Fabrication and characteristics of SnO2 gas sensor array for volatile organic compounds recognition

Abstract Ten different gas sensors were integrated as an array on a substrate to identify various kinds and quantities of volatile organic compounds (VOCs), such as benzene, toluene, ethyl alcohol, methyl alcohol, and acetone. The sensor array consists of gas-sensing materials with SnO2 as the base material and a platinum heater and is fabricated using silk printing methods on an alumina substrate. The sensors show a high and broad sensitivity and reproducibility to low concentrations based on the use of nano-sized sensing materials with different additives. Utilizing the sensing signals of the array, an artificial neural network with an error-back-propagation learning algorithm is then implemented as a recognition system for classifying and quantifying the VOCs. Simulation and experimental results demonstrated that the proposed gas sensor array with the neural network was effective in recognizing various kinds and quantities of VOCs.