Gas sensors usually exhibit lack of selectivity, require frequent calibration, exhibit drift of the response and a lot of factors, such as humidity or ambient temperature, influence their performance. Different approaches can be used to overcome this shortcomings. Building arrays of different sensors and usage of pattern recognition methods to analyze responses of elements in array is a popular approach. In this paper the approach of using a single sensor and special measurement techniques will be presented. Instead of increasing the number of sensors an additional information, needed to improve the properties of the sensor, is obtained from the response signal of a single sensor.
The practical application of human nose for fragrance recognition is severely limited by the fact that our sense of smell is subjective and gets tired easily. Consequently, there is considerable need for an instrument that can be a substitution of the human sense of smell. Electronic nose devices from the mid 1980s are used in growing number of applications. They comprise an array of several electrochemical gas sensors with partial specificity and a pattern recognition algorithms. Most of such systems, however, is only used for qualitative measurements. In this article usage of such system in quantitative determination of gas concentration is demonstrated. Electronic nose consist of a sensor array with eight commercially available Taguchi type gas sensor. Performance of three different pattern recognition algorithms is compared, namely artificial neural network, partial least squares regression and support vector machine regression. The electronic nose is used for ammonia and nitrogen dioxide concentration determination.
A periodic temperature modulation using sinusoidal heater voltage was applied to a commercial SnO2 semiconductor gas sensor. Resulting resistance response of the sensor was analyzed using a feature extraction method based on Fast Fourier Transformation (FFT). The amplitudes of the higher harmonics of the FFT from the dynamic nonlinear responses of measured gas were further utilized as an input for Artificial Neuron Network (ANN). Determination of the concentration of chlorine was performed. Moreover, this work evaluates the sensor performance upon sinusoidal temperature modulation.
KEYWORDS: Gas sensors, Feature extraction, Sensors, Electronics, Modulation, Gases, Temperature metrology, Statistical analysis, Data analysis, Data acquisition
Gas analyzers based on gas sensors are the devices which enable recognition of various kinds of volatile compounds. They have continuously been developed and investigated for over three decades, however there are still limitations which slow down the implementation of those devices in many applications. For example, the main drawbacks are the lack of selectivity, sensitivity and long term stability of those devices caused by the drift of utilized sensors. This implies the necessity of investigations not only in the field of development of gas sensors construction, but also the development of measurement procedures or methods of analysis of sensor responses which compensate the limitations of sensors devices. One of the fields of investigations covers the dynamic measurements of sensors or sensor-arrays response with the utilization of flow modulation techniques. Different gas delivery patterns enable the possibility of extraction of unique features which improves the stability and selectivity of gas detecting systems. In this article three utilized flow modulation techniques are presented, together with the proposition of the evaluation method of their usefulness and robustness in environmental pollutants detecting systems. The results of dynamic measurements of an commercially available TGS sensor array in the presence of nitrogen dioxide and ammonia are shown.
Many types of yttria-stabilized zirconia (YSZ) based gas sensors have been explored extensively in recent years. Great attention have been directed to mixed-potential-type gas sensors. It is due to growing concerns with environmental issues. Not without a significance is the fact of very attractive performance of this type of sensor allowing to detect low concentration of pollutant gases. In this paper two types of YSZ based mixed-potential planar sensors were investigated, with platinum electrode painted using commercial paste and with spin coated platinum layer. Both types had second electrode in the form of porous gold. Measurements were performed at 400 °C in synthetic air and different concentrations of SO2. Gas flow was set to 100 cm3min-1 and the concentration of 50 ppm SO2 was tested. During this measurements the sensor was sintered in-situ at increasing temperatures. Sensor with 100 nm spin-coated platinum layer sintered at 700 °C was shown to exhibit two times smaller response than sensor with 5 μm porous electrode, while consisting of over 20 times smaller amount of Pt. The influence of sintering temperature on electrical conductivity of platinum films was also examined. Moreover, the platinum microstructure was investigated using SEM microscopy.
One of the types of gas sensors used for detection and identification of toxic-air pollutant is an electro-catalytic gas sensor. The electro-catalytic sensors are working in cyclic voltammetry mode, enable detection of various gases. Their response are in the form of I-V curves which contain information about the type and the concentration of measured volatile compound. However, additional analysis is required to provide the efficient recognition of the target gas. Multivariate data analysis and pattern recognition methods are proven to be useful tool for such application, but further investigations on the improvement of the sensor’s responses processing are required. In this article the method for extraction of the parameters from the electro-catalytic sensor responses is presented. Extracted features enable the significant reduction of data dimension without the loss of the efficiency of recognition of four volatile air-pollutant, namely nitrogen dioxide, ammonia, hydrogen sulfide and sulfur dioxide.
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