. Response times of less than 5 s havebeen achieved (see Fig. 5). Many gases, such as H, CO,


Fig. 4. Resistances of the 38 sensor segments of a SnO-microarray equipped with SiO-membrane during alternating exposure to carbon monoxide contaminatedhumid air and clean air with the same relative humidity of 60%. CO gas pulses of 1 to 250 ppm were used to test the microarray.

Fig. 5. Microarray response to an octane pulse of 10 ppm after clean airexposure.

NH , H S, as well as organic gases, were detectable at concen-trations of even less than 1 ppm [16]. SnOshowed a highersensitivity to hydrocarbons while WOwas somewhat better indetecting gases bearing nitrogen or sulfur [16]. However, a plat-inum doped SnOchip (SP-chip) was preferred for an over-allscreening of indoor air as the sensitivity deficit of WOfor hy-drocarbons is much higher than the relative sensitivity short-coming of the SnOchip for nitrogen or sulfur bearing gases.

Moreover, differentiation of the 38 segments of an SP-chipbrought about by a temperature and membrane thicknessgradient has proved to provide high quality gas discriminationpower. An analysis of the signal patterns obtained for somegases present in typical household fires is depicted in Fig. 6.Prior to the signal pattern analysis, the signals of the sensorsegments were normalized to the median of all sensor segmentsin order to remove the quantitative information. The medianis chosen to represent the mean of all sensor segments ratherthan the arithmetic average because of its inherent higherstability toward changes in the signal pattern and mathematicaloperations.

Fig. 6. LDA analysis of signal patterns of typical fire gases. The datawere obtained using a SnO-microarray equipped with SiO-membrane(T = 250–300 C). Data were used of 10 and 100 ppm model gas pulses andwere taken 2 and 30 minutes after pulse start.

A variety of pattern recognition techniques has been used byresearchers in this field for the evaluation on gas sensor arraydata [17], [18]. Principal component analysis (PCA) is the mostprominent, which is an unsupervised method to break down theoriginal multidimensional data space of the signal pattern (con-sisting of as many dimensions as the number of sensors in thearray) to a few number of dimensions (usually two or three)by choosing a projection for which the maximum distances ofall data points are obtained. Hence, all influences which differ-entiate the signal patterns contribute to the spread of the datapoints. This kind of signal pattern analysis is useful for systemand process development to optimize the raw data output. Asall influences on the signal patterns are visible improvementsof the hardware of the sensor system, the sampling procedureand the data preprocessing can easily be seen and measured bythe distance and internal scatter of the data clusters obtainedin repeated measurements of the gases or gas ensembles whichshould be distinguished.


However, for practical applications, the evaluation of thesignal patterns obtained from sensor arrays is better performedby the supervised linear discriminant analysis (LDA) [18].Although the LDA also performs a projection of the originalmultidimensional data space to a two or three dimensional plot,the procedures for data transformation are different from PCA.LDA works with data points sorted to belong to certain classes(the gases or gas ensembles which should be distinguished),resulting in a projection with maximized distances betweenclasses and minimized distances between the data points be-longing to the same cluster. Hence, the differences of the signalpatterns caused by the gases or gas ensembles to be separatedin the application are enlarged while differences due to otherinfluences including statistical scatter are reduced. Therefore,pattern analysis based on an LDA type data transformation isthe more appropriate method to show the discrimination powerof the gas analysis because the differences of the measuredsignal patterns not useful for the gas separation are suppressed.

In the case of the KAMINA microarray, the LDA basicallyperforms a projection from the original signal space of 38dimensions down to the two or three dimensional data spaceshown in the Figs. 6, 8, and 10. Each position in the LDAplot represents a certain signal pattern. The distances shownin the plots represent the Mahalanobis distances [19]. Largedistances indicate a low similarity of the corresponding signalpatterns, while near data points correspond to signal patternsof high similarity. The ability of the gas sensor microarray todistinguish between the model gases is evident when looking atthe well separated data clusters of the different gases comparedto the scatter within the clusters comprising data of repeatedmeasurements of the same gas. Moreover, although the databelong to two different concentrations (10 and 100 ppm), thehomogeneity of the data clusters demonstrates the mediannormalization to indeed be appropriate to remove concentrationdependence (Fig. 6).

