Methane Detection Research Articles

NiO/ZnO heterojunction microspheres for methane detection at room temperature

Low working temperature CH4 gas sensors are necessary to explore to obtain intrinsic safety. In this work, NiO/ZnO microspheres were synthesized through facile hydrothermal methods and the strategy of photo-activation was used to develop room-temperature CH4 gas sensors. The characterizations of as-prepared samples were measured by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), UV–vis spectroscopy, photoluminescence spectra, and electrochemical impedance spectroscopy. The results indicated that NiO/ZnO is a microsphere structure assembled with porous nanosheets. NiO/ZnO composite had good optical properties to use light energy. The methane gas-sensing performances were systematically investigated at room temperature. It shows that the prepared sensors exhibit good CH4 gas sensing performances at room temperature under UV illumination. NiO/ZnO microspheres exhibited enhanced response values. In particular, NiO/ZnO-2 sensor exhibited the highest response (8.61–5000 ppm CH4), which is 3.42 times of pure ZnO, low detection limit, good selectivity, and repeatability under UV light. The excellent gas sensing performances may be related to the UV light and the formation of heterojunction, which widens the depletion layer and increases the amount of valid carriers. This work gives a sight to explore photo-activated CH4 gas sensors.

An open path optical sensor for hydrogen sulfide and methane detection by QCL

An open path optical sensor for hydrogen sulfide and methane detection by QCL

Mid-infrared auto-correction on-chip waveguide gas sensor based on 2f/1f wavelength modulation spectroscopy.

Compared to the most commonly used on-chip direct absorption spectroscopy (DAS) gas detection technique, the second harmonic (2f) based on-chip wavelength modulation spectroscopy (WMS) proposed by our group has the faculty to suppress noise and improve performance, but the accuracy of 2f WMS is easily affected by optical power variation. A mid-infrared auto-correction on-chip gas sensor based on 2f/1f WMS was proposed for decreasing the influence of the variation of optical power. The limit of detection of methane (CH4) obtained by a chalcogenide waveguide with a length of 10 mm is 0.031%. Compared with the 2f WMS, the maximum relative concentration error of the auto-correction on-chip gas sensor was decreased by ∼5.6 times. The measurement error is ≤2% in a temperature variation range of 30°C. This auto-correction sensor without a complicated manual calibration is helpful to the high accuracy measurement for on-chip integrated gas sensing.

Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models.

The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the best-performing weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH4 as a classification problem and (ii) predict the intensity of CH4 as a regression problem. The classification model performance for CH4 detection was evaluated using accuracy, F1 score, Matthew's Correlation Coefficient (MCC), and the area under the receiver operating characteristic curve (AUC ROC), with the top-performing model being 97.2%, 0.972, 0.945 and 0.995, respectively. The R 2 score was used to evaluate the regression model performance for CH4 intensity prediction, with the R 2 score of the best-performing model being 0.858. The ML models developed in this study for fugitive CH4 detection and intensity prediction can be used with fixed environmental sensors deployed on the ground or with sensors mounted on unmanned aerial vehicles (UAVs) for mobile detection.

A Practical Framework for Oil and Gas Operators to Estimate Methane Emission Duration Using Operational Data

Summary Methane management is a key greenhouse gas (GHG) reduction focus within the oil and gas industry. While there are a variety of techniques for methane detection and measurement, aircraft-mounted sensors have become popular for both academic studies and operators in onshore regions in North America due to their ability to screen many sites in a relatively short amount of time. Many traditional leak detection and repair (LDAR) techniques, like optical gas imaging (OGI), and emerging approaches, such as aircraft-based or satellite-based screenings, are periodic, which means that they can provide information about the presence of emissions and approximate rates during the observation period. Scaling periodic observations to annual emission estimates needed for regulatory and corporate reporting requires information on emission duration, which is not generally provided by the same screening techniques. In the literature to date, there have been several statistical approaches proposed for the annualization of basin-level methane observations, but these techniques may struggle to downscale to individual operators, across which methane emission intensities are expected to vary. In this paper, we propose a novel, conceptual framework for using other sources of data, such as records from operator inspections and parametric monitoring, to help define the duration of detected methane emissions. Such information may prove to be a useful input to measurement-informed methane emission protocols that are under development by multistakeholder groups, like GTI Energy Veritas and federal agencies in the United States.

