Towards Drift Modeling of Graphene-Based Gas Sensors Using Stochastic Simulation Techniques
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
IEEE Sensors
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
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 materialrelated 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
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