Adsorption behavior of NO2 molecules in ZnO-mono/multilayer graphene core–shell quantum dots for NO2 gas sensor

Carbon based sensors for air quality monitoring networks; middle east perspective

IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of pollution trends, informed decision-making, and increased public awareness. Air quality and dust pollution in the Middle East region may leads to various health issues, particularly among vulnerable populations. IoT-based Sensors networks help mitigate health risks by offering timely and accurate air quality data. Air pollution affects not only human health but also the region's ecosystems and contributes to climate change. The economic implications of deteriorated air quality include healthcare costs and decreased productivity, underscore the need for effective monitoring and mitigation. IoT-based data can guide policymakers to align with Sustainable Development Goals (SDGs) related to health, clean water, and climate action. The conventional monitor based standard air quality instruments provide limited spatial coverage so there is strong need to continue research integrated with low-cost sensor technologies to make air quality monitoring more accessible, even in resource-constrained regions. IoT-based Sensors networks monitoring helps in understanding these environmental impacts. Among these IoT-based Sensors networks, sensors are of vital importance. With the evolution of sensors technologies, different types of sensors materials are available. Among this carbon based sensors are widely used for air quality monitoring. Carbon nanomaterial-based sensors (CNS) and carbon nanotubes (CNTs) as adsorbents exhibit unique capabilities in the measurement of air pollutants. These sensors are used to detect gaseous pollutants that includes oxides of nitrogen and Sulphur, and ozone, and volatile organic compounds (VOCs). This study provides comprehensive review of integration of carbon nanomaterials based sensors in IoT based network for better air quality monitoring and exploring the potential of machine learning and artificial intelligence for advanced data analysis, pollution source identification, integration of satellite and ground-based networks and future forecasting to design effective mitigation strategies. By prioritizing these recommendations, the Middle East and other regions, can further leverage IoT-based systems to improve air quality monitoring, safeguard public health, protect the environment, and contribute to sustainable development in the region.

Machine-Learning-Aided NO2 Discrimination with an Array of Graphene Chemiresistors Covalently Functionalized by Diazonium Chemistry

Boosted by the emerging need for highly integrated gas sensors in the internet of things (IoT) ecosystems, electronic noses (e-noses) are gaining interest for the detection of specific molecules over a background of interfering gases. The sensing of nitrogen dioxide is particularly relevant for applications in environmental monitoring and precision medicine. Here we present an easy and efficient functionalization procedure to covalently modify graphene layers, taking advantage of diazonium chemistry. Separate graphene layers were functionalized with one of three different aryl rings: 4-nitrophenyl, 4-carboxyphenyl and 4-bromophenyl. The distinct modified graphene layers were assembled with a pristine layer into an e-nose for NO2 discrimination. A remarkable sensitivity to NO2 was demonstrated through exposure to gaseous solutions with NO2 concentrations in the 1-10 ppm range at room temperature. Then, the discrimination capability of the sensor array was tested by carrying out exposure to several interfering gases and analyzing the data through multivariate statistical analysis. This analysis showed that the e-nose can discriminate NO2 among all the interfering gases in a two-dimensional principal component analysis space. Finally, the e-nose was trained to accurately recognize NO2 contributions with a linear discriminant analysis approach, thus providing a metric for discrimination assessment with a prediction accuracy above 95 %.

MoS2-NiO nanocomposite for H2S sensing at room temperature

The layered 2-D materials, such as molybdenum disulfide (MoS2), are among the most promising candidates for detecting H2S gas at very low concentrations. Herein, we have designed a series of novel nanocomposites consisting of MoS2 and NiO. These materials were synthesized via a simple hydrothermal method. The microstructure and morphology of nanocomposites were studied using different characterization techniques, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), Brunauer-Emmett-Teller (BET) analysis, and X-ray photoelectron spectroscopy (XPS). These nanocomposites were used as gas sensors, and the highest response (6.3) towards 10 ppm H2S was detected by the MNO-10 gas sensor among all the tested sensors. The response value (Rg/Ra) was almost three times that of pure NiO (Rg/Ra = 2). Besides, the MNO-10 sensor exposed good selectivity, short response/recovery time (50/20 s), long-term stability (28 days), reproducibility (6 cycles), and a low detection limit (2 ppm) towards H2S gas at RT. The excellent performance of MNO-10 may be attributed to some features of MoS2, such as a layered structure, higher BET surface area, higher active sites, and a synergistic effect between MoS2 and NiO. This simple fabrication sensor throws a novel idea for detecting H2S gas.

In Situ Synthesis of Hierarchical Flower-like Sn/SnO2 Heterogeneous Structure for Ethanol GAS Detection

In this study, morphogenetic-based Sn/SnO₂ graded-structure composites were created by synthesizing two-dimensional SnO sheets using a hydrothermal technique, self-assembling into flower-like structures with an average petal width of roughly 3 um. The morphology and structure of the as-synthesized samples were characterized by utilizing SEM, XRD, XPS, etc. The gas-sensing characteristics of gas sensors based on the flower-like Sn/SnO₂ were thoroughly researched. The sensor displayed exceptional selectivity, a rapid response time of 4 s, and an ultrahigh response at 250 °C (Ra/Rg = 17.46). The excellent and enhanced ethanol-gas-sensing properties were mainly owing to the three-dimensional structure and the rise in the Schottky barrier caused by the in situ production of tin particles.