Study of improving identification accuracy of carbon nanotube film cathode gas sensor

Abstract

Ionization gas sensors need to combine with chromatograph and mass spectrograph to separate gas mixture; this results in huge, bulky architecture, high power consumption and risky high-voltage operation. Here we proposed using two-sensor array composed of two carbon-nanotube film cathode gas sensors with different gaps and using multi-information fusion technology to directly and precisely identify the gas mixture at atmospheric pressure without separating the mixture. Two-sensor array with different gaps were used, and experiments were conducted to obtain the original patterns (OPs) of the two sensors, viz. the relationship between the breakdown voltages and the gas concentrations of the two-gas mixture. The interpolation technology was used to improve OPs, and the neural network technology was used to precisely identify the two components without separating the mixture. This novel method greatly improved the performance of the gas sensor, and can be used to identify n-gas mixture ( n > 2) with n-sensor array.