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Applications and Material Design of Machine Learning-Enabled Metal Oxide Semiconductor Gas Sensors
Zhi-Lei Li1, Xiao-Hong Zheng1, Su-Yue Ren1
1Faculty of Materials Technology, Shanghai Institute of Technology, Shanghai 201418, People's Republic of China.
Langmuir : the ACS Journal of Surfaces and Colloids
|July 12, 2026
Summary
Machine learning enhances metal-oxide-semiconductor (MOS) gas sensors by improving accuracy and stability. This review explores ML applications for gas analysis, material design, and overcoming sensor limitations.
Area of Science:
- Materials Science
- Sensor Technology
- Artificial Intelligence
Background:
- Metal-oxide-semiconductor (MOS) gas sensors are vital for environmental monitoring, industrial safety, and health diagnostics due to their ease of fabrication and integration.
- However, MOS sensors face challenges like cross-sensitivity, environmental interference, device drift, and nonlinear responses, limiting their accuracy and long-term stability in complex scenarios.
Purpose of the Study:
- To systematically review the application of machine learning (ML) in improving MOS gas sensor performance.
- To explore ML's role in quantitative gas analysis, species identification, drift compensation, stability enhancement, and material design.
- To compare different ML models for feature extraction, signal interpretation, and performance optimization in MOS gas sensing.
Main Methods:
- Review of recent literature on machine learning applications for MOS gas sensors.
- Analysis of ML techniques for data processing, nonlinear mapping, and pattern recognition in gas sensing.
- Comparison of various ML models regarding their suitability for feature extraction, signal interpretation, and performance optimization.
Main Results:
- Machine learning significantly improves MOS gas sensor performance by addressing challenges like cross-sensitivity and drift.
- ML aids in quantitative gas concentration analysis, gas species identification, and enhancing sensor stability.
- ML assists in designing novel gas-sensing materials and optimizing sensor performance through advanced data interpretation.
Conclusions:
- Machine learning offers powerful solutions for enhancing MOS gas sensor accuracy, stability, and functionality.
- Addressing challenges such as limited sample sizes, model generalization, interpretability, and edge deployment is crucial for future advancements.
- Deeper integration of ML with MOS gas sensors is key to developing intelligent gas-sensing systems.
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