However, in addition to the low production costs and the ex-cellent gas analytical features, the gradient microarray chip of-fers further advantages over the classical arrays consisting ofseparate gas sensors of different chemistry. The gradual vari-ation of the sensing properties provides a method to rapidlyscreen the performance of adjacent sensors for quality assur-ance purposes. Contrary to conventional gas sensor arrays, ofwhich the relative response of adjacent sensors cannot be pre-dicted, the sensor signals of the gradient array line up accordingto the gradual difference from their neighboring sensors. Thus,the sensor signal of every element has to be close to the averageof its two neighbors. Deviations from this correlation indicatea fault. Furthermore, this relation permits smoothing the chainof signals of the gradient array. This reduces the measurement’snoise resulting in improved detection limits without taking anyextra time for data recording.

VI. I NDOOR AIR MONITORING

Applying an electronic nose for continuous monitoring of in-door air provides various information concerning the level of aircomfort (level of odor nuisances), the presence of unhealthy gas

Fig. 7. Operating device of the Karlsruhe Micronose KAMINA with the headcover lifted. A simple fan is used to bring ambient air to the gradient microarraymounted below the head cover which is pulled down during operation. The partof the device below the microarray chip contains the complete�p-controlledmeasuring electronics as well as the regulated heating supply of the four chipheaters. A standard serial data interface provides the communication to thecontrolling PC.

components, and the hazardous potential, indicated for instancein the case of the presence of fire precursor gases. In order toevaluate the practical performance of the gradient microarray,its sampling arrangement and data processing procedures havebeen investigated in two application scenarios, one checking itsefficiency for fire prevention and the other for general air qualitycontrol.

The standard gradient microarray chip equipped with 38sensor segments of platinum doped SnOcoated with a SiOmembrane of some nm thickness (variation of 2–30 nm due tothe gradient) was installed in the current KAMINA operatingdevice, which is the size of a beverage can. The current devicemerely serves as a tool for further development of the sensorsystem and its sampling periphery and is thus not yet suitablefor equipping mass products. An operating device devisedfor consumer applications (half the size of a credit card) isunderway. During the scenarios, the operating device wasconnected to a standard PC for control of the chip operatingconditions, online visualization, and data evaluation. A simplefan in the head of the KAMINA unit permits air sampling(see Fig. 7). The microarray chip was operated at a surfacetemperature ranging from 250 to 300C.

One important issue concerning fire prevention is the earlydetection of overheated wire insulation, a common source ofsmoldering fires. Therefore, model experiments were performedin order to examine how far early indications of overheated in-sulation could be recognized by the gradient microarray andits simple sampling arrangement. The tests were carried outin a closed box with the KAMINA placed next to overheatedwire insulation (overload of current). The experiments were per-formed using various different wire insulation materials (e.g.,Kapton®, Teflon®, and ethylene tetra fluorine ethylene [EFTE]).The wires were about 30 cm away from the inlet port of the


Fig. 8. LDA of the microarray signal patterns obtained with KAMINA in practical tests to detect gaseous precursors of smoldering fires induced by overheatedcable insulation (ETFE: ethylene tetra fluorine ethylene).

KAMINA. Although the current was high enough to melt or in-flame the insulation, it was kept sufficiently low in order to pre-vent early smoke emission. The time between current switch onand initial smoke emission was about 10 min. In order to checkthe discrimination power of the microarray chip for differentgas ensembles released by the overheated insulation and pos-sible interfering gases, vapors of several solvents (e. g. toluene,xylene, acetone, isopropanol, ethanol) were admitted to the testchamber by placing solvent soaked tissues inside.

