Towards Drift Modeling of Graphene-Based Gas Sensors Using Stochastic Simulation Techniques

Due to environmental conditions as well as internal processes, the lack of long-term stability of electrochemical gas sensors poses a severe problem with respect to their applications, e.g. in tracking air quality on a large scale. Thus far, the development of suitable algorithms to face these problems relies on long-term datasets obtained from sufficiently good reference devices. Since such measurements on actual sensor systems are not always available, especially in the development phase of them, simulated approaches would be a great benefit for algorithm development and the further analysis of the sensors. Those simulators, however, require proper models to capture the general principles of the functionalized materials in such sensor arrays. In this work, we propose a stochastic model that can be used for this purpose, i.e. that allows for simulating the behavior of graphene-based electrochemical gas sensors in particular. The proposed approach allows to properly map different material-related microscopic effects on the sensor surface to a signal output. Evaluations show that the proposed model is able to capture the drift dynamics of such sensors in particular when comparing the results to real measurement data.