Highly efficient and selective integrated directional couplers for multigas sensing applications

The design and fabrication of a compact, low-loss, broadband directional coupler (DC) based duplexer operating in the near-infrared (NIR) region are demonstrated. The duplexer exhibits high selectivity and coupling efficiency (CE), for target wavelengths of 1530 nm and 1653.7 nm, making it applicable in systems for the multi-gas detection of ammonia and methane. The measured CE for the duplexer is 73% and 76% at 1530 nm and 1653.7 nm respectively. These results demonstrate the effectiveness of the duplexer as a broadband and scalable power source for highly sensitive sensing techniques, like quartz-enhanced photoacoustic spectroscopy (QEPAS). Its compact size and low-loss characteristics make it highly portable and well-suited for drone-based multi-gas detection applications.

Sub-ppm Methane Detection with Mid-Infrared Slot Waveguides.

Hybrid integration of photonic chips with electronic and micromechanical circuits is projected to bring about miniature, but still highly accurate and reliable, laser spectroscopic sensors for both climate research and industrial applications. However, the sensitivity of chip-scale devices has been limited by immature and lossy photonic waveguides, weak light-analyte interaction, and etalon effects from chip facets and defects. Addressing these challenges, we present a nanophotonic waveguide for methane detection at 3270.4 nm delivering a limit of detection of 0.3 ppm, over 2 orders of magnitude lower than the state-of-the-art of on-chip spectroscopy. We achieved this result with a Si slot waveguide designed to maximize the light-analyte interaction, while special double-tip fork couplers at waveguide facets suppress spurious etalon fringes. We also study and discuss the effect of adsorbed humidity on the performance of mid-infrared waveguides around 3 μm, which has been repeatedly overlooked in previous reports.

Miniaturized and Portable Laser Gas Sensor for Standoff Methane Detection With Non-Cooperative Targets.

A standoff methane (CH4) sensor with actual hard topographic targets (usually called non-cooperative targets) is essential for natural gas pipeline leakage inspection and many other practical applications. To address this requirement, a miniaturized and low-power-consumption gas sensor was developed based on tunable diode laser absorption spectroscopy for standoff CH4 detection with a non-cooperative target. Wavelength modulation spectroscopy with a 1f normalized 2f detection method was employed for calibration-free CH4 measurement. A Kalman filter algorithm was used to improve the precision of the detection. The performance of the standoff CH4 sensor was evaluated comprehensively under various conditions, including different incident angles, different hard topographic targets, and different standoff distances. The results show that the measurement precision is 0.107% and the sensitivity is 4.08 parts per million per meter (ppm·m) with a time resolution of 1 s and a standoff distance of 40 m. The detection limit can achieve 1.24 ppm·m at an optimal integration time of 70 s. This sensor can be easily integrated into mobile platforms, which lays the foundation for intelligent leak inspection.

Transformer oil-dissolved methane detection based on non-adiabatic tapered fiber using polyacrylate and cryptophane-A overlay deposition

Transformer overheating faults can lead to an increase in the transformer oil-dissolved methane content, therefore, the high-precision in-situ detection of oil-dissolved methane is significant for early-stage transformer fault diagnosis. However, traditional methods can not achieve in-situ monitoring, and there is a shortage of methane-sensitive materials. To enable high-sensitivity in-situ detection of transformer oil-dissolved methane, this paper proposed a methane sensor based on the evanescent field of the non-adiabatic tapered optical fiber (NATOF), utilizing the specific adsorption of cryptophane-A to methane. A NATOF with a waist diameter of 8.17 µm and length of 8 mm was fabricated using the melt-drawn tapering method, and a polyacrylate/cryptophane-A film was deposited on the surface of the NATOF by drip-coating method. The sensor's response to transformer oil-dissolved methane was experimentally investigated. The results showed that the sensor exhibited a detection sensitivity of 9.8 pm/ppm for transformer oil-dissolved methane, with a detection limit of 2.35 ppm. Furthermore, the sensor's excellent reversibility was validated through cyclic experiments. The sensor proposed in this paper can realize the in-situ detection of transformer oil-dissolved methane.

Enhancing methane sensing with NDIR technology: Current trends and future prospects

Abstract This study presents an in-depth review of non-dispersive infrared (NDIR) sensors for methane detection, focusing on their principles of operation, performance characteristics, advanced signal processing techniques, multi-gas detection capabilities, and applications in various industries. NDIR sensors offer significant advantages in methane sensing, including high sensitivity, selectivity, and long-term stability. The underlying principles of NDIR sensors involve measuring the absorption of infrared radiation by the target gas molecules, leading to precise and reliable methane concentration measurements. Advanced signal processing techniques, such as single-frequency filtering and wavelet filtering algorithms, have been explored to improve the performance of the sensor by reducing noise, enhancing the signal-to-noise ratio, and achieving more accurate results. In the context of multi-gas detection, NDIR sensors face challenges due to overlapping absorption spectra. However, various solutions, including narrow-band optical bandpass filters, gas filter correlation techniques, and machine learning algorithms, have been proposed to address these issues effectively. This study delves into specific applications of NDIR sensors in various industries, such as coal mines, wastewater treatment plants, and agriculture. In these settings, NDIR sensors have demonstrated their reliability, accuracy, and real-time monitoring capabilities, contributing to environmental protection, safety, and energy recovery. Furthermore, the anticipated future trends and developments in NDIR methane detection technology are explored, including increased miniaturization, integration with artificial intelligence, improvements in power efficiency, and the development of multi-gas NDIR sensors. These advancements are expected to further enhance the capabilities and widespread adoption of NDIR sensors in methane detection applications.

