Identification and quantification of gases and their mixtures using GaN sensor array and artificial neural network
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 ph...
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Published in | Measurement science & technology Vol. 32; no. 5; p. 55111 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
01.05.2021
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Online Access | Get full text |
ISSN | 0957-0233 1361-6501 |
DOI | 10.1088/1361-6501/abd5f0 |
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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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AbstractList | 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. |
Author | Motayed, Abhishek Khan, Md Ashfaque Hossain Rao, Mulpuri V |
Author_xml | – sequence: 1 givenname: Md Ashfaque Hossain orcidid: 0000-0002-0070-8872 surname: Khan fullname: Khan, Md Ashfaque Hossain – sequence: 2 givenname: Abhishek surname: Motayed fullname: Motayed, Abhishek – sequence: 3 givenname: Mulpuri V surname: Rao fullname: Rao, Mulpuri V |
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CitedBy_id | crossref_primary_10_1016_j_sna_2024_115768 crossref_primary_10_3390_s20143889 crossref_primary_10_1109_JSEN_2024_3349862 crossref_primary_10_1109_TIM_2021_3120150 crossref_primary_10_1109_JSEN_2024_3407741 crossref_primary_10_1149_2754_2726_ad0cd6 crossref_primary_10_7831_ras_12_0_128 crossref_primary_10_1109_JSEN_2022_3211289 crossref_primary_10_3390_s21020624 crossref_primary_10_1039_D1CP02394B crossref_primary_10_1016_j_jhazmat_2023_132153 crossref_primary_10_3390_s21144826 crossref_primary_10_1021_acsami_4c14793 crossref_primary_10_1016_j_snb_2023_133767 crossref_primary_10_1371_journal_pone_0310101 |
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