Mid Sweden University

H2S gas is harmful for human health and environment, therefore novel gas sensors for real time and fast detection with high precision have been sought. Metal oxides are already known as promising candidate for this purpose. This article presents the performance of a gas sensor consists of a microheater and active layer formed on single alumina substrate for operating at high temperature applications. Ultrasonic spray pyrolysis deposition method was used to make both thick layer of SnO2 for microheater and thin and porous crystalline layer of SnO2 as sensing layer. The prepared sensor showed suitable dynamic response towards 10 to 50 ppm of H2S gas both in humid and dry conditions at 450 °C. In these experiments, the cross sensitivity of the sensor was also checked for other interfering gases e.g. CH4 and NO2.

The growth of zinc oxide thin films with controlled nanostructures on the heat resistant dielectric substrates is important for the fabrication of gas sensors, transparent electric heating elements, pyroelectric electron emitters, and many other potential electronic and optoelectronic applications. The preferred substrate for many of these applications is alumina, but the production of uniform ZnO layers on alumina is hindered by the large lattice mismatch between ZnO and Al2O3 hexagonal crystal structures. Here, we systematically investigate the growth process of ZnO thin films on alumina substrates using the ultrasonic spray pyrolysis (USP) of zinc chloride solutions in ethanol and, for the first time, demonstrate the deposition of uniform layers on the alumina substrates appropriately seeded using magnetron sputtering prior to USP. On the pristine substrates, random nucleation of the isolated nanocrystallites results in uneven layers, and extending the growth process leads to the hierarchical growth of facetted ZnO nanorods and pyramids with weak physical attachments to the substrate surface. In similar conditions, USP deposition on the seeded substrates reproducibly results in continuous networks of densely packed ZnO crystallites intimately attached to the substrate surface with adjustable thickness and electrical conductance. These results are compared with those obtained for SnO2 in similar conditions. Regardless of its tetragonal crystal structure, SnO2 reproducibly forms even layers on the pristine alumina substrates. 

Designing heterostructure materials at the nanoscale is a well-known method to enhance gas sensing performance. In this study, a mixed solution of zinc chloride and tin (II) chloride dihydrate, dissolved in ethanol solvent, was used as the initial precursor for depositing the sensing layer on alumina substrates using the ultrasonic spray pyrolysis (USP) method. Several ZnO/SnO2 heterostructures were grown by applying different ratios in the initial precursors. These heterostructures were used as active materials for the sensing of H2S gas molecules. The results revealed that an increase in the zinc chloride in the USP precursor alters the H2S sensitivity of the sensor. The optimal working temperature was found to be 450 °C. The sensor, containing 5:1 (ZnCl2: SnCl2·2H2O) ratio in the USP precursor, demonstrates a higher response than the pure SnO2 (∼95 times) sample and other heterostructures. Later, the selectivity of the ZnO/SnO2 heterostructures toward 5 ppm NO2, 200 ppm methanol, and 100 ppm of CH4, acetone, and ethanol was also examined. The gas sensing mechanism of the ZnO/SnO2 was analyzed and the remarkably enhanced gas-sensing performance was mainly attributed to the heterostructure formation between ZnO and SnO2. The synthesized materials were also analyzed by X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray, transmission electron microscopy, and X-ray photoelectron spectra to investigate the material distribution, grain size, and material quality of ZnO/SnO2 heterostructures. 

The National Institute for Occupational Safety and Health (NIOSH) has established exposure limits for sulfur-based volatile components, particularly hydrogen sulfide (H2S), at 20 ppm for an 8-hour exposure and 50 ppm for durations under 10 minutes. Detecting such toxic gases at low levels necessitates innovative sensor fabrication. This study introduces a unique sensor design, involving the direct thermophoretic deposition of SnO2 aerosol streams on one side and densely compacted SnO2 thick films via ultrasonic spray pyrolysis (UPS) on the other side, acting as a heater. Analyzing flame-made SnO2 particles using BET, XRD, and TEM techniques revealed highly crystalline particles approximately 8 nm in size. Methyl mercaptan (CH3SH) and H2S were employed as analyte gases, ranging from 20 ppb to 25 ppm and 20 ppb to 50 ppm, respectively. The results indicate that the flame-made SnO2 exhibits significant potential for developing gas sensors that are highly sensitive to CH3SH and H2S gases across a broad concentration range. The sensor demonstrates a linear increase in response at lower concentrations, saturating at concentrations exceeding 20 ppm. Consequently, highly sensitive gas sensors capable of detecting very low levels can be manufactured, suitable for machine learning applications in environmental monitoring, healthcare, and industrial safety. 

Hydrogen sulfide (H2S) is a highly toxic and corrosive gas commonly found in industrial emissions and natural gas processing, posing serious risks to human health and environmental safety even at low concentrations. The early detection of H2S is therefore critical for preventing accidents and ensuring compliance with safety regulations. This study presents the development of porous ZnO/SnO2-nanocomposite gas sensors tailored for the ultrasensitive detection of H2S at sub-ppb levels. Utilizing a screen-printing method, we fabricated five different sensor compositions—ranging from pure SnO2 to pure ZnO—and characterized their structural and morphological properties through X-ray diffraction (XRD) and scanning electron microscopy (SEM). Among these, the SnO2/ZnO sensor with a composition-weight ratio of 3:4 demonstrated the highest response at 325 °C, achieving a low detection limit of 0.14 ppb. The sensor was evaluated for detecting H2S concentrations ranging from 5 ppb to 500 ppb under dry, humid air and N2 conditions. The relative concentration error was carefully calculated based on analytical sensitivity, confirming the sensor’s precision in measuring gas concentrations. Our findings underscore the significant advantages of mixture nanocomposites in enhancing gas sensitivity, offering promising applications in environmental monitoring and industrial safety. This research paves the way for the advancement of highly effective gas sensors capable of operating under diverse conditions with high accuracy. 

The unique electronic properties of semiconductor nanowires, in particular silicon nanowires (SiNWs), are attractive for the label-free, real-time, and sensitive detection of various gases. Therefore, over the past two decades, extensive efforts have been made to study the gas sensing function of NWs. This review article presents the recent developments related to the applications of SiNWs for gas sensing. The content begins with the two basic synthesis approaches (top-down and bottom-up) whereby the advantages and disadvantages of each approach have been discussed. Afterwards, the basic sensing mechanism of SiNWs for both resistor and field effect transistor designs have been briefly described whereby the sensitivity and selectivity to gases after different functionalization methods have been further presented. In the final words, the challenges and future opportunities of SiNWs for gas sensing have been discussed.

Significant risks to public health and the environment are posed by the release of hazardous gases from industries such as pulp and paper. In this study, the aim was to develop a multi-sensor system with a minimal number of sensors to detect and identify hazardous gases. Training and test data for two gases, hydrogen sulfide and methyl mercaptan, which are known to contribute significantly to odors, were generated in a controlled laboratory environment. The performance of two deep learning models, a 1d-CNN and a stacked LSTM, for data fusion with different sensor configurations was evaluated. The performance of these models was compared with a baseline machine learning model. It was observed that the baseline model was outperformed by the deep learning models and achieved good accuracy with a four-sensor configuration. The potential of a cost-effective multi-sensor system and deep learning models in detecting and identifying hazardous gases is demonstrated by this study, which can be used to collect data from multiple locations and help guide the development of in-situ measurement systems for real-time detection and identification of hazardous gases at industrial sites. The proposed system has important implications for reducing pollution and protecting public health.