This paper investigates the effect of decorating graphene with zinc oxide (ZnO) nanoparticles (NPs) for the detection of NO₂. In this regard, two graphene sensors with different ZnO loadings of 5 wt.% and 20 wt.% were prepared, and their responses towards NO₂ at room temperature and different conditions were compared. The experimental results demonstrate that the graphene loaded with 5 wt.% ZnO NPs (G95/5) shows better performance at detecting low concentrations of the target gas than the one loaded with 20 wt.% ZnO NPs (G80/20). Moreover, measurements under dry and humid conditions of the G95/5 sensor revealed that the material is very sensitive to ambient moisture, showing an almost eight-fold increase in NO₂ sensitivity when the background changes from dry to 70% relative humidity. Regarding sensor selectivity, it presents a significant selectivity towards NO₂ compared to other gas compounds.

This paper presents the integration of a sensing layer over interdigitated electrodes and an electronic circuit on the same flexible printed circuit board. This integration provides an effective technique to use this design as a wearable gas measuring system in a target application, exhibiting high performance, low power consumption, and being lightweight for on-site monitoring. The wearable system proves the concept of using an NFC tag combined with a chemoresistive gas sensor as a cumulative gas sensor, having the possibility of holding the data for a working day, and completely capturing the exposure of a person to NO₂ concentrations. Three different types of sensors were tested, depositing the sensing layers on gold electrodes over Kapton substrate: bare graphene, graphene decorated with 5 wt.% zinc oxide nanoflowers, or nanopillars. The deposited layers were characterized using FESEM, EDX, XRD, and Raman spectroscopy to determine their crystalline structure, morphological and chemical compositions. The gas sensing performance of the sensors was analyzed against NO₂ (dry and humid conditions) and other interfering species (dry conditions) to check their sensitivity and selectivity. The resultant-built wearable NFC tag system accumulates the data in a non-volatile memory every minute and has an average low power consumption of 24.9 µW in dynamic operation. Also, it can be easily attached to a work vest.

Nanosized manganese dioxide (MnO₂) material has been successfully incorporated into a graphene oxide (GO) sensitive layer. Since this type of heterojunction has never been reported in the literature related to gas sensing, these sensors were prepared, tested, and reported. The morphological properties and composition of the MnO₂@GO material have been thoroughly studied via FESEM, XRD, Raman spectroscopy, HR-TEM, and ATR-IR. Gas sensitivity and selectivity towards mainly NO₂ and other gases (NH₃, CO, ethanol, benzene, and H₂) have also been studied. The obtained sensors were exposed to different concentrations of NO₂ ranging from 200 ppb to 1000 ppb at 150 °C and under close to real conditions (25% relative humidity and 70% relative humidity). The MnO₂@GO sensors have shown a high response of 16.3% towards 1 ppm of NO₂ under dry conditions and a higher response of 44% at 70% RH towards the same concentration. Finally, it has also shown a strong sensitivity for NO₂.

Manganese dioxide (MnO₂) has drawn attention as a sensitiser to be incorporated in graphene-based chemoresistive sensors thanks to its promising properties. In this regard, a rGO@MnO₂ sensing material was prepared and deposited on two different substrates (silicon and Kapton). The effect of the substrate nature on the morphology and sensing behaviour of the rGO@MnO₂ material was thoroughly analysed and reported. These sensors were exposed to different dilutions of NO₂ ranging from 200 ppb to 1000 ppb under dry and humid conditions (25% RH and 70% RH) at room temperature. rGO@MnO₂ deposited on Kapton showed the highest response of 6.6% towards 1 ppm of NO₂ under dry conditions at RT. Other gases or vapours such as NH₃, CO, ethanol, H₂ and benzene were also tested. FESEM, HRTEM, Raman, XRD and ATR-IR were used to characterise the prepared sensors. The experimental results showed that the incorporation of nanosized MnO₂ in the rGO material enhanced its response towards NO₂. Moreover, this material also showed very good responses toward NH₃ both under dry and humid conditions, with the rGO@MnO₂ sensor on silicon showing the highest response of 18.5% towards 50 ppm of NH₃ under 50% RH at RT. Finally, the synthetised layers showed no cross-responsiveness towards other toxic gases.

This work presents the design and setup of an IoT platform at level four of the technology readiness level (TRL-4), to detect, classify, and quantify pollutant gases. This study combines concepts such as wireless sensor networks, arrays of sensors, and multivariate data analysis to interface different nano-structured chemoresistor gas sensors. The IoT platform consists of several gas sensor nodes with Wi-Fi capability to send data from a sensor array to a server and its user interface. Each gas sensor node interfaces one sensor array (up to four chemoresistor gas sensors and one temperature and humidity sensor). The server channels the data from the gas sensor nodes to the user interface. The platform was set up following a two-stage methodology. First (training stage), sensor data was received, stored, and used to train different multilayer perceptron (MLP) artificial neural networks (ANNs). Second (recognition stage), models were implemented in the user interface to classify and quantify the presence of pollutants. The platform was tested in laboratory conditions under exposure to nitrogen dioxide and ammonia at different % RH. As a result, the platform improves the classification and quantification times compared with the single-sensor approach. Additionally, the system was evaluated using a gas mixture of both gases, showing a classification accuracy exceeding 99 %. Likewise, the training and recognition stages can be repeated to add new chemoresistor gas sensors in the node, add new nodes to the platform, and deploy the nodes in different scenarios.

Manganese dioxide (MnO2) has drawn attention as a sensitizer to be incorporated in graphene-based chemoresistive sensors thanks to its promising properties. In this regard, a rGO@MnO2 sensing material was prepared and deposited on two different substrates (Silicon and Kapton). These sensors were exposed to different dilutions of NO2 under dry and humid conditions at room temperature. Other gases or vapours such as NH3, CO, ethanol and H2 were also tested. FESEM, HRTEM, RAMAN and XRD were used to characterize the prepared sensors. The experimental results showed that the incorporation of MnO2 in the rGO material enhanced its response towards NO2. Moreover, this material showed also very good responses toward NH3 both under dry and humid conditions and showed no cross-responsiveness towards other toxic gases.