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Selective Identification of Hazardous Gases Using Flexible, Room-Temperature Operable Sensor Array Based on Reduced

Dong-Bin Moon¹, Atanu Bag^1,2, Hamna Haq Chouhdry³

¹School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea.

|October 29, 2024

Summary

This study presents a flexible gas sensor array using metal oxide nanoparticles for selective detection of hazardous gases like NO2, NO, and SO2. Machine learning enhances accuracy for environmental monitoring and personal safety.

Keywords:

flexible gas sensor array machine learning metal oxide nanoparticles reduced graphene oxide room-temperature operable

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Area of Science:

Materials Science
Chemical Sensing
Nanotechnology

Background:

Selective detection of hazardous gases is crucial for human safety.
Chemiresistive gas sensors often lack selectivity for similar gases.
Flexible, room-temperature sensors are needed for wearable applications.

Purpose of the Study:

To develop a flexible, room-temperature operable gas sensor array for selective hazardous gas detection.
To utilize machine learning for enhanced gas identification and concentration determination.
To assess sensor performance under mechanical strain and humidity.

Main Methods:

Fabrication of a flexible sensor array on a polyimide substrate using reduced graphene oxide (rGO) decorated with ZnO, TiO2, and SnO2 nanoparticles.
Design of four distinct sensing layers within the array.
Application of machine learning algorithms with data fusion for analyzing sensor response patterns.

Main Results:

The sensor array demonstrated stable performance under mechanical deformation and up to 60% humidity.
Unique response patterns were observed for different gases (NO2, NO, SO2) due to varying metal oxide nanoparticles.
Machine learning achieved high training (98.20%) and testing (97.70%) accuracies for gas identification and concentration.
Distinctive recovery patterns were leveraged for selective gas differentiation.

Conclusions:

The developed flexible sensor array combined with machine learning offers a promising solution for selective hazardous gas detection.
This technology has significant potential for environmental monitoring and personal safety applications.
The sensor's robustness to mechanical stress and humidity enhances its practical applicability.