Tin Oxide Gas Sensors Research Articles

Optimal configuration of a thermally cycled gas sensor array with neural network pattern recognition

Abstract Through the application of novel thermal modulation, pre-processing and feature extraction techniques the performance of an 8-element tin oxide gas sensor array has been significantly enhanced when applied to the task of classifying the aromas of three loose leaf teas. Array signatures were generated by thermally cycling the sensor array over the temperature range 250–500°C, whilst exposing the array to the odorous headspace of the three teas. The application of thermal modulation and an enhanced feature extraction algorithm, generating 208 parameters, proved to be highly successful giving a cross-validated classification rate of 90% for unseen samples of the three classes of tea. In comparison, a fixed temperature steady state metric, based upon the same array of sensors, yielded a cross-validated classification rate of only 69%. Furthermore, using a novel genetic algorithm optimisation technique to identify a near-optimal sensor parameter configuration for the task of tea classification, it was shown that a correct classification rate of 93% could be achieved with only 21 dynamic parameters.

Transient response of thick-film tin oxide gas-sensors to multicomponent gas mixtures

Abstract In this study we analyse the conductance transient of thick-film tin oxide sensors under a step change in a multicomponent gas mixture concentration. We discuss a new nonlinear diffusion model which accurately depicts experimental sensor behaviour in the low-medium gas concentration range. This model allows a set of n concentration-independent parameters ( τ ′ 1 , τ ′ 2 , ..., τ ′ n ) to be estimated. Each parameter is characteristic of a species present in the n -component mixture and varies from one sensor to another. By adjusting conductance transient measurements of binary mixtures (ethanol, toluene and ethylbenzene) to theoretical transients, we obtained τ ′ i values. This new set of constants may give useful information for identifying the components of gas mixtures.

Outputs of Plural Tin Oxide Gas Sensors for Compound Gaseous Indoor-air Pollutants

Output patterns of four types of tin oxide gas sensors for compound gaseous indoor air-pollutants are investigated. The sensor patterns prefer higher gas-sensitivity characteristics when there is a compound gaseous pollutant including two kinds of gases. After the experimental system detects a lower gas-sensitivity pattern in a residence, it can detect a higher one. The reverse is impossible. Three NDVs (NDVA, NDVC, NDVN) characteristics are also adopted to increase the reliability of the system. NDVA characterisitic decreases for a few minutes and gets back to an original state or under the state when an alcoholic gas generates. The system can identify whether the alcoholic gas is generating or not in an indoor environment by using NDV characteristics with high reliability.

A modeling of tin-oxide gas sensor responses for temperature and humidity changes

Output responses of an tin-oxide gas sensor depends on indoor temperature/humidity, but not on the reacted gas concentrations. The modeling of a gas sensor characteristics curve between gas concentrations and voltage was attempted with respect to a temperature/humidity change, to remove the influence of indoor environment from sensor responses. A characteristics curve was derived as an inverse proportion function and approximated by undecided variables with a plane approximation of temperature/humidity coefficients. The results of this study in a home indicate that each temperature/humidity coefficient is approximate to linear equation in error ranges of 5% and approximate functions of a characteristics curve are in a mean error range of about 1% for a reserved region.

An adaptive neural network topology for degradation compensation of thin film tin oxide gas sensors

A hybrid neural network for gas sensing application is presented, which is based on adaptive resonance theory. The network may use as an input one or more gas sensors. The basic feature of the proposed topology is its ability to learn a new pattern or form a new pattern category at any point of its operation. At the same time it retains knowledge of previously learned patterns or pattern categories. This adaptation ability helps the network to solve many of the problems encountered with tin oxide gas sensors, like instabilities and degradation. The functionality of the network is presented in the two cases of one and four input providing gas sensors. The experimental results show that the effect of sensor degradation maybe compensated by the proposed network topology.

Conductance-transient analysis of thick-film tin oxide gas sensors under successive gas-injection steps

Dynamically characterizing the electrical response of chemical sensors is a strategy to improve their selectivity. In this paper, the conductance transient of thick-film tin oxide gas sensors under successive step rises in the gas concentration is analysed. The transient response of three thick-film tin oxide gas sensors in the presence of organic solvent vapours (toluene, o-xylene and ethanol) has been measured. As a main result, it has been found that, although the first step in the conductance-transient rise time is concentration independent, for the second and successive steps, decreases and becomes concentration dependent. This behaviour has been modelled successfully with a nonlinear diffusion - reaction model. The measured set of rise times is characteristic for each sensor - gas pair and may give useful information for gas recognition. Furthermore, the successive injection of gas samples during the sensor-calibration procedure may be a time-saving technique.

Effect of surface catalysts on the long-term performance of reactively sputtered tin and indium oxide gas sensors

Long term performance of tin and indium oxide thin films with palladium and platinum deposited on their surface as catalysts was studied using a standardization method proposed recently by other researchers. A simple gas classification algorithm was used to investigate the validity of the standardization method proposed and to study the effect of doping type and thickness onto the surface of SnO x and InO x films on their selectivity and stability characteristics. It was found that: (a) the proposed method is reliable for the assessment of sensor degradation; and (b) sensors with relatively thick additive layer are more reliable.

