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Identification and quantification of gases and their mixtures using GaN sensor array and artificial neural network
Abstract Accurate identification and quantification of gas mixtures are almost unattainable utilizing only a metal-oxide/GaN sensor because of its cross-sensitivity to many gases. Here, an array of sensors has been formed consisting of Ag and Pt incorporated ZnO, In 2 O 3 and TiO 2 coated two termin...
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Published in: | Measurement science & technology 2021-05, Vol.32 (5), p.55111 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Abstract
Accurate identification and quantification of gas mixtures are almost unattainable utilizing only a metal-oxide/GaN sensor because of its cross-sensitivity to many gases. Here, an array of sensors has been formed consisting of Ag and Pt incorporated ZnO, In
2
O
3
and TiO
2
coated two terminal GaN photoconductors. The common environmental toxic gases, such as SO
2
, NO
2
, H
2
, ethanol and their mixtures have been chosen as the gas analytes. All the gas responses have been obtained at 20 °C under UV illumination. Temporal responses have been post-processed to develop the training and test dataset. Then, four different artificial neural network models have been analyzed and optimized for gas classification study, which is done for the first time on GaN sensors. Statistical and computational complexity results indicate that back-propagation neural network (NN) stands out as the optimal classifier among the considered algorithms. Then, ppm concentrations of the identified gases have been estimated using the optimal model. Furthermore, implementation of the developed sensor array in combination with NN algorithm for real-time gas monitoring applications has been discussed. |
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ISSN: | 0957-0233 1361-6501 |
DOI: | 10.1088/1361-6501/abd5f0 |