Advances in metal oxide semiconductor gas sensor arrays based on machine learning algorithms

• Published in: Journal of materials chemistry. C, Materials for optical and electronic devices Vol. 13; no. 9; pp. 4285 - 433
• Main Authors: Han, Jiayue, Li, Huizi, Cheng, Jiangong, Ma, Xiang, Fu, Yanyan
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 27.02.2025
• Subjects: Algorithms; Data processing; Gas sensors; Gases; Machine learning; Metal oxide semiconductors; Metal oxides; Neural networks; Sensor arrays; Sensors
• ISSN: 2050-7526; 2050-7534
• DOI: 10.1039/d4tc05220j

Metal oxide semiconductor (MOS) gas sensors have garnered significant attention for their excellent sensitivity and rapid response times. However, distinguishing similar gases in complex environments remains a major challenge. Integrating sensor arrays with machine learning algorithms significantly enhances gas recognition and detection accuracy, making it a key approach for intelligent gas monitoring. This review summarizes recent advances in MOS gas sensor arrays driven by machine learning algorithms. It further explores the mechanisms of MOS/MOS sensor arrays, conventional sensing materials and machine learning algorithms suitable for gas sensor arrays. Additionally, this review reports, summarizes, and evaluates both classical gas sensing algorithms and neural network-based algorithms for gas identification, considering aspects such as operating principles, advantages and disadvantages, and practical applications. In conclusion, this study considers the current landscape and challenges, providing predictions for future research directions. It is hoped that this work will contribute positively to the progression of machine learning-assisted MOS gas sensor arrays and offer valuable insights for gas sensing data processing. In this article, we summarize the progress of materials, mechanisms and ML-assisted gas sensing data processing for MOS gas sensor arrays, with a view to providing a breakthrough direction for future research.