Towards a Stochastic Drift Simulation Model for Graphene-Based Gas Sensors
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Monitoring air quality in cities as well as indoors has become an important topic in recent years. Being easily and densely deployable and rather low-cost, chemiresistive gas sensors indicate a feasible technology for this problem, especially if they are assembled as e-noses tracking multiple gases in one device. Nonetheless, the long-term stability of such sensors poses a severe problem for their measurement accuracy, which substantiates the need for drift compensation procedures as well as robust algorithms in the gas prediction process. In order to test such drift compensation methods and to generate synthetic data for algorithm training, a simulation model would be highly useful. In this paper, we present our investigations towards a simulation framework aiming at generating typical sensor responses of a graphene-based gas sensor with an emphasis on its drift behavior. Different drift models are studied, implemented and compared to real measurement data from a lab setup. The evaluation shows promising results when compared to experiments from a real chemiresistive sensor to which the model was parametrized. By using a second setup with fundamentally different heating characteristics we observed that recalibration is a necessary step to generalize to different sensing modes and to ensure the overall quality of the simulation data. Overall, these investigations provide a proper basis towards tackling the challenges in drift simulation.