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Correction Model for Metal Oxide Sensor Drift Caused by Ambient Temperature and Humidity.

Abdulnasser Nabil Abdullah1,2, Kamarulzaman Kamarudin1,2, Latifah Munirah Kamarudin1,2

  • 1Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia.

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Summary

Metal oxide (MOX) gas sensors are sensitive to temperature and humidity. This study developed regression models to correct MOX sensor drift, significantly improving their stability for reliable gas monitoring.

Keywords:
3D linear regressionMOX sensorscross-sensitivitydrift correctionhumiditytemperature

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Area of Science:

  • Materials Science
  • Sensor Technology
  • Environmental Monitoring

Background:

  • Metal oxide (MOX) gas sensors offer high sensitivity, broad detection range, fast response, and cost-effectiveness, making them suitable for smart city applications, gas monitoring, and safety systems.
  • However, the performance of MOX gas sensors can be significantly impacted by environmental factors such as ambient temperature and humidity, leading to cross-sensitivity and unreliable readings.
  • Understanding and mitigating these environmental effects is crucial for enhancing the accuracy and dependability of MOX gas sensor applications.

Purpose of the Study:

  • To investigate the cross-sensitivity of different MOX gas sensors (MiCS-5524, GM-402B, GM-502B, and MiCS-6814) to ambient temperature and humidity variations.
  • To develop and validate regression models capable of correcting the sensor response drift caused by these environmental factors.
  • To demonstrate the effectiveness of the proposed models in improving the stability and reliability of MOX gas sensor outputs.

Main Methods:

  • A gas sensor array was constructed, incorporating temperature and humidity sensors alongside four distinct MOX gas sensors.
  • The sensor array was exposed to varying concentrations of gases under controlled temperature (16 °C to 30 °C) and humidity (75% to 45%) conditions, simulating a typical indoor environment.
  • Regression models were developed for each MOX sensor to mathematically correlate and compensate for the observed drifts in sensor response due to temperature and humidity fluctuations.

Main Results:

  • Gas sensor responses were found to be significantly influenced by ambient temperature and humidity. Increased temperature and humidity generally led to decreased sensor response, with the MiCS-6814 sensor exhibiting an inverse relationship.
  • The developed regression models successfully corrected the sensor response drift, resulting in substantially reduced standard deviations compared to the raw sensor outputs.
  • Validated models demonstrated a significant minimization of drift, with corrected standard deviations being considerably lower than the raw sensor response values (e.g., 1.66 kΩ vs. 18.22 kΩ for one sensor).

Conclusions:

  • Ambient temperature and humidity significantly affect the performance of MOX gas sensors, necessitating compensation strategies for accurate measurements.
  • The proposed regression models effectively mitigate the drift in MOX gas sensor responses caused by temperature and humidity variations, leading to more stable and reliable outputs.
  • The developed correction models are applicable to the specific MOX sensors tested and show potential for broader use with other MOX gas sensors in various applications, including training processes.