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GRAPHICAL ABSTRACT
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PUBLIC SUMMARY
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Artificial Intelligence (AI) enhances gas sensors for smarter, efficient chemical detection.
AI improves sensitivity, selectivity, and adaptability in dynamic environments.
Machine learning enables real-time data processing and accurate gas identification.
AI-integrated sensors benefit industry, environment, healthcare, and agriculture applications.
Future convergence with Internet of Things (IoT) and 5G boosts sensing capability.
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ABSTRACT
The integration of Artificial Intelligence (AI) with gas sensor technology has significantly advanced the field of chemical sensing, enabling more intelligent, efficient, and adaptive systems for a wide range of applications. Traditional gas sensors, such as electrochemical, semiconductor, and optical sensors, have limitations in terms of sensitivity, selectivity, and adaptability to dynamic environmental conditions. AI, particularly machine learning and deep learning techniques, offers a promising solution to overcome these challenges by enhancing sensor performance, enabling real-time data processing, and improving gas identification and concentration estimation. This review explores the applications of AI in gas sensing, focusing on AI’s role in sensor data preprocessing, feature extraction, classification, and regression tasks. Additionally, we examine recent advances in near-sensor and in-sensor computing architectures, together with deployment strategies that tightly integrate AI computation into sensor hardware. These approaches are discussed in terms of data processing efficiency, energy consumption, and latency, while also addressing persistent challenges associated with computational scalability and reliable operation in complex gas environments. Overcoming these challenges will require coordinated progress in sensor device engineering as well as the development of more adaptive and robust machine learning and AI algorithms. The review also highlights successful applications of AI-powered gas sensing systems in industrial monitoring, environmental protection, healthcare, and agriculture. Finally, we explore the future development of AI in gas sensing, focusing on the convergence of AI with emerging technologies like the Internet of Things (IoT), edge computing, and 5G, which promise to further enhance the capabilities of intelligent gas detection systems. -
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Yan J., Kang Y., Wang T., et al. (2025). AI empowers intelligent chemical sensing systems. The Innovation Informatics 1:100014. https://doi.org/10.59717/j.xinn-inform.2025.100014
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Yan J., Kang Y., Wang T., et al. (2025). AI empowers intelligent chemical sensing systems. The Innovation Informatics 1:100014. https://doi.org/10.59717/j.xinn-inform.2025.100014
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Figure 1.
The AI-enabled gas sensor data processing pipeline, which encompasses signal acquisition, preprocessing, feature extraction, model training, task-specific modeling for classification and regression, hardware-accelerated deployment and real-time adaptation.
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Figure 2.
Mixture-gas sensing systems embedded with AI computing
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Figure 3.
The conceptual framework of AI-driven intelligent gas sensing applications, illustrating their deployment across environmental monitoring (A),109 energy safety (B),110 agriculture (C),111 and healthcare (D).104
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Figure 4.
Development trends of AI-empowered gas sensing systems integrated with IoT, edge computing, and 5G.
