Single Metal Oxide Sensor Research Articles

Chemisorption of hydrogen on metal oxides with palladium (II) oxide additives

Chemisorption can lead to a change in electrical conductivity, which allows using semiconductor materials to create gas sensors. On the other hand, semiconductor sensors can be used as devices for studying the chemisorption of gases. In this work, we set two tasks. The first of them was to create a sensor for the selective determination of hydrogen in air, including in a mixture with other gases. The second task was to determine the mechanism of hydrogen chemisorption on the surface of metal oxides with palladium (II) oxide additives. We obtained and characterised nanodispersed materials based on SnO2 and WO3 with additions of 3% PdO by weight. We compared the electrical characteristics of these materials in stationary temperature conditions as well as with temperature modulation in the presence of hydrogen, methane, and their mixtures. The specific features of hydrogen chemisorption were determined on the surface of metal oxide semiconductors based on SnO2 and WO3 with PdO additives in the temperature modulation mode of the sensor. It was shown that the extrema in the dependence of the sensor’s electrical conductivity on temperature can be explained by the contribution of proton transfer to the total electrical conductivity. The presence of extrema in the electrical conductivity of sensors, which were observed in thermal modulation mode in the presence of hydrogen, allows conducting qualitative and quantitative analysis of not only single-component gas systems, but also of mixtures of hydrogen with other analyte gases. In particular, this work demonstrated that it was possible to determine the composition of a hydrogen-methane mixture in air using a single metal oxide sensor. The advantage of this approach is that multidimensional data arrays can be easily processed.

Sensing and Delineating Mixed-VOC Composition in the Air Using a Single Metal Oxide Sensor

Monitoring volatile organic compounds (VOCs) places a crucial role in environmental pollutants control and indoor air quality. In this study, a metal-oxide (MOx) sensor detector (used in a commercially available monitor) was employed to delineate the composition of air containing three common VOCs (ethanol, acetone, and hexane) under various concentrations. Experiments with a single component and double components were conducted to investigate how the solvents interact with the metal oxide sensor. The experimental results revealed that the affinity between VOC and sensor was in the following order: acetone > ethanol > n-hexane. A mathematical model was developed, based on the experimental findings and data analysis, to convert the output resistance value of the sensor into concentration values, which, in turn, can be used to calculate a VOC-based air quality index. Empirical equations were established based on inferences of vapour composition versus resistance trends, and on an approach of using original and diluted air samples to generate two sets of resistance data per sample. The calibration of numerous model parameters allowed matching simulated curves to measured data. Therefore, the predictive mathematical model enabled quantifying the total concentration of sensed VOCs, in addition to estimating the VOC composition. This first attempt to obtain semiquantitative data from a single MOx sensor, despite the remaining selectivity challenges, is aimed at expanding the capability of mobile air pollutants monitoring devices.

Noise spectroscopy data analysis-based gas identification with a single MOX sensor

• Noise spectroscopy-based gases identifications methods are evaluated. • Noise measurements are performed on tungsten trioxide (WO 3 ) gas microsensors. • Precise spectral decomposition of measured noise responses is applied. • We can identify three gases using noise spectroscopy applied to a single gas sensor. Air quality monitoring and analysis have become a major issue in recent years. Metal oxide (MOX) gas sensors are very sensitive, due to high variability of their resistivity in presence of gas. However, they are not selective, i.e. it is not possible to determine the nature and concentration of the gas using only variations in the resistance of the sensor. Noise spectroscopy is one of the solutions to improve selectivity. In this paper, we evaluate recent noise spectroscopy-based gases identifications methods, to distinguish the nature of different gases using a single MOX sensor. From noise measurements performed on MOX gas sensors with tungsten trioxide (WO 3 ) sensing layer, under several nitrogen dioxide (NO 2 ), ozone (O 3 ) and carbon monoxide (CO) concentrations in dry air, we have created a database. This database is increased by extracting the spectral attributes of noise responses, and then reduced by using principal component analysis to extract only useful information. The results obtained in this study demonstrated that it is possible to distinguish three air quality gases with only one single WO 3 sensor.

Accurate detection and discrimination of pollutant gases using a temperature modulated MOX sensor combined with feature extraction and support vector classification

• This work proposes a data-driven methodology for the detection and identification of multiple pollutants and their mixtures using a single MOX sensor. • The approach consists of augmenting the raw data from the sensors device by the extraction of time-domain features. • The extracted features are ranked using the ReliefF algorithm. This ranking is then used to select the best features to discriminate the pollutants. • Highly encouraging results from the Support Vector Classification demonstrate the accuracy of the proposed approach. Gas detection and discrimination have been, until recently, sensors-specific, with different sensors and techniques used for each of the gases. In this work, we describe a novel approach relying on a single physical sensor in conjunction with data-driven algorithms for detecting the presence of one of the three dangerous gases: CO, NO 2 , and O 3 individually or in mixtures. The approach uses a single Metal Oxide (MOX) sensor coupled with two heaters in its hardware part. Then, its software part uses a supervised machine learning model. The sensor is exposed to the different gases and their mixtures and would react accordingly with a change in its electric signals. These raw signals, along with the readings from the heaters, constitute the primary dataset for the discrimination. To further enhance the classification results, the raw dataset is augmented by calculating several time-domain features of each of the measurements. Then, the features are ranked, and the ones with the best results to solve the classification problem are selected. Once the pretreatment of the data is finished, the selected features are used to train and validate a multi-Support Vector Machine model. Finally, the results showcased in this paper highlight the effectiveness of the proposed approach.

