Highly Sensitive p-SmFeO3/p-YFeO3 Planar-Electrode Sensor for Detection of Volatile Organic Compounds

Abstract

Nanocomposites of SmFeO3/YFeO3 (1:0, 0.8:0.2, 0.6:0.4, 0.4:0.6, 0.2:0.8, and 0:1) with different molar proportions were prepared by the sol–gel method. The material’s properties were characterized by various test methods, such as scanning-electron microscopy (SEM) and X-ray photoelectron-diffraction spectrometry (XPS). The gas-sensing characteristics of the sensor were tested in darkness and under illumination using monochromatic light with various selected wavelengths. The test results show that the SmFeO3/YFeO3 sensor with the molar ratio of 0.4:0.6 had the highest gas response to volatile organic compound (VOC) gases and that the optimum operating temperature was lower (120 °C). The light illumination improved the sensor’s sensitivity to gas. Under the 370-nanometer light illumination, the sensor’s responses to 30 ppm of ethanol, acetone, and methanol gases were 163.59, 134.02, and 111.637, respectively, which were 1.35, 1.28, and 1.59 times higher, respectively, than those without light. The high gas sensitivity of the sensor was mainly due to the adsorption of oxygen on the material’s surface and the formation of a p–p heterojunction. The SmFeO3/YFeO3 sensor, which can respond to different VOC gases, can be used to detect the safety of unknown environments and provide a timely warning of the presence of dangerous gases in working environments.

Gas-Sensing Performance of Gadolinium Ferrates with Rod and Butterfly Morphologies

“…The percentages of lattice oxygen, defect oxygen, and adsorbed oxygen are calculated to be 47.9%, 37.5%, and 14.6%, respectively. The good gas-sensing performance of GdFeO 3 rods may be due to the high content of defect oxygen and adsorbed oxygen [33]. Herein, the adsorbed oxygen species on the surface of GdFeO 3 rods reacts with the target gas of reducing molecules, increasing the concentration of hole carriers, thus contributing to the response value of the sensor.…”

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

“…The Computational intelligence models utilize in load forecasting expert systems, artificial intelligence [41,42] neural networks [43][44][45][46][47], fuzzy logic and wavelets, ICEEMDAN-GS-WT-LSTM-ISSA [15][16][17]. Usually most of computational intelligence models used to calibrate parameters in time series.…”