In this paper, we propose a new model of adsorption–desorption (AD) noise in chemoresistive gas sensors by taking into account the polycrystalline structure of the sensing layer and the effect of the adsorbed molecule’s density fluctuation on the grain boundary barrier height. Using Wolkenstein’s isotherm, in the case of dissociative and non-dissociative chemisorption, combined with the electroneutrality, we derive an exact expression for power density spectrum (PDS) of the AD noise generated around one grain. We show that the AD noise generated in the overall sensing layer is a combination of multi-Lorentzian components. The parameters of each Lorentzian depend on the nature of the detected gas, the grain size, and the gas concentration. Moreover, we show that, according to the sensing layer microstructure (distribution of grain sizes in the sensing layer), this combination can lead to a [Formula: see text] spectrum, and in this case the noise level of the [Formula: see text] spectrum depends on the nature of the detected gas. The noise modeling presented in this paper confirms that noise spectroscopy is a useful tool for improving the gas sensor selectivity.
Noise spectroscopy has been proposed as a means of extracting a more selective response from metallic oxide gas sensors. In this paper, we complete our previous models of adsorption–desorption (A–D) noise by taking into account the effect of the fluctuation of the adsorbed molecule's density, not only on the density of free carriers but also on their mobility. Using Wolkenstein's isotherm, combined with the electroneutrality and the fluctuation of both free electron density and mobility, we derive an exact expression for the A–D noise in the case of dissociative and non-dissociative chemisorption. The model shows that the power density spectrum of the fluctuation of the sensor's conductance has a cut-off frequency and a low frequency magnitude which are specifics of the adsorbed gas. The cut-off frequency is four orders of magnitude lower than the one we obtained without considering mobility fluctuations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.