Acta Scientific Computer Sciences

Research Article Volume 5 Issue 3

Quantitative Detection of Ammonia Gas Via V2O5 Metal-Oxide Based Sensor Combining with Regression Model

Vinutha Srikanth1, MG Srinivasa2* and Santosh Desai3

1Associate Professor, Department of Electronics and Communication Engineering, RVITM, Bangalore, Karnataka, India
2Associate Professor, Department of Electronics and Communication Engineering, MIT, Thandavapura, Mysore, Karnataka, India
3Professor, Department of Electronics and Instrumentation Engineering, BMSCE, Bangalore, India

*Corresponding Author: MG Srinivasa, Associate Professor, Department of Electronics and Communication Engineering, MIT, Thandavapura, Mysore, Karnataka, India.

Received: December 07, 2022; Published: February 12, 2023


In this proposed work, nanoparticles of vanadium pentoxide (V2O5) and vanadium pentoxide with rGO have been fruitfully synthesized through a suitable hydrothermal method at a constant temperature (180○C).As synthesized V2O5 nano particles were characterized, and fabricated as a gas sensor by coating the synthesized material on ITO glass substrate. The NH3gas sensing properties of the fabricated sensors were carried out. Structural investigation of the same showed that V2O5 nanoparticles formed have orthorhombic crystal phase and the size of the nanorods was found to be 90 nm. The gas sensor studies revealed a higher sensing response at 2500C and demonstrated excellent gas sensing properties toward 1-3.2 ppm NH3 gas. It also exhibited a good selectivity, sensitivity and stability, with rapid response/recovery time, and a linear relationship between the response and the target gas concentration. In particular, the sensing performance of the V2O5 gas sensor was trained through MATLAB for regression analysis, which suggests the permissible R2 value of 0.88 of a prompt gas sensing response.

Keywords: Gas Sensors; Nano Materials; Gas Chamber


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Citation: MG Srinivasa., et al. “Quantitative Detection of Ammonia Gas Via V2O5 Metal-Oxide Based Sensor Combining with Regression Model". Acta Scientific Computer Sciences 5.3 (2023): 54-60.


Copyright: © 2023 MG Srinivasa., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate35%
Acceptance to publication20-30 days

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