Qualitative and quantitative analysis of volatile organic compounds using transient and steady-state responses of a thick-film tin oxide gas sensor array

Quantitative analysis of gases, by means of semiconductor sensor arrays and pattern-recognition techniques such as artificial neural networks, has been the goal of a great deal of work over the last few years. However, the lack of selectivity, repeatability and drifts of the sensors, have limited the applications of these systems to qualitative or semi-quantitative gas analysis. While the steady-state response of the sensors is usually the signal to be processed in such analysis systems, our method consists of processing both, transient and steady-state information. The sensor transient behaviour is characterised through the measure of its conductance rise time ( Tr), when there is a step change in the gas concentration. Tr is characteristic of each gas/sensor pair, concentration-independent and shows higher repeatability than the steady state measurements. An array of four thick-film tin oxide gas sensors and pattern-recognition techniques are used to discriminate and quantify among ethanol, toluene and o-xylene [concentration range: 25, 50 and 100 ppm]. A principal component analysis is carried out to show qualitatively that selectivity improves when the sensor behaviour is dynamically characterised. The steady-state and transient conductance of the array components are processed with artificial neural networks. In a first stage, a feed-forward back-propagation-trained ANN discriminates among the studied compounds. Afterwards, three separate ANN (one for each vapour) are used to quantify the previously identified compound. Processing data from the dynamic characterisation of the sensor array, considerably improves its identification performance, rising the discrimination success rate from a 66% when only steady-state signals are used up to 100%.

Signal processing for improving gas sensor response time

In this paper, we describe a system for reducing the response time of gas sensor arrays. The system uses analog signal processing techniques to analyze the transient response of sensory inputs sufficiently to discriminate among chemicals of interest before steady state is reached. The signal processing thresholds the sensory inputs at periodic intervals during the transient response, thereby producing a binary wavefront that is stored and retained for discrimination tasks. Experimental results described in this paper effectively reduce the response time of an array of discrete tin-oxide gas sensors from 10 to 2 s; experimental data are consistent across a range of concentrations for a variety of reducing gases. We expect that equivalent improvements in response time will also be possible for integrated microsensor systems using the same techniques described in this work.

Detection of Relative Gaseous Indoor Air-pollution by Tin Oxide Gas Sensor using Production System

Gaseous indoor air-pollutants are detected by a tin oxide gas-sensor using the OPS5 system. The system is one of several production systems. The grade of pollution can be indicated more correctly by recognizing types of human behaviors, awaking and sleeping etc. The standard level for gaseous indoor air-pollution is computed by sensor outout data in last 3 weeks. The current input data is compared with the level and the grade of pollution displayed. There are 15 rules to help recognizing indooor air-pollution. Rete algorithm is adopted to match working memory elements and condition parts of rules in production memory quickly.

Microstructural characterization of nanograin tin oxide gas sensors

Tin oxide thin films are applied as detectors for monitoring nitrogen dioxide and carbon monoxide levels in the atmosphere. Sensor elements are fabricated by depositing nanocrystalline tin oxide on polycrystalline alumina substrates by a reactive sputtering technique. Their response is dependent on the operating temperature and can be modified by doping with a catalyst. Microstructural examination of these devices has been carried out using low voltage scanning electron microscopy, conventional and high resolution transmission electron microscopy and atomic force microscopy. The sensitivity is improved due to the ultrafine grain size (∼10 nm, typically) of the active layer and the surface roughness of the substrate, which together result in an effective increase of the reactive area by a factor of 2–3. Permeation along intercolumnar fissures and enhanced grain boundary diffusion in the tin oxide layer may also play a significant role in assisting gas migration. The presence of crystallographic shear planes in the nanocrystalline grains will modify the local distribution of oxygen vacancies, which are the principal carrier donors and traps.

Multiple tin-oxide gas sensing system to monitor the indoor of the aged of solitary living

Four kinds of tin-oxide gas sensors are installed in a home of a solitary elderly person and the sensor outputs are monitored to know the activity of the aged. These sensors are designed to detect a combustible gas(CGS), ammonia(AMS), carbon monoxide(COS) and nitrogen dioxide(NXS), respectively. As the monitoring results indicate, outputs of the sensors are related with daily living patterns. NDVA indeces especially have periodicity. NDVA is a value which is obtained by an output of AMS divided by an output of CGS, namely NDVA=V(AMS)/V(CGS). The periodical characteristics indicate the daily living patterns of the aged and the system can judge whether the person has been spending a normal life or not. The indoor activity(IA) is derived as a sum total of square of each slipped value from a mean value of a daily sensor-level. The average of IA is about 12 in the experimental house.

