Gas sensing based on the dynamic nonlinear responses of a semiconductor gas sensor: dependence on the range and frequency of a cyclic temperature change

Kinetics of Chemisorption on the Surface of Nanodispersed SnO2-PdOx and Selective Determination of CO and H2 in Air

In this work, the kinetics and mechanisms of the interaction of carbon monoxide and hydrogen with the surface of a nanosized SnO₂-PdO_x metal oxide material in air is studied. Non-stationary temperature regimes make it possible to better identify the individual characteristics of target gases and increase the selectivity of the analysis. Recently, chemometric methods (PCA, PLS, ANN, etc.) are often used to interpret multidimensional data obtained in non-stationary temperature regimes, but the analytical solution of kinetic equations can be no less effective. In this regard, we studied the kinetics of the interaction of carbon monoxide and hydrogen with atmospheric oxygen on the surface of SnO₂-PdO_x using semiconductor metal oxide sensors under conditions as close as possible to classical gas analysis. An analysis of the influence of catalytic surface temperature on the mechanisms of chemisorption processes allowed us to correctly interpret and mathematically describe the electrophysical characteristics of the sensor in the selective determination of carbon monoxide and hydrogen under nonstationary temperature conditions. The reaction mechanism is applied as well to the analysis of the operation scheme of the CO sensor TGS 2442 of Figaro Inc.

Electronic Nose Based on Temperature Modulation of MOS Sensors for Recognition of Excessive Methanol in Liquors

An electronic nose based on metal oxide semiconductor (MOS) sensors has been used to identify liquors with excessive methanol. The technique for a square wave temperature modulated MOS sensor was applied to generate the response patterns and a back-propagation neural network was used for pattern recognition. The parameters of temperature modulation were optimized according to the difference in response features of target gases (methanol and ethanol). Liquors with excessive methanol were qualitatively and quantitatively identified by the neural network. The results showed that our electronic nose system could well identify the liquors with excessive methanol with more than 92% accuracy. This electronic nose based on temperature modulation is a promising portable adjunct to other available techniques for quality assurance of liquors and other alcoholic beverages.

Selective Detection of Hydrogen Sulfide and Methane by a Single MOX-Sensor

In this paper, we describe a technique for the qualitative and quantitative analysis of such gas mixtures as “hydrogen sulfide in air” and “methane in air” using temperature modulation of a single metal oxide sensor. Using regression analysis in the principal components plane (PC1, PC2), we performed a selective determination of analytes on the minimum set of their concentrations in the training set, which is essential for solving practical problems. An important feature of this work is the difference in test gas concentrations from their concentrations in the training set. For the qualitative analysis of gas mixtures in a wide range of concentrations, we have developed an improved method for processing arrays of multidimensional data. For this improvement, we form a Mahalanobis neighborhood for polynomial regression lines constructed from the projection of training samples for each analyte on the (PC1, PC2) plane. Using the temperature modulation mode for the metal oxide sensor allowed us to increase its response when determining hydrogen sulfide by two to four orders of magnitude compared with the constant temperature mode.

Facile Quantification and Identification Techniques for Reducing Gases over a Wide Concentration Range Using a MOS Sensor in Temperature-Cycled Operation

Dedicated methods for quantification and identification of reducing gases based on model-based temperature-cycled operation (TCO) using a single commercial MOS gas sensor are presented. During high temperature phases the sensor surface is highly oxidized, yielding a significant sensitivity increase after switching to lower temperatures (differential surface reduction, DSR). For low concentrations, the slope of the logarithmic conductance during this low-temperature phase is evaluated and can directly be used for quantification. For higher concentrations, the time constant for reaching a stable conductance during the same low-temperature phase is evaluated. Both signals represent the reaction rate of the reducing gas on the strongly oxidized surface at this low temperature and provide a linear calibration curve, which is exceptional for MOS sensors. By determining these reaction rates on different low-temperature plateaus and applying pattern recognition, the resulting footprint can be used for identification of different gases. All methods are tested over a wide concentration range from 10 ppb to 100 ppm (4 orders of magnitude) for four different reducing gases (CO, H₂, ammonia and benzene) using randomized gas exposures.

ODOR MIXTURES AND CHEMOSENSORY ADAPTATION IN GAS SENSOR ARRAYS

Inspired by the process of olfactory adaptation in biological olfactory systems, this article presents two algorithms that allow a chemical sensor array to reduce its sensitivity to odors previously detected in the environment. The first algorithm is based on a committee machine of linear discriminant functions that operate on multiple subsets of the overall sensory input. Adaptation occurs by depressing the voting strength of discriminant functions that display higher sensitivity to previously detected odors. The second algorithm is based on a topology-preserving linear projection derived from Fisher's class separability criteria. In this case, the process of adaptation is implemented through a reformulation of the between-to-within-class scatter eigenvalue problem. The proposed algorithms are validated on two datasets of binary and ternary mixtures of organic solvents using an array of temperature-modulated metal-oxide chemoresistors.

Distinction of gases with a semiconductor sensor depending on the scanning profile of a cyclic temperature

A gas-sensing system based on a dynamic nonlinear response is reported to improve the selectivity in the sensor response toward sample gases. A cyclic temperature composed of fundamental and second harmonics was applied to a SnO(2) semiconductor gas sensor and the resulting conductance of the sensor was analyzed by fast Fourier transformation (FFT). The dynamic nonlinear responses to the gas species were further characterized depending on the scanning profile of the temperature. These characteristic sensor responses under the application of second-harmonic perturbation were theoretically considered based on a reaction-diffusion model for the semiconductor surface.

Chemical sensor based on nonlinearity: principle and application

Novel chemical sensors based on a time-dependent nonlinear response are reviewed. The strategy is to artificially mimic information transduction in living organisms. In taste and olfaction, information of chemical structure and concentration is transformed into nervous impulses in the nervous cell, i.e., time-dependent multi-dimensional information. Because the excitation and pulse generation in the nervous cell are typically nonlinear phenomena, it may be worthwhile to utilize the nonlinearity as the multi-dimensional information for molecular recognition. The principle of a "nonlinear" sensor is that a sinusoidal modulation is applied to a system, and the output signal is analyzed. The output signal of the sensor is characteristically deformed from the sinusoidal input depending on the chemical structure and concentration of the chemical stimuli. The characteristic nonlinear responses to chemical stimuli are discussed in relation to the kinetics of chemical compounds on the sensor surface. As a practical application, we introduced electrochemical sensors based on the differential capacitance, semiconductor gas sensors under the application of sinusoidal temperature or diffusion change, and a chemical sensor based on the spatio-temporal information. We demonstrated that mutli-dimensional information based on nonlinearity can provide quite useful information for the analysis of chemical species, even in the presence of another analyte or an interference with a single detector.