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

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

doi:10.1109/sensors47125.2020.9278754

Cited by 10 publications

(11 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As previously, the benzene response was not included for the same reasons (see Supplementary Materials; Figure S4). Here, the sensors experience a very significant baseline drift which seems to be dependent on the RGO material, as already observed for carbon material-based sensors [67,68]. The origin of this drift is difficult to explain, but both intrinsic and extrinsic factors can affect it [68].…”

Section: Resistive Sensors Responsesupporting

confidence: 52%

Macrocycle-Functionalized RGO for Gas Sensors for BTX Detection Using a Double Transduction Mode

et al. 2021

View full text Add to dashboard Cite

To fabricate mass and resistive sensors based on reduced graphene oxide (RGO), we investigated the functionalization of RGO by tetra tert-butyl phthalocyanine (PcH2tBu), which possesses a macroring and tert-butyl peripheral groups. Herein, we present the gas sensor responses of the functionalized RGO toward benzene, toluene, and xylene (BTX) vapors. The RGO was obtained by the reduction of graphene oxide (GO) using citrate as a reducing agent, while the functionalization was achieved non-covalently by simply using ultrasonic and heating treatment. The sensor devices based on both QCM (quartz crystal microbalance) and resistive transducers were used simultaneously to understand the reactivity. Both the GO and the RGO showed less sensitivity to BTX vapors, while the RGO/PcH2tBu presented enhanced sensor responses. These results show that the p-network plays a very important role in targeting BTX vapors. The resistive response analysis allowed us to state that the RGO is a p-type semiconductor and that the interaction is governed by charge transfer, while the QCM response profiles allowed use to determine the differences between the BTX vapors. Among BTX, benzene shows the weakest sensitivity and a reactivity in the higher concentration range (>600 ppm). The toluene and xylene showed linear responses in the range of 100–600 ppm.

show abstract

Section: Resistive Sensors Responsesupporting

confidence: 52%

Macrocycle-Functionalized RGO for Gas Sensors for BTX Detection Using a Double Transduction Mode

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The sensor response model then translates the input concentrations at each sensor location to the expected sensor signal measurements. This is done by using a stochastic sensor model which has been developed in previous research [4]. In particular, the sensor response model simulates the processes which are directly occurring on the sensor surface by modeling its adsorption and desorption processes on a microscopic level.…”

Section: Methodsmentioning

confidence: 99%

“…Here, sensitivity loss The sensor response model then translates the input concentrations at each sensor location to the expected sensor signal measurements. This is done by using a stochastic sensor model which has been developed in previous research [4].…”

Section: Methodsmentioning

confidence: 99%

“…For the specific case of sensor drift, such frameworks have already been investigated to evaluate calibration algorithms, e.g., in [3]. Therefore, in order to study other fault types, we want to present a framework based upon a stochastic sensor simulator [4], which can provide sensor network simulations specifically feasible for a number of different sensor defects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simulating Defects in Environmental Sensor Networks Using Stochastic Sensor Models

Schober

Carbonelli

Wille

2021

The 8th International Symposium on Sensor Science

Self Cite

View full text Add to dashboard Cite

Chemiresistive gas sensors are an important tool for monitoring air quality in cities and large areas due to their low cost and low power and, hence, the ability to densely distribute them. Unfortunately, such sensor systems are prone to defects and faults over time such as sensitivity loss of the sensing material, less effective heating of the surface due to battery loss, or random output errors in the sensor electronics, which can lead to signal jumps or sensor stopping. Although these defects usually can be compensated, either algorithmically or physically, this requires an accurate screening of the entire sensor system for such defects. In order to properly develop, test, and benchmark corresponding screening algorithms, however, methods for simulating gas sensor networks and their defects are essential. In this work, we propose such a simulation method based on a stochastic sensor model for chemiresistive sensor systems. The proposed method rests on the idea of simulating the defect-causing processes directly on the sensor surface as a stochastic process and is capable of simulating various defects which can occur in low-cost sensor technologies. The work aims to show the scope and principles of the proposed simulator as well as to demonstrate its applicability using exemplary use cases.

show abstract

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 10 publications

References 12 publications

Macrocycle-Functionalized RGO for Gas Sensors for BTX Detection Using a Double Transduction Mode

Macrocycle-Functionalized RGO for Gas Sensors for BTX Detection Using a Double Transduction Mode

Simulating Defects in Environmental Sensor Networks Using Stochastic Sensor Models

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

Contact Info

Product

Resources

About