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Development of ZnO-doped SnO2 sensor for Detection of SO2 and Performance Validation through Artificial Neural Network

Jitendra K. Srivastava1 , Deepak Kumar Verma2

Section:Research Paper, Product Type: Journal Paper
Volume-9 , Issue-8 , Page no. 72-75, Aug-2021

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v9i8.7275

Online published on Aug 31, 2021

Copyright © Jitendra K. Srivastava, Deepak Kumar Verma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Jitendra K. Srivastava, Deepak Kumar Verma, “Development of ZnO-doped SnO2 sensor for Detection of SO2 and Performance Validation through Artificial Neural Network,” International Journal of Computer Sciences and Engineering, Vol.9, Issue.8, pp.72-75, 2021.

MLA Style Citation: Jitendra K. Srivastava, Deepak Kumar Verma "Development of ZnO-doped SnO2 sensor for Detection of SO2 and Performance Validation through Artificial Neural Network." International Journal of Computer Sciences and Engineering 9.8 (2021): 72-75.

APA Style Citation: Jitendra K. Srivastava, Deepak Kumar Verma, (2021). Development of ZnO-doped SnO2 sensor for Detection of SO2 and Performance Validation through Artificial Neural Network. International Journal of Computer Sciences and Engineering, 9(8), 72-75.

BibTex Style Citation:
@article{Srivastava_2021,
author = {Jitendra K. Srivastava, Deepak Kumar Verma},
title = {Development of ZnO-doped SnO2 sensor for Detection of SO2 and Performance Validation through Artificial Neural Network},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {8 2021},
volume = {9},
Issue = {8},
month = {8},
year = {2021},
issn = {2347-2693},
pages = {72-75},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5382},
doi = {https://doi.org/10.26438/ijcse/v9i8.7275}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v9i8.7275}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5382
TI - Development of ZnO-doped SnO2 sensor for Detection of SO2 and Performance Validation through Artificial Neural Network
T2 - International Journal of Computer Sciences and Engineering
AU - Jitendra K. Srivastava, Deepak Kumar Verma
PY - 2021
DA - 2021/08/31
PB - IJCSE, Indore, INDIA
SP - 72-75
IS - 8
VL - 9
SN - 2347-2693
ER -

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Abstract

In the present work we have fabricated a thick film SnO2 sensor on a 1?x1? alumina substrate. It consists of a gas sensitive layer (SnO2) doped with ZnO, a pair of electrodes underneath the gas sensing layer serving as a contact pad for sensor and a heater element on the backside of the substrate was printed. The sensitivity of sensor has been studied and measured at different temperatures (1500C - 3500C) upon exposure to SO2. An approach is made to measure the sensitivity of ZnO-doped SnO2 by using Artificial Neural Network tool. Finally we have shown the results that shows the potential of Artificial Neural Network as a design tool in the area of thick film gas sensor fabrication and development.

Key-Words / Index Term

Artificial Neural Network, Thick film sensor, sensitivity, ZnO

References

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