Toxic gases, such as NOx, SOx, H2S and other S-containing gases, cause numerous harmful effects on human health even at very low gas concentrations. Reliable detection of various gases in low concentration is mandatory in the fields such as industrial plants, environmental monitoring, air quality assurance, automotive technologies and so on. In this paper, the recent advances in electrochemical sensors for toxic gas detections were reviewed and summarized with a focus on NO2, SO2 and H2S gas sensors. The recent progress of the detection of each of these toxic gases was categorized by the highly explored sensing materials over the past few decades. The important sensing performance parameters like sensitivity/response, response and recovery times at certain gas concentration and operating temperature for different sensor materials and structures have been summarized and tabulated to provide a thorough performance comparison. A novel metric, sensitivity per ppm/response time ratio has been calculated for each sensor in order to compare the overall sensing performance on the same reference. It is found that hybrid materials-based sensors exhibit the highest average ratio for NO2 gas sensing, whereas GaN and metal-oxide based sensors possess the highest ratio for SO2 and H2S gas sensing, respectively. Recently, significant research efforts have been made exploring new sensor materials, such as graphene and its derivatives, transition metal dichalcogenides (TMDs), GaN, metal-metal oxide nanostructures, solid electrolytes and organic materials to detect the above-mentioned toxic gases. In addition, the contemporary progress in SO2 gas sensors based on zeolite and paper and H2S gas sensors based on colorimetric and metal-organic framework (MOF) structures have also been reviewed. Finally, this work reviewed the recent first principle studies on the interaction between gas molecules and novel promising materials like arsenene, borophene, blue phosphorene, GeSe monolayer and germanene. The goal is to understand the surface interaction mechanism.

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In the last two decades, GaN nanostructures of various forms like nanowires (NWs), nanotubes (NTs), nanofibers (NFs), nanoparticles (NPs) and nanonetworks (NNs) have been reported for gas sensing applications. In this paper, we have reviewed our group’s work and the works published by other groups on the advances in GaN nanostructures-based sensors for detection of gases such as hydrogen (H2), alcohols (R-OH), methane (CH4), benzene and its derivatives, nitric oxide (NO), nitrogen dioxide (NO2), sulfur-dioxide (SO2), ammonia (NH3), hydrogen sulfide (H2S) and carbon dioxide (CO2). The important sensing performance parameters like limit of detection, response/recovery time and operating temperature for different type of sensors have been summarized and tabulated to provide a thorough performance comparison. A novel metric, the product of response time and limit of detection, has been established, to quantify and compare the overall sensing performance of GaN nanostructure-based devices reported so far. According to this metric, it was found that the InGaN/GaN NW-based sensor exhibits superior overall sensing performance for H2 gas sensing, whereas the GaN/(TiO2–Pt) nanowire-nanoclusters (NWNCs)-based sensor is better for ethanol sensing. The GaN/TiO2 NWNC-based sensor is also well suited for TNT sensing. This paper has also reviewed density-functional theory (DFT)-based first principle studies on the interaction between gas molecules and GaN. The implementation of machine learning algorithms on GaN nanostructured sensors and sensor array has been analyzed as well. Finally, gas sensing mechanism on GaN nanostructure-based sensors at room temperature has been discussed.

In this study, electrical characteristics of MoTe2 field-effect transistors (FETs) are investigated as a function of channel thickness. The conductivity type in FETs, fabricated from exfoliated MoTe2 crystals, switched from p-type to ambipolar to n-type conduction with increasing MoTe2 channel thickness from 10.6 nm to 56.7 nm. This change in flake-thickness-dependent conducting behavior of MoTe2 FETs can be attributed to modulation of the Schottky barrier height and related bandgap alignment. Change in polarity as a function of channel thickness variation is also used for ammonia (NH3) sensing, which confirms the p- and n-type behavior of MoTe2 devices.

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Internet of Things applications require ultra-low power, integrable into electronic circuits and mini-sized chemical sensors for automated remote air quality monitoring system. In this work, a highly sensitive and selective detection of nitrogen dioxide (NO2) has been demonstrated by functionalizing gallium nitride (GaN) submicron wire with titania (TiO2) nanoclusters. The two-terminal GaN/TiO2 sensor device was fabricated by top-down approach. The photo-enabled sensing makes it possible to operate this sensor at room-temperature, resulting in a significant reduction in operating power. The GaN/TiO2 sensor was able to detect NO2 concentrations as low as 10 ppb in air at room temperature (20 °C) with a quick response-recovery process. The sensor was found highly selective toward NO2 against other interfering gases, such as ethanol (C2H5OH), ammonia (NH3), sulfur dioxide (SO2), methane (CH4) and carbon dioxide (CO2). Furthermore, principal component analysis has been performed to address the cross-sensitive nature of TiO2. The sensor device exhibited excellent long-term stability at room temperature and humidity and was quite stable and reliable at various environmental conditions. Continuous exposure of the device to siloxane for a one-month period has shown a very small degradation in sensor response to NO2. Finally, interaction of NO2 gas molecules with the GaN/TiO2 sensor has been modeled and explained under the light of energy band diagram. The photoinduced oxygen desorption and subsequent charge transfer between TiO2 nanoclusters and NO2 molecules modulate the depletion region width within the GaN, thus contributing to a high performance NO2 gas sensing.

In this work, a TiO2-coated GaN nanowire-based back-gate field-effect transistor (FET) device was designed and implemented to address the well-known cross-sensitive nature of metal oxides. Even though a two-terminal TiO2/GaN chemiresistor is highly sensitive to NO2, it suffers from lack of selectivity toward NO2 and SO2. Here, a Si back gate with C-AlGaN as the gate dielectric was demonstrated as a tunable parameter, which enhances discrimination of these cross-sensitive gases at room temperature (20 °C). Compared to no bias, a back-gate bias resulted in a significant 60% increase in NO2 response, whereas the increase was an insignificant 10% in SO2 response. The differential change in gas response was explained with the help of a band diagram, derived from the energetics of molecular models based on density functional theory (DFT). The device geometries in this work are not optimized and are intended only for proving the concept.

Accurate identification and quantification of gas mixtures are almost unattainable utilizing only a metal-oxide/GaN sensor because of its cross-sensitivity to many gases. Here, an array of sensors has been formed consisting of Ag and Pt incorporated ZnO, In2O3 and TiO2 coated two terminal GaN photoconductors. The common environmental toxic gases, such as SO2, NO2, H2, ethanol and their mixtures have been chosen as the gas analytes. All the gas responses have been obtained at 20 °C under UV illumination. Temporal responses have been post-processed to develop the training and test dataset. Then, four different artificial neural network models have been analyzed and optimized for gas classification study, which is done for the first time on GaN sensors. Statistical and computational complexity results indicate that back-propagation neural network (NN) stands out as the optimal classifier among the considered algorithms. Then, ppm concentrations of the identified gases have been estimated using the optimal model. Furthermore, implementation of the developed sensor array in combination with NN algorithm for real-time gas monitoring applications has been discussed.