Characterization of Mixed xWO3(1-x)Y2O3 Nanoparticle Thick Film for Gas Sensing Application

Microstructural, topology, inner morphology, and gas-sensitivity of mixed xWO3(1-x)Y2O3 nanoparticles (x = 1, 0.95, 0.9, 0.85, 0.8) thick-film semiconductor gas sensors were studied. The surface topography and inner morphological properties of the mixed powder and sensing film were characterized with X-ray diffraction (XRD), atomic force microscopy (AFM), transmission electron microscopy (TEM), and scanning electron microscopy (SEM). Also, gas sensitivity properties of the printed films were evaluated in the presence of methane (CH4) and butane (C4H10) at up to 500 °C operating temperature of the sensor. The results show that the doping agent can modify some structural properties and gas sensitivity of the mixed powder

CO2 gas sensing properties of screen printed La2O3/SnO2 thick film

The present investigation deals with the fabrication of CO2 gas sensor based on La2O3/SnO2 metal-oxide material. In this paper, the sensitive material was prepared by La2O3/SnO2 nanopowder and the addition of 1 wt. % and 3 wt. % platinum (Pt) using high-speed ball milling method. The sensitive film prepared by sensitive powder was printed on alumina (Al2O3) substrate by screen printing method. This film was characterized by X-Ray powder diffraction spectroscopy, and Field-emission scanning electron microscopy. As a result, the prepared 3 wt. % Pt/La2O3/SnO2 thick film sensitive paste exhibits a high sensitivity to increasing the CO2 gas concentration at 225 °C in air atmosphere

Analytical investigations of gas-sensor using methane decomposition system

This paper reports on a set of experiments designed to develop a workable gas sensor prototype using an electronic system with methane. The current is found to be sensitive to the presence of methane gas, which is a conduit for a variety of gas sensors. The sensitivity is shown to depend on pointed or broad electrode configurations. Scanning electron microscopy images show the area of conductance that determines the quality of the electrodes in three configurations. Data processing is performed with a support vector regression algorithm in conjunction with statistical analysis for error and quality control. The reported results can be adapted to a broad range of industrial applications for enhanced productivity, safety, innovation, data processing, and overall total quality management