Quantifying non-steady state natural gas leakage from the pipelines using an innovative sensor network and model for subsurface emissions - InSENSE

Detecting and quantifying subsurface leaks remains a challenge due to the complex nature and extent of belowground leak scenarios. To address these scenarios, monitoring and evaluating changes in gas leakage behavior over space and time are crucial for ensuring safe and efficient responses to known or potential gas leaks. This study demonstrates the capability of linking environmental and gas concentration data obtained using a low-cost, near real-time methane (CH4) detector network and an inverse gas migration model to capture and quantify non-steady state belowground natural gas (NG) leaks. The Estimating Surface Concentration Above Pipeline Emission (ESCAPE) model was modified to incorporate the impact of soil properties on gas migration. Field-scale controlled NG experiments with leakage rates ranging from 37 to 121 g/h indicate that elevated belowground near-surface (BNS) gas concentrations persist long before elevated surface concentrations are observed. On average, BNS CH4 concentrations were 20%–486% higher than surface CH4 concentrations within the monitoring radius of 4 m from the leak location. An increase in the BNS CH4 concentration was observed within 3 h as the leak rate increased from 37 to 89 g/h. However, due to the atmospheric fluctuations, any changes in surface CH4 concentrations could not be confirmed within this period. The plume area of the BNS CH4 extended approximately two times farther than that of the surface CH4 as the gas leak rate increased from 37 to 121 g/h. The estimated NG leak rates by the modified ESCAPE model agreed well with the experimental NG leak rates (m = 0.99 and R2 = 0.77), demonstrating that including soil characteristics and BNS CH4 measurements can advance estimations of non-steady NG leak rates in low and moderate NG leak rate scenarios. The CH4 detector network and model show potential as an innovative tool to improve operators' risk assessment and NG leakage response.

Evaluation of the applicability of a metal oxide semiconductor gas sensor for methane emissions from agriculture

This work investigated the potential of metal oxide semiconductor (MOS) gas sensors for environmental monitoring of methane. Calibrations were performed under controlled conditions in the lab, and under semi-controlled conditions in the field, using a modified head space chamber set-up. Concentrations up to ±300 ppm methane were tested. The relationship between sensor conductance and methane concentrations could be very well described using principles from adsorption theory. The adjustable parameters were background conductance G0, a sensitivity constant S and a non-ideality coefficient n, where n has a non-rational value between 0 and 1. Sensor behaviour was very different in dry air than in humid air, with the background conductance increasing approximately tenfold and sensitivity decreasing between 20 fold and 80 fold, while the non-ideality coefficient increased from ±0.4 to ±0.6. Nevertheless, at high methane concentrations comparable conductance values were recorded in dry and humid air. The standard deviation of predicted values was 1.6 μS.for the least well described dataset. Using the corresponding calibration curve, a detection limit of 11 ppm is calculated for humid ambient air. This values suggests that MOS sensor are adequately sensitive to be used for methane detection in an agricultural context.

Near-infrared wide-range dual-gas sensor system for simultaneous detection of methane and carbon monoxide in coal mine environment

To effectively monitor gas explosion and coal spontaneous combustion in coal mine, an intrinsically safe and explosion-proof dual-gas sensor system was developed for methane (CH4) and carbon monoxide (CO) measurement. A distributed feedback (DFB) laser was used to scan two adjacent absorption lines (CH4: 4284.51 cm−1, CO: 4285.01 cm−1). Two measurement ranges of 0–500 parts per million by volume (ppmv) and 0–300,000 ppmv for CH4 and 0–100 ppmv and 0–4000 ppmv for CO were realized using a ∼25 cm single-pass gas cell (SPGC) and a ∼25 m multi-pass gas cell (MPGC), respectively. A gas pre-treatment module was used to remove moisture and aerosols from gas samples. A temperature compensation algorithm was adopted to deal with ambient temperature change. Based on the Allan deviation analysis, the limit of detection (LoD) of CH4 is 0.19 ppmv and the LoD of CO is 0.12 ppmv, respectively, at an averaging time of 0.5 s. A continuous two-day CH4 measurement was performed to demonstrate the long-term stability and environmental monitoring capability. The sensor system was successfully deployed in coal mine gas analysis at Longjiapu, Changchun City, China.

