Today, resistance gas sensors which are mainly realized from metal oxides are among the most used sensing devices. However, generally, their sensing temperature is high and other materials with a lower operating temperature can be an alternative to them. Graphene and its derivatives with a 2D structure are among the most encouraging materials for gas-sensing purposes, because a 2D lattice with high surface area can maximize the interaction between the surface and gas, and a small variation in the carrier concentration of graphene can cause a notable modulation of electrical conductivity in graphene. However, they show weak sensing performance in pristine form. Hence, doping, and in particular N doping, can be one of the most promising strategies to enhance the gas-sensing features of graphene-based sensors. Herein, we discuss the gas-sensing properties of N-doped graphene and its derivatives. N doping can induce a band gap inside of graphene, generate defects, and enhance the conductivity of graphene, all factors which are beneficial for sensing studies. Additionally, not only is experimental research reviewed in this review paper, but theoretical works about N-doped graphene are also discussed.

Metal oxide nanowires (NWs) with a high surface area, ease of fabrication, and precise control over diameter and chemical composition are among the best candidates for the realization of resistive gas sensors. Among the different techniques used for the synthesis of materials with NW morphology, approaches based on the vapor–liquid–solid (VLS) mechanism are very popular due to the ease of synthesis, low price of starting materials, and possibility of branching. In this review article, we discuss the gas-sensing features of metal oxide NWs grown by the VLS mechanism, with emphasis on the growth conditions and sensing mechanism. The growth and sensing performance of SnO₂, ZnO, In₂O₃, NiO, CuO, and WO₃ materials with NW morphology are discussed. The effects of the catalyst type, growth temperature, and other variables on the morphology and gas-sensing performance of NWs are discussed.

Cd _x Zn_1−x O (0 ≤ x ≤ 0.20) thin films are deposited on soda lime glass substrates using spray pyrolysis technique. To check the thermal stability, Cd _x Zn_1−x O thin films are subjected to annealing. Both the as-deposited and annealed Cd _x Zn_1−x O thin films are characterized using X-ray diffraction (XRD), scanning electron microscope (SEM) and energy-dispersive X-ray analysis (EDAX) to check the structural, surface morphological and compositional properties, respectively. XRD analysis reveals that the both as-deposited and annealed Cd _x Zn_1−x O thin films are (002) oriented with wurtzite structure. SEM studies confirm that as-deposited, as well as annealed Cd _x Zn_1−x O thin films are free from pinholes and cracks. Compositional analysis shows the deficiency in Cd content after annealing. Optical properties evaluated from UV–Vis spectroscopy shows red shift in the band gap for Cd _x Zn_1−x O thin films. Electrical property measured using two probe method shows a decrease in the resistance after Cd incorporation. The results indicate that cadmium can be successfully incorporated in zinc oxide thin films to achieve structural changes in the properties of films.

Semiconducting two-dimensional (2D) materials have emerged as promising candidates for gas sensors due to their exceptional sensitivity and rapid response/recovery times. However, these sensors often face significant challenges, including baseline drift, nonlinearity, cross-sensitivity to multiple gases, and early response saturation, all of which compromise their accuracy and reliability. Conventional resistive sensing approaches, which rely on a single output signal for gas concentration estimation, fail to capture the complex interactions inherent to 2D materials, such as charge carrier generation, transport, and polarization. This work addresses these limitations by utilizing impedance measurements across multiple frequencies for MoS₂- and WS₂-based sensors, coupled with machine learning-assisted data processing for accurate relative humidity (RH) quantification. By leveraging the impedance domain, we effectively mitigated baseline drift over extended periods and identified mutually exclusive phase behavior for the WS₂-based sensor. The MoS₂-based sensor exhibited long-term stability, motivating the application of a neural network-based multilayer perceptron (MLP), one-dimensional convolutional network (1D-CNN), and long short-term memory (LSTM) models to interpret multifrequency impedance data for precise RH measurements. Our approach enabled robust humidity sensing over a wide range (0-90%) with significantly faster response and recovery times than commercial sensors. Additionally, the neural network-assisted WS₂ sensor effectively minimized cross-sensitivity between humidity and CO₂. This work showcases the potential of multifrequency impedance-based sensing, combined with machine learning, to overcome the traditional limitations of 2D material-based sensors, offering a pathway toward more reliable, stable, and precise gas-sensing technologies.

Nonlinearity, baseline drift, humidity interference, and selectivity are among the primary challenges associated with direct current measurements in semiconducting gas sensors today. This study demonstrates that integrating PtSnO₂ and MXene-based sensors enables high-performance gas sensing by effectively mitigating humidity-induced interference. Impedance measurements across multiple frequencies were employed, yielding sensors with tunable gas responses, low noise, extended dynamic range, enhanced baseline stability, and minimal humidity cross-sensitivity. PtSnO₂ was optimized for simultaneous NH₃ and relative humidity (RH) detection, while MXene served as a dedicated RH sensor. A multilayer perceptron was trained on the impedance dataset to deconvolute and accurately predict RH and NH₃ concentrations. The proposed sensor system and analytical framework were benchmarked against commercial DHT22 (humidity) and DFrobot (NH₃) sensors, demonstrating superior performance and sensitivity. This methodology is extendable to other material systems, including metal oxides and transition metal dichalcogenides, for advanced gas-sensing applications. These findings advance gas-sensing technology by improving detection accuracy and robustness in industrial safety, environmental monitoring, and biomedical diagnostics, including early disease detection.