Methane Detection Research Articles

Comparative studies of metamorphic and strained InGaAs p-type semiconductor - intrinsic absorption layer - n-type semiconductor photodetector for low-temperature CH4 detection

This paper proposes two wavelength-extended InGaAs p-type semiconductor - intrinsic absorption layer - n-type semiconductor structures to achieve methane detection at low temperatures. The responsivity of the strained and metamorphic structures is 0.98 and 1.09 A/W at 20 °C. The strained structure detector can achieve a response level of 0.7 A/W in low-temperature environments above −10 °C. The metamorphic structure shows a responsivity not less than 0.8 A/W within the temperature span of −40 to 85 °C. The dark current of both devices is at the nA level at −10 V. The saturation optical power of the strained structure and the metamorphic structure is 13 and 17 mW at −0.5 V, respectively. The cut-off wavelength is 1690 and 1730 nm for the strained structure and the metamorphic structure. These two structures tested by x-ray diffraction have high crystalline quality. The strained structure has a root mean square roughness of 0.13 nm, while the metamorphic structure's roughness is 0.39 nm. These results suggest that both strained and metamorphic structures have promising applications in low-temperature probes.

Mid‐Infrared Sensing Using a Hollow–Core Fiber in a Nonlinear Interferometer

AbstractIncreasing the interaction path length is a well‐known method for enhancing the sensitivity of the optical detection system. Hollow–core fibers (HCFs) represent a viable alternative to the traditional multi‐path cells offering low optical losses and strong confinement of the optical field. Here, the incorporation of an Antiresonant Hollow–core Fiber (AR‐HCF) section into a nonlinear interferometer, where the AR‐HCF section serves as a gas‐sensing cell operating in the IR range is presented. By exploiting the effect of nonlinear interference, the detection is brought into the more operation‐friendly visible range. The detection of methane (CH4) gas at mid‐IR wavelengths within a half‐meter section of AR‐HCF, with an estimated concentration accuracy of 200 ppm·m is demonstrated. These results represent the combination of two research fields within a single instrument and pave the way for further advancement of quantum‐inspired gas sensing techniques.

Influence of alumina on the performance of Ag/ZnO based catalysts for carbon dioxide hydrogenation

We study silver-zinc oxide type catalysts with and without the addition of alumina and perform structural analysis and activity tests for the hydrogenation of carbon dioxide. Adding alumina has a dispersing effect on the zinc oxide without structurally altering the silver phase. An alumina-surface enriched ZnO/Al2O3 phase is observed with an increased surface reducibility. Ag/ZnO has a high selectivity towards carbon monoxide (63 ± 12 %) and methane (24 ± 3 %) and low selectivity towards methanol (13 ± 0.5 %). Operando infrared (SSITKA-FTIR) and mass spectrometric product detection indicate methane formation via an adsorbed carbon monoxide (COads) intermediate. The selectivity changes gradually with increasing alumina content, up to 80 ± 3 % toward methanol, and 20 ± 4 % carbon monoxide without methane detection, combined with a tripling of the space time yield to 0.65 ± 0.02mmolMeOH*gcat-1*h−1 at 250 °C and 30 bar. Kinetic analysis suggests that the selectivity change originates from hindering the CO-pathway, while the formate pathway leading to methanol remains active.

Development of Low-Stress Double-Pass Filter Device for Methane and Ethane Flammable Gas Detection System

To meet the needs of industrial and environmental monitoring of methane and ethane flammable gases, a low-stress filter device for detecting methane and ethane simultaneously is developed based on the absorption spectra of methane and ethane and the thin-film design theory. In order to improve the sensitivity of the detection system and reduce the influence of environmental noise, the information of methane and ethane gas is extracted simultaneously by using a filter with double bandpass and wide cut-off in the middle-infrared and far-infrared bands. The transmittance of the filter is 91.3%, 93.1%, and 93.02% at the wavelengths of 3.31 μm, 6.71 μm, and 7.67 μm, respectively. The cut-off depth in the cut-off interval is above OD3, which meets the system’s requirements.

A Comprehensive Review of State-of-the-art Optical Methods for Methane Gas Detection

Methane (CH<sub>₄</sub>), a potent greenhouse gas, significantly contributes to climate change and global warming. Its impact over 100 years surpasses carbon dioxide (CO<sub>₂</sub>) by 28 times. Addressing methane emissions, particularly from oil and gas production activities such as transmission pipelines, is imperative. One promising avenue is the development of reliable sensors to detect and mitigate methane leaks and prevent hazardous issues. Optical-based methods present notable advantages, including versatility and remote operation, making them pivotal in this endeavor. This review article provides a concise overview of optical-based methane identification technologies, encompassing sensing materials, absorption spectra, operational mechanisms, and recent advancements. Potential perspectives are explored, and inferences from this assessment are also derived. Emphasizing the significance of optical fiber-based methane detection methods, the authors advocate for further research to support ongoing efforts and foster innovation in this critical area.

