Sensor Response Features Research Articles

On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WO3 and WS2 Nanomaterials.

This paper presents a methodology to quantify oxidizing and reducing gases using n-type and p-type chemiresistive sensors, respectively. Low temperature sensor heating with pulsed UV or visible light modulation is used together with the application of the fast Fourier transform (FFT) to extract sensor response features. These features are further processed via principal component analysis (PCA) and principal component regression (PCR) for achieving gas discrimination and building concentration prediction models with R2 values up to 98% and RMSE values as low as 5% for the total gas concentration range studied. UV and visible light were used to study the influence of the light wavelength in the prediction model performance. We demonstrate that n-type and p-type sensors need to be used together for achieving good quantification of oxidizing and reducing species, respectively, since the semiconductor type defines the prediction model’s effectiveness towards an oxidizing or reducing gas. The presented method reduces considerably the total time needed to quantify the gas concentration compared with the results obtained in a previous work. The use of visible light LEDs for performing pulsed light modulation enhances system performance and considerably reduces cost in comparison to previously reported UV light-based approaches.

Design and Development of Instrumentation for Remote Detection of Hydrogen Using Metal Oxide Sensor

Indigenous instrumentation setup was designed, developed and demonstrated for remote detection of hydrogen using metal oxide (MO) sensors. The metal oxide sensor was developed to sense the low concentration of hydrogen. The hardware developed was based on a function generator configuration and uniquely designed to give low excitation voltage across the sensor to improve the life of the sensor and reduces the number of components. The data acquisition software was developed to collect the sensor response in PC remotely. The hardware was calibrated, tested with standard resistance and found to give an accuracy of ±0.5% and high stability and reliability. The sensor response towards hydrogen was measured by the instrument. Different features of sensor response viz., sensitivity, response time and recovery time were studied towards different concentrations of hydrogen at optimized temperature.

A Minimum Distance Inlier Probability (MDIP) Feature Selection Method to Improve Gas Classification for Electronic Nose Systems

For electronic nose systems to obtain meaningful information from sensor data, sensor response features are first extracted for further signal processing. However, redundant features may diminish the accuracy of gas classification. To solve this problem, a minimum distance inlier probability (MDIP) feature selection (FS) method is proposed. By incorporating the intrinsic properties of features and ranking strategy, MDIP can efficiently eliminate redundant features and provide better classification accuracy. The performance of the method was validated on two open-access datasets that provide information for system variation and sensor drift problems, respectively. Experimental results revealed that the average classification accuracy for the two datasets was higher by 46.1% and 37.5%, respectively, with the MDIP method.

Development of instrumentation setup for discrimination of analytes using principal component analysis

An indigenous instrumentation set up was designed and developed for the discrimination of analytes. The instrumentation consists of a sensor array, measurement unit, data acquisition software, and a pattern recognition tool. The conductivity changes of three metal oxide sensors (ZnO (S1), In2O3 (S2) and SnO2 (S3)) with different analytes (hydrogen (A1), formaldehyde (A2) and hydrazine (A3)) were measured using in-house developed instrumentation and is sent to PC to store the sensor data for discrimination of analytes using pattern recognition technique. The data acquisition software collects the data in PC to train the data for discrimination of analytes using pattern recognition tool in real time. The various features of sensor response were derived from the response curve and the Principal Component Analysis (PCA) algorithm was implemented in software to extract the information from sensor response data for discrimination of analytes. The instrument set up is able to distinguish among hydrogen, formaldehyde, and hydrazine in real time.

(Invited) Cross-Reactive Graphene and Metal Oxide Sensors for Gas Discrimination

Discriminating similar molecules remains a very challenging problem for semiconductor gas sensors. Here, we report a method to achieve precise gas discrimination of similar chemical vapors (mesitylene, o-xylene, and toluene) by using cross-reactive arrays consisting of metal oxide semiconductor and graphene sensors. It is difficult to identify these three chemicals as they have very similar responses to these sensors. Through a cross-reactive Principal Component Analysis (PCA) of the sensor response features, however, the discrimination accuracy improved from about 70% with a single gas sensor to almost 100% with the cross-reactive sensor array. Such a precise discrimination and the low-cost planar process make this approach a very attractive candidate for smart gas sensing and for future Internet of Things (IoT) applications.

Precise gas discrimination with cross-reactive graphene and metal oxide sensor arrays

Discriminating similar molecules remains a very challenging problem for semiconductor gas sensors. Here, we report a method to achieve precise gas discrimination of similar chemical vapors (mesitylene, o-xylene, and toluene) by using cross-reactive arrays consisting of metal oxide semiconductor and graphene sensors. It is difficult to identify these three chemicals as they have very similar responses to these sensors. Through cross-reactive Principal Component Analysis of the sensor response features, however, the discrimination accuracy improved from about 70% with a single gas sensor to almost 100% with the cross-reactive sensor array. Such a precise discrimination and the low-cost planar process make this approach a very attractive candidate for smart gas sensing and for future Internet of Things applications.

Ozone monitoring by micro-machined sensors with WO 3 sensing films

Tungsten trioxide sensing films were deposited by rf sputtering onto silicon micro-machined substrates. The sensor substrate consisted of four-element integrated micro-hotplate arrays constructed using micro-systems technology. The sensing films were deposited using two sputtering technologies. The first one was a standard deposition without interruption of film deposition process. The second one included three interruptions of the process. It is shown that the sensor response to ozone is enhanced by the use of the second approach. Moreover, the features of sensor response to ozone demonstrate that fast responses and good sensor stability at low ozone concentrations are achieved using the micro-sensors.

A Layered Surface Acoustic Wave ZnO/LiTaO<SUB>3</SUB> Structure with a WO<SUB>3</SUB> Selective Layer for Hydrogen Sensing

A layered surface acoustic wave (SAW) gas sensor is investigated for hydrogen concentrations of 0.5% and 1% in air at different operational temperatures. The device structure is based on a layered SAW device fabricated on a 36° X-cut, Y-propagating LiTaO3 substrate. A ZnO guiding layer is employed to increase sensor sensitivity, and a thin film of WO3 provides the selectivity toward hydrogen gas. Such a structure has the advantage of confining the acoustic wave energy at the surface of the device, which increases the sensitivity of the device. In this paper, the fabrication of the ZnO/36° YXLiTaO3 sensor is described, and the sensor's response features are analyzed in terms of response time, recovery time, and response magnitude as a function of operational temperature. Furthermore, the hydrogen interaction with the WO3 selective layer is briefly described.

A surface acoustic wave mercury vapor sensor

A sensor for the in situ detection and measurement of low concentrations of gaseous mercury is presented. The sensor is based upon a dual delay line SAW oscillator with a gold-coated delay path. Gaseous mercury interacts rigorously with the gold film, forming an amalgam. The resulting increase in film mass is manifested as a decrease in oscillation frequency. Measurement of gas concentration is achieved by differentiating the sensor response at room temperature or by operating the sensing element at a temperature where gas-film reaction kinetics result in equilibrium rates of mercury amalgamation and desorption. This equilibrium value of amalgamated mercury is highly dependent upon the gas concentration. Thus, the delay line oscillation frequency is a sensitive measure of gaseous mercury concentration. Responses of this sensor to gaseous mercury concentrations in the ppb range are presented. The sensor response features are analyzed in terms of response shape, response magnitude, response time, and linearity at 25/spl deg/C and 200/spl deg/C.