Fig. 8 shows the results of an LDA of the signal patterns ob-tained in different exposure scenarios. Prior to the actual patternanalysis, the resistances of the sensor segments were normal-ized to the median of all sensor segments in order to eliminatethe quantitative information. The latter is useless in this appli-cation, as the gas transport away from the hot cable occurs inan uncontrolled and discontinuous way. Furthermore, most ofthe signal noise was stripped off by a preceding PCA, reducingthe number of variables to the first 12 principal componentsof the PCA that significantly contribute to the variance of thedata set. Thereby the noise as well as the volume of the dataset is reduced. Two different kinds of LDA were carried out.In the left plot, the data of each insulation and solvent vaporwere treated as separate classes with individual signal patterns.In this case, the LDA algorithm performs a projection from theoriginal signal space down to the two dimensions shown whichexhibit the maximum of differences between the signal patternsof all different cable insulation and vapors. The circles describea confidence range of 95%. The resulting LDA suggests that thedifferences between the signal patterns of the overheated insu-lation materials and clean air are significant enough to detectoverheating of the cables and even the separation between dif-ferent types of insulation is possible. The latter is also true con-cerning the vapors that are obviously distinguishable by theirsignal pattern. Moreover, the signal patterns of the solvents arewell separated from those of the overheated insulation materials.A validation of this model was made with measurements of iso-propanol soaked tissues and smoldering cables. The gases ofboth sources were clearly recognized.

However, the principle detection of smoldering cables willnot be disturbed by the variety of signal patterns of different in-sulation materials or different solvent vapors. If the LDA algo-

Fig. 9. Median of the resistances of the gradient microarray with 38 sensorsegments during a session with 18 people in a meeting room.

rithm is instructed to separate the signal patterns into three cate-gories only, clean air, overheated insulation, and presence of sol-vent vapors, a well-separated three-class system can also be es-tablished, as the right LDA diagram in Fig. 8 shows. Hence, theresults demonstrate the potential of the gradient microarray toact as an intelligent early fire detector, which detects overheatedwires by their typical gas release long before smoke emissioncan be observed. Hence, the advantage over conventional smokedetection is evident: More information is provided at an earliertime.

Concerning the quality of indoor air, the interaction of peoplewith their gaseous environment is of primary importance. Theair quality may be understood here and in the following as aterm to describe the air in terms of its content of unhealthy com-ponents and odor inventory (air comfort). Of course, buildingmaterials and furniture are also gas and odor sources. However,usually these sources do not initiate short term changes of the airquality as persons do. Therefore, the response of the KAMINAwas primarily investigated concerning the response to air qualitychanges caused by the presence of people. For this experiment, alecture room of 258 m(height of the ceiling is 2.80 m) withoutventilation was chosen. The KAMINA was operated in the backof the room at a height of 1.0 m over the floor. The measure in-terval was 20 s. In all cases, the room temperature was about22 C.


(a) (c)

(b) (d)

Fig. 10. LDA of the microarray signal patterns measured in a lecture room during night and day. (a) The model of two lecture sessions and six measurements ofan isopropanol source. (b) Data of a third lecture session and three more measurements of isopropanol. These data are not included in the LDA model shown in (a).(c) LDA only considering the ordinary air situations allows more subtle discriminations of the air inventory. (d) Data of the third lecture session asshown in (b).

Breathing and transpiration of people were indeed sensitivelydetected by the KAMINA chip. Fig. 9 shows one example ofseveral measuring campaigns of room air changes under the in-fluence of people present in the room. In this case, 18 peoplegathered in the meeting room. Soon after they entered the me-dian resistance began to drop considerably. The decrease ofthe sensor resistances indicated oxidizable organic componentsthat dominated the gas ensemble, entering the air by transpira-tion and breathing. Maybe gas release of the clothing also con-tributed to that effect. About two hours after the people had en-tered the room, the gas emission rate and the low air exchangerate (leakage through closed door and windows) reached a kindof equilibrium keeping the pollution level constant. The tempo-rary increase of the air exchange by opening the window for aperiod of 15 min increased the median resistance, indicating aslight temporary improvement of the air quality level. Only afterthe end of the meeting, when the people had left the room anda window was opened, the inlet of fresh air led to a substantialimprovement of the air quality.

