Toward a Stochastic Drift Simulation Model for Graphene-Based Gas Sensors

Schober, Sebastian A.; Carbonelli, Cecilia; Roth, Alexandra; Zoepfl, Alexander; Travan, Caterina; Wille, Robert

doi:10.1109/jsen.2021.3114103

Cited by 8 publications

(7 citation statements)

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%

Temperature Stability Investigations of Neural Network Models for Graphene-Based Gas Sensor Devices

Bahri

Schober

Carbonelli

et al. 2021

The 8th International Electronic Conference on Sensors and Applications

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Chemiresistive gas sensors are a crucial tool for monitoring gases on a large scale. For the estimation of gas concentrations based on the signals provided by such sensors, pattern recognition tools, such as neural networks, are widely used after training them on data measured by sample sensors and reference devices. However, in the production process of low-cost sensor technologies, small variations in their physical properties can occur, which can alter the measuring conditions of the devices and make them less comparable to the sample sensors, leading to less adapted algorithms. In this work, we study the influence of such variations with a focus on changes in the operating and heating temperature of graphene-based gas sensors in particular. To this end, we trained machine learning models on synthetic data provided by a sensor simulation model. By varying the operation temperatures between −15% and +15% from the original values, we could observe a steady decline in algorithm performance, if the temperature deviation exceeds 10%. Furthermore, we were able to substantiate the effectiveness of training the neural networks with several temperature parameters by conducting a second, comparative experiment. A well-balanced training set has shown to improve the prediction accuracy metrics significantly in the scope of our measurement setup. Overall, our results provide insights into the influence of different operating temperatures on the algorithm performance and how the choice of training data can increase the robustness of the prediction algorithms.

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Section: Sensor Modelingmentioning

confidence: 99%

Temperature Stability Investigations of Neural Network Models for Graphene-Based Gas Sensor Devices

Bahri

Schober

Carbonelli

et al. 2021

The 8th International Electronic Conference on Sensors and Applications

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“…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%

Using Machine Learning to Overcome Interfering Oxygen Effects in a Graphene Volatile Organic Compound Sensor

Capman,

Chaganti,

Simms

et al. 2024

ACS Appl. Mater. Interfaces

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Discriminating between volatile organic compounds (VOCs) for applications including disease diagnosis and environmental monitoring, is often complicated by the presence of interfering compounds such as oxygen. Graphene sensors are effective at detecting VOCs; however, they are also known to be highly sensitive to oxygen. Therefore, the combined effects of each of these gases on graphene sensors must be understood. In this work, we use graphene variable capacitor (varactor) sensors to examine the cross-selectivity of oxygen at 3 concentrations and 3 VOCs (ethanol, methanol, and methyl ethyl ketone) at 5 concentrations each. The sensor responses exhibit distinct shapes dependent on the relative concentrations in mixtures of oxygen and VOCs. Because the entire response shape is therefore informative for distinguishing between each gas mixture, a classification algorithm that utilizes entire sequences of data is needed. Accordingly, a long short-term memory (LSTM) network is used to classify the mixtures and VOC concentrations. The model achieves 100% accurate classification of the VOC type, even in the presence of varying levels of oxygen. When the VOC type and VOC concentration are classified, we show that the sensors can provide VOC concentration resolution within approximately 200 ppm. Throughout this work, we also demonstrate that an effective gas mixture classification can be achieved, even while the sensors exhibit varied drift patterns typical of graphene sensors. This is made possible due to the data analysis and machine learning methods employed.

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“…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%

Neural Network Robustness Analysis Using Sensor Simulations for a Graphene-Based Semiconductor Gas Sensor

et al. 2022

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Despite their advantages regarding production costs and flexibility, chemiresistive gas sensors often show drawbacks in reproducibility, signal drift and ageing. As pattern recognition algorithms, such as neural networks, are operating on top of raw sensor signals, assessing the impact of these technological drawbacks on the prediction performance is essential for ensuring a suitable measuring accuracy. In this work, we propose a characterization scheme to analyze the robustness of different machine learning models for a chemiresistive gas sensor based on a sensor simulation model. Our investigations are structured into four separate studies: in three studies, the impact of different sensor instabilities on the concentration prediction performance of the algorithms is investigated, including sensor-to-sensor variations, sensor drift and sensor ageing. In a further study, the explainability of the machine learning models is analyzed by applying a state-of-the-art feature ranking method called SHAP. Our results show the feasibility of model-based algorithm testing and substantiate the need for the thorough characterization of chemiresistive sensor algorithms before sensor deployment in order to ensure robust measurement performance.

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Toward a Stochastic Drift Simulation Model for Graphene-Based Gas Sensors

Cited by 8 publications

References 30 publications

Temperature Stability Investigations of Neural Network Models for Graphene-Based Gas Sensor Devices

Temperature Stability Investigations of Neural Network Models for Graphene-Based Gas Sensor Devices

Using Machine Learning to Overcome Interfering Oxygen Effects in a Graphene Volatile Organic Compound Sensor

Neural Network Robustness Analysis Using Sensor Simulations for a Graphene-Based Semiconductor Gas Sensor

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