Hotspot Detection and Estimation of Methane Emissions from Landfill Final Cover

The main objectives of this study were to identify methane hotspots through spatial distribution tests of the surface methane concentration above a landfill final cover and to investigate the effects of rainfall, atmospheric pressure, ground temperature, and ambient methane concentration on methane emissions. A portable laser methane detector was used to measure the spatial distribution of methane concentrations. The methane concentration distribution showed a distinct spatial variability. The maximum methane concentration reached 3225 ppm, while 73.0% of the methane concentration values were below 10.0 ppm. Several meteorological factors were found to be associated with the variation in methane emissions. Rainfall limited gas transport in the cover, resulting in more significant methane hotspots. Atmospheric pressure was negatively correlated with methane emission. The ambient methane concentration and methane flux had a significant positive linear correlation. Based on a linear correlation equation, the spatial distribution of methane concentrations in the landfill could be converted into a methane emission distribution. The estimated average value for methane emissions in the test area was approximately 4.3 g m−2 d−1. This study provides an experimental basis for locating methane hotspots and assessing methane emissions in landfill final covers, and proposes supplementary means for detecting geomembrane damage in landfill covers.

Quantitative hydrogen and methane gas sensing via implementing AI based spectral analysis of plasma discharge

In this report we explore the feasibility of a quantitative gas detection system concept based on alternations in spectral emissions of a radio frequency power generated plasma in presence of a target gas. We then proceed with training a deep learning residual network computer vison model with the spectral data obtained from the plasma to be able to perform regressive calculation of the target gas content in the plasma. We explore this concept with hydrogen and methane gas present in the plasma at know quantities to evaluate the applicability of the concept as hydrogen or methane detection system. We will demonstrate that the system is well capable of quantitatively detecting either of the gases efficiently while it is challenging to estimate hydrogen content in presence of methane.

Estimating the Below-Ground Leak Rate of a Natural Gas Pipeline Using Above-Ground Downwind Measurements: The ESCAPE-1 Model.

Natural gas (NG) leaks from below-ground pipelines pose safety, economic, and environmental hazards. Despite walking surveys using handheld methane (CH4) detectors to locate leaks, accurately triaging the severity of a leak remains challenging. It is currently unclear whether CH4 detectors used in walking surveys could be used to identify large leaks that require an immediate response. To explore this, we used above-ground downwind CH4 concentration measurements made during controlled emission experiments over a range of environmental conditions. These data were then used as the input to a novel modeling framework, the ESCAPE-1 model, to estimate the below-ground leak rates. Using 10-minute averaged CH4 mixing/meteorological data and filtering out wind speed < 2 m s-1/unstable atmospheric data, the ESCAPE-1 model estimates small leaks (0.2 kg CH4 h-1) and medium leaks (0.8 kg CH4 h-1) with a bias of -85%/+100% and -50%/+64%, respectively. Longer averaging (≥3 h) results in a 55% overestimation for small leaks and a 6% underestimation for medium leaks. These results suggest that as the wind speed increases or the atmosphere becomes more stable, the accuracy and precision of the leak rate calculated by the ESCAPE-1 model decrease. With an uncertainty of ±55%, our results show that CH4 mixing ratios measured using industry-standard detectors could be used to prioritize leak repairs.

Integrating a UAV System Based on Pixhawk with a Laser Methane Mini Detector to Study Methane Emissions

This article describes the process of integrating one of the most commonly used laser methane detectors, the Laser Methane mini (LMm), and a multi-rotor unmanned aerial vehicle (UAV) based on the Pixhawk flight controller to create an unmanned aerial system designed to detect methane leakages from the air. The integration is performed via the LaserHub+, a newly developed device which receives data from the laser methane detector, decodes it and transmits it to the flight controller with the protocol used by the ArduPilot platform for laser rangefinders. The user receives a single data array from the UAV flight controller that contains both the values of the methane concentrations measured by the detector, and the co-ordinates of the corresponding measurement points in three-dimensional space. The transmission of data from the UAV is carried out in real time. It is shown in this project that the proposed technical solution (the LaserHub+) has clear advantages over not only similar serial commercial solutions (e.g., the SkyHub complex by SPH Engineering) but also experimental developments described in the scientific literature. The main reason is that LaserHub+ does not require a deep customization of the methane detector or the placement of additional complex devices on board the UAV. Tests using it were carried out in aerial gas surveys of a number of municipal solid waste disposal sites in Russia. The device has a low cost and is easy for the end user to assemble, connect to the UAV and set up. The authors believe that LaserHub+ can be recommended as a mass solution for aerial surveys of methane sources. Information is provided on the approval of LaserHub+ for aerial gas surveys of a number of Russian municipal waste disposal facilities.

