Emerging Trends in Single-Atom Engineered Materials for Intelligent Gas Sensing: Materials Design, Machine Learning, and Future Opportunities

Intelligent gas sensors (IGSs) have garnered significant attention owing to their highly sensitive, selective, and reliable gas detection in industrial safety, environmental monitoring, food quality assessment, healthcare diagnostics, and smart Internet of Things (IoT) frameworks. While traditional metal oxide semiconductor (MOS) gas sensors demonstrate exceptional sensitivity and cost-effective fabrication, their practical implementation is typically hindered by slow recovery, poor selectivity, long-term instability, and susceptibility to environmental interference. In recent times, single-atom engineered materials, specifically single-atom catalysts (SACs), have attracted considerable attention because isolated metal atoms maximize atomic utilization efficiency while providing highly uniform and well-defined catalytic active sites. These exceptional atomic-scale properties substantially enhance charge transfer, gas adsorption, surface reaction kinetics, and catalytic activity, resulting in marked improvements in sensing performance. At the same time, the rapid advancement of artificial intelligence (AI) and machine learning (ML) has transformed traditional gas sensors into smart sensing technologies with optimized pattern recognition, feature extraction, gas classification, concentration prediction, drift compensation, and instantaneous decision-making. In this review, we have comprehensively summarized the recent advancements in single-atom engineered materials for smart gas sensing. The fundamental sensing mechanisms of single-atom materials, such as electronic structure engineering, adsorption modulation, and catalytic activation, are first discussed. Afterward, recent progress in SA-supported MOs gas sensors (SAC/MO-GSs) is thoroughly addressed, with special focus on SA classification, anchoring and stabilization strategies, metal–support interactions, and their impact on catalytic reactions, gas adsorption, charge transfer, and sensing performance. The incorporation of ML algorithms-such as random forests (RF), support vector machines (SVMs), k-nearest neighbors (k-NNs), principal component analysis (PCA), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and deep learning architectures-for smart gas sensing and sensor-array design is critically examined. Additionally, current challenges relating to atomic stability, scalable synthesis, data availability, computational cost, model generalization, practical device integration, and long-term durability are reviewed alongside emerging prospects in explainable AI, edge computing, digital twins, and self-powered wearable sensing devices. In conclusion, we have proposed future research directions toward the design of next-generation smart gas sensors that integrate atomic-scale sensing material engineering along with cutting-edge artificial intelligence, offering a route toward low-power, accurate, and autonomous gas-sensing applications.