J.A. Agapito

The effect of additives in tin oxide on the sensitivity and selectivity to NOx and CO

Abstract Undoped and Pt-, In- and Al-doped, SnO 2 film gas sensors prepared by reactive sputtering (r.f.) were tested for detection of NO x and CO, and their sensitivities were studied in dry synthetic air. The concentrations of CO vary from 50 to 1000 ppm and that of NO x from 2 to 70 ppm. The selectivity of the sensors was studied to determine the response of NO x when CO was the interfering gas. The sensitivity to gases was studied in the temperature range from 300 to 675 K in order to find the optimum detection temperature. The experiments to determine the interference effects of CO on the response to NO x were carried out at 525 K. Measurements of electrical conductance show that these films have a sensitivity higher than 1000% to NO x at temperatures below 575 K and 50 ppm gas concentration. At the same temperature the sensitivities to CO at 1000 ppm always remain lower than 100%. The best sensitivity to NO x is achieved with sensors doped with Al or In and operated in the range 300–525 K. CO detection begins at 325 K, the best sensitivity being obtained with sensory doped with Pt. We observe that sensitivity, selectivity, optimum work temperature and response time depend on the dopants.

Use of complex impedance spectroscopy in chemical sensor characterization

Abstract The complex impedance method is a powerful tool in the characterization of gas adsorption in semiconductor structures used as gas sensors. Different tin oxide structures have been used. In tests of sensitivity to dry air, air + H2O, O2, etc, the operating temperature has increased to 175 °C. A variable frequency (10 μHz–32 MHz) a.c. voltage is applied to the sensor structure to perform the complex impedance spectrum measurements. The different parameters are a function of the type of gas in the atmosphere. A model has been proposed based on adsorption on the grain boundary.

Application of artificial neural networks to calculate the partial gas concentrations in a mixture

Abstract A way of improving the selectivity and sensitivity of semiconductor gas sensors is to use a multisensor array and analyze the whole response using pattern recognition methods, such as artificial neural network models (ANN). We use these models, not only to detect the individual components of the gas mixture (NO 2 and CO), but also to measure the concentration of both gases with sufficient accuracy. We present a systematic study to enhance all parameters of the neural network, including pre-processing techniques. Finally, we have implemented the enhanced neural network into a 68HC11 micro, showing the NO 2 and CO concentrations in real time on a digital display.

The influence of the tin-oxide deposition technique on the sensitivity to CO

Abstract The sensor parameters for CO detection on semiconductor films are strongly dependent on the film-preparation techniques. This is observed for semiconductor films prepared by sputtering and screen-printing. The influence of sensor-film porosity on the sensitivity to CO has also been observed. The effect of catalysts on semiconductor sensors combines the catalytic activity of the metals with the surface properties of the semiconductor oxides. It is very important that the catalyst is highly dispersed on the particles of the semiconductor and that the added quantity is adequate. We have studied the effect of catalysts (Pt and Pd on films prepared by reactive sputtering and screen-printing by analysing their respective roles. Depending on whether the catalyst is Pt or Pd, the catalytic mechanism is different. Wiht Pt the effect is purely chemical, whereas with Pd the effect is electronic. Surface analysis technique (GAXRD, XPS, EDX) have been used to compare the two different preparation techniques with regard to thickness, composition and morphology.

The interaction of oxygen with nanocrystalline SnO2 thin films in the framework of the electron theory of adsorption

Thin film nanocrystalline tin dioxide sensors have been prepared by reactive sputtering in order to test them at different oxygen partial pressures. Variations on the film conductivity covers almost two orders of magnitude and, in the range studied, obeys a power law relationship. The results are discussed in the framework of the electron theory of adsorption where the mean parameters are grain size, surface trap energy level and number of chemisorption sites. Variations of the last two parameters with temperature result in a very good agreement between predicted and observed values of sensitivity.

The interaction of different oxidizing agents on doped tin oxide

Abstract The interaction of NO2, NOx and O2 and O2 on an SnO2 thin-film surface is studied. Experiments have been carried out for different tin-oxide films, undoped and doped with Pt, In and Al, prepared by reactive sputtering (r.f.) on alumina substrates. These films should allow the development of detectors for nitrogen oxides at concentrations of about a few ppm. Conductance measurements have been done in a constant flow of inert atmosphere (N2) and in synthetic air containing oxidizing agents (NO2 and NOx) to study directly adsorbed NO2 and NO and their reaction with chemisorbed oxygen. The results are discussed in terms of surface reactions.

Integrated sensor array for gas analysis in combustion atmospheres

Abstract A semiconductor-based sensor array has been developed for highly toxic gas analysis in atmospheres with low oxygen content and in the presence of humidity and corrosive gases. The device consists of 16 discrete sensing elements formed by tin oxide thin layers deposited by sputtering. The sensor array was exposed to a gas mixture formed by N 2 , O 2 , CO 2 , H 2 S, HF, HCl and water vapour with a constant flow rate of 500 ml min −1 . Once their electric resistance at different temperatures between 150°C and 350°C was stabilized, the response to polluting gases coming from combustion processes (NO x , SO 2 , C 6 H 6 ) was studied.

Long-term reliability of sensors for detection of nitrogen oxides

Abstract In this work, the reliability and reproducibility of tin oxide as sensor material for the detection of nitrogen oxides as well as its behaviour when operating for long periods are studied. The experiments were carried out with thin films (3000 A) of tin oxide doped with aluminium. The sensors were operating for six months in synthetic air at the constant temperature of 525 K. During this time they were exposed with regularity to nitrogen oxide in order to determine the frequency at which calibration checks should be carried out and the frequency at which sensors may have to be replaced.

The interaction of low NO2 concentrations in air with degenerate nanocrystalline tin dioxide thin films

Abstract A multispecies adsorption model has been developed based on a generalization of the Volkenstein isotherm. The study of the dependence of the resistivity on the NO2 concentration was carried out by numerically solving the Poisson equation with pressure dependent boundary conditions. The model predicts a power law behaviour of the electrical response in a wide range of oxygen partial pressure with the exponent varying with the grain size. For the NO2 response there is also a power law dependence but saturation effects arise at high levels. In order to confirm the theory predictions, the effects of NO2 concentrations ranging from 50 to 800 ppb on the electrical resistivity of the nanocrystalline films were studied. The experiments resulted in excellent agreement with the theory.

STUDY OF THE INTERFERENCES OF NO2 AND CO IN SOLID STATE COMMERCIAL SENSORS

Abstract Commercial solid state gas sensors have been used to measure NO 2 and CO concentrations in the range of the first alarm thresholds for urban environments. Their mutual interference has been studied at three operating temperature ranges. The results showed that different sensitivities and selectivities can be obtained with the same type of sensors. This fact is used to make a multisensorial system based on commercial solid state gas sensors. Using artificial neural networks analysis, we detect the presence of NO 2 and CO gases, and even measure the concentration of both gases with a sufficient accuracy.