In a second type of experiment in the same, but this timeempty lecture room, vapors of isopropanol were released intothe air by a soaked tissue (0.1 ml) placed at a height of 1.0 mabove the floor. The distance between KAMINA and the tissuewas 0.5 m. The measuring interval was 1 s.

In order to characterize the air composition, a signal patternanalysis was performed with LDA, preceded by a PCA filter (as

described in the fire detection example above). The resistancesof the sensor segments were referenced to the air of the emptyroom at night and normalized by the median of all sensor seg-ments. To get rid of transient conditions, approximate stationarydata only were cut out of the complete data set for LDA analysis.The circles describe a confidence range of 99% (Fig. 10). TheLDA presented in Fig. 10(a) shows data of two lecture meetingswith a period of one week in between and of six measurementsin the empty room with an isopropanol source. Four differentstates of air quality are shown: the air in the empty room withopened windows, the air in the empty room with closed win-dows (taken in the morning), the air in the same room whenpeople are present, and the air in the empty room with an iso-propanol soaked tissue. Clearly, the air of the empty room withclosed and opened windows can be distinguished from air oflower quality caused by people. These differences are smallercompared to the presence of the pollutant isopropanol whichsimulates the situation of an unnoticed accident with a releaseof unhealthy gases. Fig. 10(b) shows a validation of the model.The test data for both situations, isopropanol source and lecturesession, were collected in the same room 33 days and one weekrespectively after the model data were taken. During the lecturesession, 17 persons were in the room; the whole meeting took2.7 h. These test data were not cut; they represent the periodbeginning with the people entering the room up to the momentthey left. For the isopropanol experiment, three measurements


were taken. The data normalization followed the same way asfor the created model. Hence, the deviation of the air inventoryfrom its normal state can easily be detected. For a number ofaccidents the situation can, indeed, be trained in a model exper-iment (cigarette smoke, presence of glues and solvents, naturalgas leaks, incoming polluted air from outdoors, fire detection).However, even if the observed signal pattern is unknown, thepeople in the room can be warned of the uncommon situationby an acoustic or visual warning.

If accidents are discarded and ordinary air situations only areanalyzed, differences in the air inventory can be resolved in aneven more subtle way. The LDA model in Fig. 10(c) then allowsto distinguish the slight difference in the empty room betweenday and night. Moreover, still the smell of smoke coming froma pipe smoked by someone outside the investigated room in thehallway was detectable, although the doors were closed. Theindependent test data measured when people were in the roomcan also be seen in Fig. 10(d).

VII. SUMMARY AND OUTLOOK

In this paper, we have show the feasibility of using the gra-dient microarray of KAMINA to monitor indoor air quality. Theanalysis of the air compositions also seems to allow recogni-tion of conditions of a developing fire. An extensive collectionof data over a longer period of time is, however, necessary inorder to obtain a definitive statistical basis. Moreover, many fur-ther tests have to be performed in various room situations, e.g.,different room sizes, different building materials, different fur-niture, and rooms of various usage to find out how the instru-ment and its data processing software should be designed for agreat variety of applications. Since LDA plots are not proof ofclass separability, additional experiments are being conducted.First, the datasets are being expanded. These expanded datasetswill be analyzed by an appropriate classifier, such as K-Nearest-Neighbor and evaluated in a Leave-One-Out performance esti-mator. This will give us a true estimate of the percentage chanceof successful classification for our KAMINA system used inthese applications. Such investigations are underway.