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.

A Linear-Quadratic Model for the Quantification of a Mixture of Two Diluted Gases with a Single Metal Oxide Sensor.

The aim of our work is to quantify two gases (acetone and ethanol) diluted in an air buffer using only a single metal oxide (MOX) sensor. We took advantage of the low selectivity of the MOX sensor, exploiting a dual-temperature mode. Working at two temperatures of the MOX sensitive layer allowed us to obtain diversity in the measures. Two virtual sensors were created to characterize our gas mixture. We presented a linear-quadratic mixture sensing model which was closer to the experimental data. To validate this model and the experimental protocol, we inverted the system of quadratic equations to quantify a mixture of the two gases. The linear-quadratic model was compared to the bilinear model proposed in the literature. We presented an experimental evaluation on mixtures made of a few ppm of acetone and ethanol, and we obtained a precision close to the ppm. This is an important step towards medical applications, particularly in terms of diabetes, to deliver a non-invasive measure with a low-cost device.

The use of a gas chromatograph coupled to a metal oxide sensor for rapid assessment of stool samples from irritable bowel syndrome and inflammatory bowel disease patients

There is much clinical interest in the development of a low-cost and reliable test for diagnosing inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS), two very distinct diseases that can present with similar symptoms. The assessment of stool samples for the diagnosis of gastro-intestinal diseases is in principle an ideal non-invasive testing method. This paper presents an approach to stool analysis using headspace gas chromatography and a single metal oxide sensor coupled to artificial neural network software. Currently, the system is able to distinguish samples from patients with IBS from patients with IBD with a sensitivity and specificity of 76% and 88% respectively, with an overall mean predictive accuracy of 76%.

Temperature optimization of metal oxide sensor arrays using Mutual Information

The sensitivity and selectivity of metal oxide (MOX) gas sensors change significantly when the sensors operate at different temperatures. While previous investigations have presented systematic approaches to optimize the operating temperature of a single MOX sensor, in this paper we present a methodology to select the optimal operating temperature of all the MOX sensors constituent of a gas sensor array based on the multivariate response of all the sensing elements. Our approach estimates a widely used Information Theory measure, the so-called Mutual Information (MI), which quantifies the amount of information that the state of one random variable (response of the gas sensor array) can provide from the state of another random variable representing the gas quality. More specifically, our methodology builds sensor models from experimental data to solve the technical problem of populating the joint probability distribution for the MI estimation. We demonstrate the relevance of our approach by maximizing the MI and selecting the best operating temperatures of a four-sensor array sampled at 94 different temperatures to optimize the discrimination task of ethanol, acetic acid, 2-butanone, and acetone. In addition to being applicable in principle to sensor arrays of any size, our approach gives precise information on the ability of the system to discriminate odors according to the temperature of the MOX sensors, for either the optimal set of temperatures or the temperatures that may render inefficient operation of the system itself.

Evaluation of fish spoilage by means of a single metal oxide sensor under temperature modulation

In this paper the feasibility of using metal oxide gas sensor technology for evaluating spoilage process for sea bream ( Sparus aurata) is explored. It is shown that a single sensor under temperature modulation is able to find a correlation with the fish spoilage process. Results are obtained in real frigorific storage conditions; that is, at low measurement temperatures with variations of relative humidity.

One-sensor electronic olfactometer for rapid sorting of fresh fruit juices

A low-cost electronic olfactometer was developed, based on virtual sensors array (an array of signals from a single metal oxide sensor) and on our proprietary semi-automated sampling system. This concept enables us to drastically improve the sensitivity and selectivity of the entire olfactometer to make it suited to food products analysis. Each step of the prototype was checked separately in earlier work, and finally the system performance was evaluated first on pure chemicals, and then at-line for grape juice during grape-harvesting in Champagne. It is shown that over a 3-week vintage period, the drift of the unique-sensor was quite low (absolute baseline value: avg., 2152; std., 150; response to test solution: avg., 5.08, std., 0.39), and the transient response to the temperature steps provided information for the discrimination and mapping of these products. The results correspond well with the separative quantification of the aroma by high speed GC analysis made in parallel on the same samples.