Identification for gaseous indoor air-pollutants using NDV

Four types of tin oxide gas sensors are used to identify indoor air-pollutants in a domestic environment. They are a combustible gas, ammonia gas, carbon monoxide gas and nitrogen oxide gas sensors. Eight pollutants are investigated using these sensors. This system can identify one kind of pollutant out of these pollutants. Three NDVs are adopted to identify a pollutant. NDV is a value which an output of a gas sensor is divided by the output of combustible gas sensor, namely NDV = V( a sensor) V( combustible gas sensor) . The characteristic of NDV varies with a pollutant. It becomes clear that three types of NDVs are effective in identifying one of these eight indoor air-pollutants.

Analysis of the conductance transient in thick-film tin oxide gas sensors

In this paper, we analyse the conductance transient of a Taguchi TGS-822 sensor under a step change in the vapour concentration. A diffusion-limited range in the conductance transient is observed. Adjustments between the theoretical calculations based on a non-linear diffusion-reaction model and the experimental results alloy a constant, τ′, to be estimated, which is independent of the final conductance value and depends, among other parameters, on the effective diffusion coefficients of vapours in the porous tin oxide sensor. From transient measurements of organic solvents (benzene and o-xylene) we have obtained τ′ values that are independent of concentration and characteristic for each vapour. This new parameter can give useful information for gas/vapour recognition.

Thickness Dependence of Microstructure and Hall Effect in Tin Oxide Gas Sensor Films

Thickness Dependence of Microstructure and Hall Effect in Tin Oxide Gas Sensor Films

新しい室内空気汚染検知システムの開発

新しい室内空気汚染検知システムの開発

Sensing mechanism in tin oxide-based thick-film gas sensors

A model has been developed for the sensing mechanism of metal oxide-based thick-film gas sensors. The model explains the behaviour of the sensor conductance as a function of the concentration of test gas and the operating temperature of the sensor. Using the Schottky-barrier conduction mechanism through grain boundaries, a relationship between the degree of surface coverage θ and the conductance G has been obtained. To relate the conductance with the concentration of the gas, the Freundlich adsorption isotherm for gases and vapours on a solid surface has been used. The isotherm relates the degree of surface coverage θ with the partial pressure of the gas (concentration). By eliminating θ, an expression relating the variation of G with concentration has been obtained. To study the validity of the model, a thick-film Pd-doped tin oxide gas sensor has been fabricated and tested with propanol (C 3H 7OH). The variation in the conductance with changes in concentration and temperature has been observed. The observed data show an excellent fit with the developed model. Using the experimental data, the constants of the theoretical equation have also been evaluated.

Modelling and simulation of abnormal behaviour of thick-film tin oxide gas sensors in CO

A new model is proposed that takes into account oxygen vacancies in tin oxide resistive-type, assuming that lattice oxygen modifies the rate of oxygen adsorption. Applying this hypothesis in a Monte Carlo simulation, effects observed in thick-film samples are explained. Morevoer, computational techniques have been used in order to simulate different thick-film structures, and the role of both surface coverage and reduction in the sensing mechanism is investigated. The simulation results are in good qualitative agreement with the experimental results obtained from our samples. In particular, phenomena like undershoot and overshoot of the sample's resistance, very long recovery times and poisoning of the sensor surface are discussed.

Study of fire-alarm system using plural semiconductor and quartz-resonator gas sensors

Recognition of an output pattern from many sensors with different characteristics is promising for realizing an artificial odour-sensing system from the biomimetic viewpoint. The authors have applied the method to a fire alarm so that its reliability could be raised. Although it is easy to discriminate the smell of burnt paper from those of unburnt kerosene and gasoline, the smell of burnt paper cannot be distinguished from that of burnt cigarette when five tin oxide gas sensors are used. The introduction of a quartz resonator coated with lipid is useful to enlarge the pattern separation.

Tin oxide (SnO x) alcohol sensor from metal organic decomposed (MOD) thick film

Since the invention of the Taguchi tin oxide gas sensor [l], numerous papers [2-4] have been published regarding the compositions, fabrications, catalysts, additives, mechanisms and selectivity of semiconductortype solid-state gas sensors, which can be classified according to the methods of fabrication into the following types: sintered bulk type [l], thick-film type [5], and thin-film type [6,7]. Commercial products nowadays are mostly the sintered bulk and thick-film types. The ditliculties in making the sintered bulk-type sensors arise from the contradictory effects of treatments between achieving strong mechanical strength and retaining a sufficient surface area. Thick-film-type sensors have more or less the same problems, except that the availability of IC techniques helps in mass production. Thin-tilm techniques provide many possibilities for future generations of sensor development; however, at present thin&t sensors are not fully commercialized. The metallo-organic decomposition (MOD) method [8, 91 for the deposition of metal oxide thick tihn is easy to apply and inexpensive. In this paper investigations of a sensor fabricated by the MOD and thick-film methods are reported. Further application-related testing results were reported elsewhere [lo]. /