2022
DOI: 10.1109/jsen.2021.3114103
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Toward a Stochastic Drift Simulation Model for Graphene-Based Gas Sensors

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Cited by 8 publications
(7 citation statements)
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References 30 publications
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“…A system-level gas sensor model by Schober et al [14,15] was used in order to generate signals with different variations in their temperature profile. Here, the stochastic simulation of the adsorption and desorption of the gases of interest, for instance Ozone, is modeled by discrete Markov processes on a sample grid representing the sensor surface.…”
Section: Sensor Modelingmentioning
confidence: 99%
“…A system-level gas sensor model by Schober et al [14,15] was used in order to generate signals with different variations in their temperature profile. Here, the stochastic simulation of the adsorption and desorption of the gases of interest, for instance Ozone, is modeled by discrete Markov processes on a sample grid representing the sensor surface.…”
Section: Sensor Modelingmentioning
confidence: 99%
“…Molecules filling binding sites in the sensor materials often desorb at a slower rate than they adsorb, leading to a steady change in the baseline throughout testing as adsorption sites fill up. 28 Drift may also be caused by the slow desorption of molecules bound to adsorption sites or trapped between layers of the sensor when the sensor is exposed to a different gas. 25 Drift is a concern for the long-term stability of devices, as well as error in sensing, and is a nonideality in this type of sensor that is unlikely to be completely eliminated in the near future.…”
Section: ■ Introductionmentioning
confidence: 99%
“…Graphene sensors are well-known for issues with baseline drift, where signals rise or fall slowly throughout longer periods of time, and this drift can be caused by a number of factors. Molecules filling binding sites in the sensor materials often desorb at a slower rate than they adsorb, leading to a steady change in the baseline throughout testing as adsorption sites fill up . Drift may also be caused by the slow desorption of molecules bound to adsorption sites or trapped between layers of the sensor when the sensor is exposed to a different gas .…”
Section: Introductionmentioning
confidence: 99%
“…The results obtained by the described characterization procedures can help to develop models for the studied sensor. This is usually achieved by fitting experimental measurements to the model equations, taking the material-specific effects derived from the characterization into consideration [11][12][13][14].…”
Section: Introductionmentioning
confidence: 99%
“…Each of the studies contains an additional overview of the related work in the specific area in the Materials and Methods section. The studies share the same data configuration in terms of concentration profiles, which is provided by a simulation model of a graphene-based chemiresistive gas sensor that we developed in previous research [14,25]. Furthermore, the techniques that we present throughout this paper are designed to be model-agnostic, which means that they are also applicable for other pattern recognition algorithms, which are not part of our study setup.…”
mentioning
confidence: 99%