The technology of ENs is to a large extent still in an infantstate. However, worldwide R&D on these systems have revealedan immense analytical potential of such systems that has hardlybeen exploited yet. But application of this technology will onlycross the Rubicon with regard to widespread use, if the devel-opment succeeds in offering analytical capability with high ac-curacy and reliability at a very low price, with only little energyconsumption and negligible spatial requirements. This is espe-cially true concerning consumer applications for which produc-tion costs of less than 50 USD, power consumption of less than1 Watt, and spatial dimensions of a matchbox are demanded.Only if these requirements are met will the EN find its way intoconsumer applications.

The R&D work at the Forschungszentrum Karlsruhe hasshown that a microsystem based on a sensor array structure ofgreat simplicity and high integration allows EN systems to bebuilt combining low fabrication costs and high gas-analyticalperformance. The developed gradient microarrays can be ex-pected to meet the consumer product requirements concerningprice, small dimensions and low energy consumption under

mass production conditions. The work with these kind ofmicroarrays has just begun, leaving a high potential for futureimprovements. In a similar way, as microelectronic deviceshave continuously improved their price/performance ratio byshrinking structures, leading to an enormous spreading ofmicroelectronics, the application spectrum of EN devices willbenefit from the microsystem approach, the more, the highertheir level of integration.

ACKNOWLEDGMENT

The authors are very grateful to Mrs. R. Young of theKennedy Space Center who carried out the experiments onoverheated wire insulation.

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Christina Arnold received the Ph.D. degree in1998 from the Technical University of Darmstadt,Germany, where she studied inorganic chemistry andinvestigated homogenous catalysis of complexes.

After receiving the Ph.D. degree, she was witha hair cosmetics company for a short time, whereshe first worked with an electronic nose. In 2000,she joined the R&D group for development of gasanalytical micro sensors at the Karlsruhe ResearchCenter, Karlsruhe, Germany.

Michael Harms studied chemistry at the TechnicalUniversity of Braunschweig, Braunschweig, Ger-many. During his studies, he focused on analyticalchemistry and received the Ph.D. degree fromthe Institute of Ecological Chemistry and WasteAnalysis, Technical University of Braunschweig, onanalytical studies of landfill leachate.

Since 1998, he has been a member of the R&Dgroup “MOX sensors” at the Karlsruhe ResearchCenter, Karlsruhe, Germany, and is responsible forapplication studies with both explosive and fire

gases.

Joachim Goschnick was born in 1950 in Berlin,Germany, and he began his scientific career in 1969,studying chemistry at the Free University Berlin. Hereceived the Ph.D. degree from the Fritz-Haber-Insti-tute of the Max-Planck-Association, Berlin, and histhesis focused on investigations on heterogeneousreactions at metal surfaces.

Currently, he is responsible for the developmentof gas analytical micro systems and depth-resolvedchemical characterization of microstructures at theInstitute for Instrumental Analysis of the Karlsruhe

Research Center, Karlsruhe, Germany. During intermediate periods, he workedas a Visiting Scientist at the Queen Mary College, London, U.K. His postdoc-torate life began at the Physical Chemistry Department, University Heidelberg,Heidelberg, Germany, from where he went to the Karlsruhe Research Centerin 1988 to set up a laboratory for depth-resolved analysis of environmentalmaterials, especially airborne micro particles. With the launching of the Microengineering Project of the Karlsruhe Research Center in 1993, his R&D fieldwas expanded to include the development of gas analytical micro sensors. Themain aim is the development of inexpensive, small, and robust electronic nosesbased on micro/nanotechnology. One outcome of this work is the well-knownKAMINA. Since 1999, he has been Project Manager of the strategy project,“Electronic Micronoses,” which is a joint project of four institutes of theKarlsruhe Research Center funded by the Hermann-von-Helmholtz Associationof German Research Centers.

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14 Air Quality Monitoring And