Nanomaterial-Optimized Device Construction and AI-Enhanced Signal Analysis of Gas Sensors

Dear Colleagues,

The development of gas sensors, as the core hardware components of artificial olfactory systems for odor digitization, has been a continuous hotspot in both academic research and industrial applications in recent years. The most widely used semiconductor-based gas sensors have been suffering from characteristics of poor selectivity, excessive power consumption, and poor stability, which severely limit their use in quantitative analysis, high-reliability scenarios, and small smart devices. The rise of nanofunctional materials and artificial intelligence technologies in recent decades has delivered new approaches and further possibilities to address the above gas sensor-associated dilemmas. Novel nanomaterials, such as MXenes, TMDs, MOFs, COFs, etc., have attracted significant attention from many scholars and demonstrated their outstanding properties in gas sensing applications, such as controllable specific modifications, flexibility, and room-temperature applications. Artificial intelligence techniques, especially deep learning methods that have emerged in recent years, have proven to be powerful tools for further enhancing sensor performance and expanding their applications. This Special Issue will focus on the latest research progress in two aspects: One is the modulation mechanism of nanosensitive materials on the response and selectivity of gas sensors. The other is the enhancement of data analysis methods for encoding and decoding output signals. We anticipate that this Special Issue will discuss the development of gas sensors from a range of unique perspectives and inspire the innovation of multidisciplinary technologies to promote the advancement of sensor technologies for a sustainable future.

We are looking forward to your contributions.

Dr. Tao Wang
Dr. Tetsuya Shimamura
Dr. Jia Yan
Dr. Mingzhi Jiao
Guest Editors

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• gas sensor
• chemiresistive sensor
• nanomaterials
• constructing heterojunctions
• two-dimensional materials
• electronic sensitization
• machine learning
• deep learning
• electronic nose
• signal processing