Dataset for characterization of gas sensor under varying ambient temperature and humidity conditions
Abdulnasser Nabil Abdullah, Kamarulzaman Kamarudin, Sukhairi Sudin, Syed Muhammad Mamduh, Md Ashequl Islam, Zaffry Hadi Mohd Juffry
Universiti Malaysia Perlis Technical University of Malaysia Malacca
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摘要与影响
Chemi resistive gas sensors based on metal oxide semiconductor (MOX) materials provide a practical and cost-effective solution for detecting hazardous gases in industrial and environmental monitoring applications. However, their sensing performance is strongly influenced by variations in ambient temperature and relative humidity, which can affect sensitivity, selectivity, and long-term stability. This study presents a comprehensive experimental dataset and characterization of MOX gas sensors operating under controlled environmental conditions. Sensors targeting hydrogen sulfide (H 2 S), carbon monoxide (CO), and methane (CH 4 ) were selected due to their relevance in hazardous atmosphere monitoring, particularly in confined-space environments. A custom-designed sensing platform integrating a signal-conditioning circuit, environmental control chamber, and data acquisition unit was developed to enable precise and repeatable measurements across a temperature range of 16 °C–30 °C and relative humidity levels of 45%–75%. The experimental results demonstrate that sensor resistance (Rs) varies systematically with changes in ambient temperature and humidity, confirming the strong environmental cross-sensitivity of MOX sensing materials. In addition to sensor characterization, this work provides a structured and openly accessible time-series dataset containing synchronized measurements of sensor resistance, temperature, and humidity. The dataset offers a valuable resource for the development and benchmarking of environmental compensation algorithms, sensor drift correction techniques, and machine-learning-based gas sensing models.
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工程Gas Sensing Nanomaterials and Sensors
Advanced Chemical Sensor Technologies · Air Quality Monitoring and Forecasting
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