Mid Sweden University

A system for the measurement of methane concentration in water is presented. The system is a stand- alone device using a high-resolution NDIR (Non-Dispersive Infra-Red) gas sensor. The NDIR sensor is configured to measure methane, water vapor, and carbon dioxide in the air. It is mounted in a housing with a stabilized environment and includes cross-sensitivity compensation. An equilibrator is used to transfer the methane concentration from the water into a circulating gas flow that is analyzed by the NDIR gas sensor. The equilibrator consists of a vertical plastic tube filled with 2,000 glass marbles, where the water runs from top to bottom on the surface of the glass marbles, in contact with a circulating air flow, exchanging gas. The system is stand- alone, including power supply and logging features for 72 hours of operation. The system performance was evaluated in a field test, measuring the methane content of seawater at a fiber bank in Sundsvall, Sweden. This fiber bank consists of remaining waste from an old paper industry from before 1970 and is known to produce methane. The detection limit of the tested system is below 1.4 nmol/L in water, corresponding to 1 ppm methane concentration in the air. The settling time of the system in its current configuration, including the equilibrator and gas sensor housing, is 30 minutes. 

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 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. 

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.

Measurements of atmospheric gas concentrations using of NDIR gas sensors requires compensation of ambient pressure variations to achieve reliable result. The extensively used general correction method is based on collecting data for varying pressures for a single reference concentration. This one-dimensional compensation approach is valid for measurements carried out in gas concentrations close to reference concentration but will introduce significant errors for concentrations further away from the calibration point. For applications, requiring high accuracy, collecting, and storing calibration data at several reference concentrations can reduce the error. However, this method will cause higher demands on memory capacity and computational power, which is problematic for cost sensitive applications. We present here an advanced, but practical, algorithm for compensation of environmental pressure variations for relatively low-cost/high resolution NDIR systems. The algorithm consists of a two-dimensional compensation procedure, which widens the valid pressure and concentrations range but with a minimal need to store calibration data, compared to the general one-dimensional compensation method based on a single reference concentration. The implementation of the presented two-dimensional algorithm was verified at two independent concentrations. The results show a reduction in the compensation error from 5.1% and 7.3%, for the one-dimensional method, to −0.02% and 0.83% for the two-dimensional algorithm. In addition, the presented two-dimensional algorithm only requires calibration in four reference gases and the storing of four sets of polynomial coefficients used for calculations. 

A selective algorithm for nondispersive infrared (NDIR) sensing of gases with overlapping absorption spectra was developed and evaluated in modified multichannel NDIR sensor. Measurements in the optic channel with the spectral band where two gas species (target and secondary gas) have overlapping absorption lines are complemented by additional measurements in second channel where spectral absorption for only one gas (secondary gas) is present. The real concentration for the target gas is retrieved by adjusting the absorption data obtained in the overlapping gas spectra's optic channel, with respect to the absorption data retrieved in the second optic channel that has sensitivity only for the secondary gas. An implementation example is performed by obtaining the true concentration of CH4 (as target gas) in a mixture with H2O vapor. The channel for the target gas is equipped by an optic filter with spectra at 3.375 μm where both CH4 and H2O have absorption lines. The complementary second channel provides sensing in spectra at 2.7 μm where only H2O have absorption. Data from a third channel, at 3.95 μm, is used as reference value for 'zero-level' calibration. A calibration procedure was developed and tested, which involves matching of the absorbed light energy in target and secondary channels in humid reference environments. A selective algorithm for sensing of CH4 with elimination of spectral impact from H2O was validated in environments with variable CH4 and H2O concentrations. By implementing the multispectral approach and the developed algorithm, an uncertainties of 5-10 ppm relative the reference concentrations were achieved. For the environments where selective algorithm was validated this should be compared to an uncertainty of 70-90 ppm for the non-corrected CH4 concentration. 

A multispectral nondispersive infrared (NDIR) sensor was developed for simultaneous detection of methane and water vapor in air. The NDIR sensor is capable of measuring optic transmission in the CH4 absorption spectra at 3.375 μm and the H2O absorption spectra at 2.7 μm. Data from a third channel, 3.95 μm, is used as reference value for 'zero-level' calibration. The actual CH4 concentration is retrieved by adjusting the data obtained in the CH4 spectra with respect to the concentration sensed in the H2O spectra. A calibration procedure was developed and tested, which involves matching of the absorbed light energy in the CH4 and the H2O spectrum in humid reference environments. A compensation algorithm for elimination of humidity impact was developed and validated in environments with variable CH4 and H2O concentrations. By implementing the multispectral approach, and the developed algorithm, an uncertainty of 15-25 ppm relative the reference concentrations was achieved. For a concentration range valid for environmental monitoring applications this should be compared to an uncertainty of 180-200 ppm for the non-corrected CH4 concentration. 

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. 

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 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.