A Smart Gas Sensor Insensitive to Humidity and Temperature Variations

Abstract

The accuracy of the quantitative sensing of volatile organic compounds by chemoresistive gas sensors suffers from the fluctuations in the background atmospheric conditions. This is caused by the drift-like terms introduced in the responses by these instabilities, which should be identified and compensated. Here, a mathematical model is presented for a specific chemoresistive gas sensor, which facilitates these identification and compensation processes. The resistive gas sensor was considered as a multi-input-single-output system. Along with the steady state value of the measured sensor resistance, the ambient humidity and temperature are the inputs to the system, while the concentration level of the target gas is the output. The parameters of the model were calculated based on the experimental database. The model was simulated by the utilization of an artificial neural network. This was connected to the sensor and could deliver the correct contamination level upon receiving the measured gas response, ambient humidity and temperature.