Graphene-based gas/vapor sensors have attracted much attention in recent years due to their variety of structures, unique sensing performances, room-temperature working conditions, and tremendous application prospects, etc. Herein, we summarize recent advantages in graphene preparation, sensor construction, and sensing properties of various graphene-based gas/vapor sensors, such as NH₃, NO₂, H₂, CO, SO₂, H₂S, as well as vapor of volatile organic compounds. The detection mechanisms pertaining to various gases are also discussed. In conclusion part, some existing problems which may hinder the sensor applications are presented. Several possible methods to solve these problems are proposed, for example, conceived solutions, hybrid nanostructures, multiple sensor arrays, and new recognition algorithm.

As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring. Gas sensors based on conventional bulk materials often suffer from their poor surface-sensitive sites, leading to a very low gas adsorption ability. Moreover, the charge transportation efficiency is usually inhibited by the low defect density of surface-sensitive area than that in the interior. In this work, a gas sensing structure model based on CuS quantum dots/Bi₂S₃ nanosheets (CuS QDs/Bi₂S₃ NSs) inspired by artificial neuron network is constructed. Simulation analysis by density functional calculation revealed that CuS QDs and Bi₂S₃ NSs can be used as the main adsorption sites and charge transport pathways, respectively. Thus, the high-sensitivity sensing of NO₂ can be realized by designing the artificial neuron-like sensor. The experimental results showed that the CuS QDs with a size of about 8 nm are highly adsorbable, which can enhance the NO₂ sensitivity due to the rich sensitive sites and quantum size effect. The Bi₂S₃ NSs can be used as a charge transfer network channel to achieve efficient charge collection and transmission. The neuron-like sensor that simulates biological smell shows a significantly enhanced response value (3.4), excellent responsiveness (18 s) and recovery rate (338 s), low theoretical detection limit of 78 ppb, and excellent selectivity for NO₂. Furthermore, the developed wearable device can also realize the visual detection of NO₂ through real-time signal changes.