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Chemiresistive gas sensors for intelligent sensing: design strategies, emerging applications and future challenges
Chencheng Hu1, Wei Zhang2, Jiaqi Yang1
1State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, China. linxu@jlu.edu.cn.
Abstract:
Intelligent chemiresistive gas sensing platforms are emerging as pivotal technologies for addressing global challenges such as climate change, urbanization, and public health crises. As core components of modern sensing systems, these platforms demonstrate immense potential in environmental monitoring, industrial safety, and medical diagnostics. Chemiresistive gas sensors, with their high sensitivity, rapid response, low power consumption, and miniaturization capabilities, serve as a key enabler for next-generation intelligent gas sensing. However, critical challenges remain in selectivity, long-term stability, energy efficiency, and scalable manufacturing. Recent advancements in materials science, including the development of novel materials such as metal-organic frameworks, two-dimensional transition metal dichalcogenides, and MXenes, has expanded design possibilities for sensors. Meanwhile, progress in artificial intelligence and the Internet of Things have significantly enhanced chemiresistive sensors, where machine learning algorithms improve selectivity, dynamically compensate for environmental interference, and facilitate distributed data training to optimize performance. This review systematically examines the design strategies, emerging applications, and current challenges of intelligent chemiresistive gas sensing platforms, with a focus on their multidimensional evolution from material innovation to cognitive decision-making. Furthermore, it explores future directions in environmental sustainability and interdisciplinary research. By integrating hardware-software co-design, advanced signal processing, and energy management strategies, intelligent chemiresistive gas sensing platforms are poised to transition from standalone detection to integrated system functionality, laying the foundation for responsive sensing ecosystems.
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