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Review

Recent Advances in Resistive Gas Sensors: Fundamentals, Material and Device Design, and Intelligent Applications

1
School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2
Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
*
Authors to whom correspondence should be addressed.
Chemosensors 2025, 13(7), 224; https://doi.org/10.3390/chemosensors13070224 (registering DOI)
Submission received: 16 May 2025 / Revised: 16 June 2025 / Accepted: 18 June 2025 / Published: 21 June 2025

Abstract

Resistive gas sensors have attracted significant attention due to their simple architecture, low cost, and ease of integration, with widespread applications in environmental monitoring, industrial safety, and healthcare diagnostics. This review provides a comprehensive overview of recent advances in resistive gas sensors, focusing on their fundamental working mechanisms, sensing material design, device architecture optimization, and intelligent system integration. These sensors primarily operate based on changes in electrical resistance induced by interactions between gas molecules and sensing materials, including physical adsorption, charge transfer, and surface redox reactions. In terms of materials, metal oxide semiconductors, conductive polymers, carbon-based nanomaterials, and their composites have demonstrated enhanced sensitivity and selectivity through strategies such as doping, surface functionalization, and heterojunction engineering, while also enabling reduced operating temperatures. Device-level innovations—such as microheater integration, self-heated nanowires, and multi-sensor arrays—have further improved response speed and energy efficiency. Moreover, the incorporation of artificial intelligence (AI) and Internet of Things (IoT) technologies has significantly advanced signal processing, pattern recognition, and long-term operational stability. Machine learning (ML) algorithms have enabled intelligent design of novel sensing materials, optimized multi-gas identification, and enhanced data reliability in complex environments. These synergistic developments are driving resistive gas sensors toward low-power, highly integrated, and multifunctional platforms, particularly in emerging applications such as wearable electronics, breath diagnostics, and smart city infrastructure. This review concludes with a perspective on future research directions, emphasizing the importance of improving material stability, interference resistance, standardized fabrication, and intelligent system integration for large-scale practical deployment.
Keywords: resistive gas sensor; metal oxide; smart sensing; machine learning; material design; device optimization; environmental monitoring resistive gas sensor; metal oxide; smart sensing; machine learning; material design; device optimization; environmental monitoring

Share and Cite

MDPI and ACS Style

Wang, P.; Xu, S.; Shi, X.; Zhu, J.; Xiong, H.; Wen, H. Recent Advances in Resistive Gas Sensors: Fundamentals, Material and Device Design, and Intelligent Applications. Chemosensors 2025, 13, 224. https://doi.org/10.3390/chemosensors13070224

AMA Style

Wang P, Xu S, Shi X, Zhu J, Xiong H, Wen H. Recent Advances in Resistive Gas Sensors: Fundamentals, Material and Device Design, and Intelligent Applications. Chemosensors. 2025; 13(7):224. https://doi.org/10.3390/chemosensors13070224

Chicago/Turabian Style

Wang, Peiqingfeng, Shusheng Xu, Xuerong Shi, Jiaqing Zhu, Haichao Xiong, and Huimin Wen. 2025. "Recent Advances in Resistive Gas Sensors: Fundamentals, Material and Device Design, and Intelligent Applications" Chemosensors 13, no. 7: 224. https://doi.org/10.3390/chemosensors13070224

APA Style

Wang, P., Xu, S., Shi, X., Zhu, J., Xiong, H., & Wen, H. (2025). Recent Advances in Resistive Gas Sensors: Fundamentals, Material and Device Design, and Intelligent Applications. Chemosensors, 13(7), 224. https://doi.org/10.3390/chemosensors13070224

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