Optimized temperature modulation of micro-hotplate gas sensors through pseudorandom binary sequences

Abstract

In recent years, modulating the working temperature of metal-oxide gas sensors has been one of the most widely used methods to enhance sensor selectivity. When the working temperature of a gas sensor is modulated, the kinetics of the gas-sensor interaction are altered, and this leads to characteristic response patterns. Many works have shown that it is possible to identify and determine the concentration of gases in simple mixtures, even using a single temperature-modulated metal-oxide gas sensor. However, the selection of the frequencies used to modulate temperature remains an empirical process. In this paper, we introduce a method, borrowed from the field-of-system identification, to systematically determine the optimal set of modulating frequencies to solve a given gas-analysis application. The method consists of using maximum-length pseudorandom binary sequences to modulate the working temperature of metal-oxide gas sensors. Since these signals have a flat power spectrum (i.e., like white noise) in a wide frequency range, an estimate of the impulse response of each gas-sensor pair can be computed by the cross correlation of the excitatory and response sequences. Studying the impulse response estimates, the set of modulating frequencies that are useful to discriminate between different gases and to estimate gas concentration, is obtained in a systematic way. The method is demonstrated with tungsten oxide micro-hotplate gas sensors applied to detect ammonia, nitrogen dioxide, and their binary mixtures at different concentrations. It is shown that it is possible to find temperature-modulating frequencies to obtain high gas identification and quantification rates (95.55% and 100%, respectively).