Multivariate analysis and digital twin modelling: Alternative approaches to evaluate molecular relaxation in photoacoustic spectroscopy

A comparative analysis of two different approaches developed to deal with molecular relaxation in photoacoustic spectroscopy is here reported. The first method employs a statistical analysis based on partial least squares regression, while the second method relies on the development of a digital twin of the photoacoustic sensor based on the theoretical modelling of the occurring relaxations. Methane detection within a gas matrix of synthetic air with variable humidity level is selected as case study. An interband cascade laser emitting at 3.345 µm is used to target methane absorption features. Two methane concentration ranges are explored targeting different absorptions, one in the order of part-per-million and one in the order of percent, while water vapor absolute concentration was varied from 0.3 % up to 2 %. The results achieved employing the detection techniques demonstrated the possibility to efficiently retrieve the target gas concentrations with accuracy > 95 % even in the case of strong influence of relaxation effects.

Carbon-bearing Molecules in a Possible Hycean Atmosphere

The search for habitable environments and biomarkers in exoplanetary atmospheres is the holy grail of exoplanet science. The detection of atmospheric signatures of habitable Earth-like exoplanets is challenging owing to their small planet–star size contrast and thin atmospheres with high mean molecular weight. Recently, a new class of habitable exoplanets, called Hycean worlds, has been proposed, defined as temperate ocean-covered worlds with H2-rich atmospheres. Their large sizes and extended atmospheres, compared to rocky planets of the same mass, make Hycean worlds significantly more accessible to atmospheric spectroscopy with JWST. Here we report a transmission spectrum of the candidate Hycean world K2-18 b, observed with the JWST NIRISS and NIRSpec instruments in the 0.9–5.2 μm range. The spectrum reveals strong detections of methane (CH4) and carbon dioxide (CO2) at 5σ and 3σ confidence, respectively, with high volume mixing ratios of ∼1% each in a H2-rich atmosphere. The abundant CH4 and CO2, along with the nondetection of ammonia (NH3), are consistent with chemical predictions for an ocean under a temperate H2-rich atmosphere on K2-18 b. The spectrum also suggests potential signs of dimethyl sulfide (DMS), which has been predicted to be an observable biomarker in Hycean worlds, motivating considerations of possible biological activity on the planet. The detection of CH4 resolves the long-standing missing methane problem for temperate exoplanets and the degeneracy in the atmospheric composition of K2-18 b from previous observations. We discuss possible implications of the findings, open questions, and future observations to explore this new regime in the search for life elsewhere.

Application of Semiconductor Metal Oxide in Chemiresistive Methane Gas Sensor: Recent Developments and Future Perspectives.

The application of semiconductor metal oxides in chemiresistive methane gas sensors has seen significant progress in recent years, driven by their promising sensitivity, miniaturization potential, and cost-effectiveness. This paper presents a comprehensive review of recent developments and future perspectives in this field. The main findings highlight the advancements in material science, sensor fabrication techniques, and integration methods that have led to enhanced methane-sensing capabilities. Notably, the incorporation of noble metal dopants, nanostructuring, and hybrid materials has significantly improved sensitivity and selectivity. Furthermore, innovative sensor fabrication techniques, such as thin-film deposition and screen printing, have enabled cost-effective and scalable production. The challenges and limitations facing metal oxide-based methane sensors were identified, including issues with sensitivity, selectivity, operating temperature, long-term stability, and response times. To address these challenges, advanced material science techniques were explored, leading to novel metal oxide materials with unique properties. Design improvements, such as integrated heating elements for precise temperature control, were investigated to enhance sensor stability. Additionally, data processing algorithms and machine learning methods were employed to improve selectivity and mitigate baseline drift. The recent developments in semiconductor metal oxide-based chemiresistive methane gas sensors show promising potential for practical applications. The improvements in sensitivity, selectivity, and stability achieved through material innovations and design modifications pave the way for real-world deployment. The integration of machine learning and data processing techniques further enhances the reliability and accuracy of methane detection. However, challenges remain, and future research should focus on overcoming the limitations to fully unlock the capabilities of these sensors. Green manufacturing practices should also be explored to align with increasing environmental consciousness. Overall, the advances in this field open up new opportunities for efficient methane monitoring, leak prevention, and environmental protection.