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Deep Neural Network Based Modelling of Chemisorption Process on Surface of Oxide Based Gas Sensors

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Title Deep Neural Network Based Modelling of Chemisorption Process on Surface of Oxide Based Gas Sensors
 
Creator Gupta, Rahul
Kumar, Pradeep
Kumar, Dinesh
 
Subject Chemisorption
Deep neural network
Gas sensor
Numerical modelling
 
Description 1143-1151
The sensor response of the metal oxide based gas sensor has been simulated using Deep Neural Network (DNN) model.
The neural network designed for the modelling of the sensor has single input layer, three hidden layers and single output
layer. The linear regression algorithm has been used to compute the electrical conductance of the sensor at given
temperature and pressure. The data generated through modified Wolkenstein method has been used for training, validation
and testing of the developed network. The data for materials Tin (IV) oxide (SnO2), Tin (II) oxide (SnO) and Copper (I)
oxide (Cu2O) with different Eg values has been utilized. The other input parameters like Temperature, ND, NC, NV,
EF−ESSand ECS−EF are varied for the specific range to collect a variety of data for calculation of electrical conductance of
the sensor. The total data used for training, validation and testing was 1,90,512 data points. The plots for training, validation
and testing phase have been plotted. The sensor response computed through the proposed model is validated with the results
of already published mathematical model. The sensor response shows steep change when the gas concentration of the target
gas reaches above 10−8 atm. The proposed model can be retrained or transfer learning can be applied for using the same
model for other types of materials for gas sensing applications.
 
Date 2023-11-06T09:19:47Z
2023-11-06T09:19:47Z
2023-11
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/62862
https://doi.org/10.56042/jsir.v82i11.1978
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(11) [November 2023]