Real-time detection of urban gas pipeline leakage based on machine learning of IoT time-series data

China is advancing urban gas pipeline safety through the strategic deployment of methane detectors. This study introduces a systematic approach for identifying underground gas leaks, and overcoming challenges such as biogas interference and environmental factors. Abnormal methane sequences, defined by expert experience, undergo data preprocessing, including cleansing, compressing, and extracting rising segments. Feature engineering is performed on these rising segments across three dimensions: temporal, volatility, and temperature features. Different classification algorithms, including K-nearest neighbor, decision tree, and support vector machine, are employed for gas leakage recognition. The decision tree proves most effective, achieving a 92.86% recall rate for methane leakage segments. Additionally, feature importance ranking from the decision tree model highlights the maximum value of preprocessed methane concentration time series and the fitting slope of the rising segment as crucial features for methane leakage detection.

Methane Detection using Optical Gas Imaging: Passive Infrared Technology

Methane Detection using Optical Gas Imaging: Passive Infrared Technology

Detection of Exhaled Methane in Gastrointestinal Disease Population Based on TDLAS

Detection of Exhaled Methane in Gastrointestinal Disease Population Based on TDLAS

Preparation and regulation of high-strength and high-permeability porous ceramic/PDMS composite membranes for gas-liquid separation

Methane detection is crucial in identifying combustible ice within the energy sector. The gas–liquid separation membrane, a key component of methane sensors, plays a critical role in effectively separating methane and water. It significantly enhances the efficiency and stability of detecting methane concentration in situ. However, the related permeability and mechanical property present conflicting requirements in order to achieve optimal membrane performance. This work proposes an investigation into the mass transfer performance of a high-strength composite membrane, utilizing PDMS as the functional layer and porous ceramics as the supporting layer. By adjusting the pore distribution of the porous ceramics, particularly through the use of spherical graphite as a porogen, the study examines the impact of spherical graphite (SG) on the support layer’s morphology and its ability to regulate pore structure. Results indicate that incorporating spherical graphite with a porogen content of 10 wt% and a particle size of 25 μm enhances connections between ceramic grains, resulting in a flexural strength of 56.36 MPa and a permeability coefficient of 1861.57 barrer. This configuration demonstrates good waterproof and breathable performance. Overall, this research presents a novel approach for fabricating methane-water separation membranes, offering valuable insights for underwater methane detection efforts.

Detection of Harmful H2S Concentration Range, Health Classification, and Lifespan Prediction of CH4 Sensor Arrays in Marine Environments

Underwater methane (CH4) detection technology is of great significance to the leakage monitoring and location of marine natural gas transportation pipelines, the exploration of submarine hydrothermal activity, and the monitoring of submarine volcanic activity. In order to improve the safety of underwater CH4 detection mission, it is necessary to study the effect of hydrogen sulfide (H2S) in leaking CH4 gas on sensor performance and harmful influence, so as to evaluate the health status and life prediction of underwater CH4 sensor arrays. In the process of detecting CH4, the accuracy decreases when H2S is found in the ocean water. In this study, we proposed an explainable sorted-sparse (ESS) transformer model for concentration interval detection under industrial conditions. The time complexity was decreased to O (n logn) using an explainable sorted-sparse block. Additionally, we proposed the Ocean X generative pre-trained transformer (GPT) model to achieve the online monitoring of the health of the sensors. The ESS transformer model was embedded in the Ocean X GPT model. When the program satisfied the special instructions, it would jump between models, and the online-monitoring question-answering session would be completed. The accuracy of the online monitoring of system health is equal to that of the ESS transformer model. This Ocean-X-generated model can provide a lot of expert information about sensor array failures and electronic noses by text and speech alone. This model had an accuracy of 0.99, which was superior to related models, including transformer encoder (0.98) and convolutional neural networks (CNN) + support vector machine (SVM) (0.97). The Ocean X GPT model for offline question-and-answer tasks had a high mean accuracy (0.99), which was superior to the related models, including long short-term memory–auto encoder (LSTM–AE) (0.96) and GPT decoder (0.98).

Performance Characterization of an Illumination-Based Low-Cost Multispectral Camera.

Spectral imaging has many applications, from methane detection using satellites to disease detection on crops. However, spectral cameras remain a costly solution ranging from 10 thousand to 100 thousand euros for the hardware alone. Here, we present a low-cost multispectral camera (LC-MSC) with 64 LEDs in eight different colors and a monochrome camera with a hardware cost of 340 euros. Our prototype reproduces spectra accurately when compared to a reference spectrometer to within the spectral width of the LEDs used and the ±1σ variation over the surface of ceramic reference tiles. The mean absolute difference in reflectance is an overestimate of 0.03 for the LC-MSC as compared to a spectrometer, due to the spectral shape of the tiles. In environmental light levels of 0.5 W m-2 (bright artificial indoor lighting) our approach shows an increase in noise, but still faithfully reproduces discrete reflectance spectra over 400 nm-1000 nm. Our approach is limited in its application by LED bandwidth and availability of specific LED wavelengths. However, unlike with conventional spectral cameras, the pixel pitch of the camera itself is not limited, providing higher image resolution than typical high-end multi- and hyperspectral cameras. For sample conditions where LED illumination bands provide suitable spectral information, our LC-MSC is an interesting low-cost alternative approach to spectral imaging.

A Non-Source Optical Fiber Sensor for Multi-Point Methane Detection.

Fast, accurate, real-time measurement of gas concentration is an important task for preventing coal mining disasters. In order to develop an accurate monitoring method for methane gas concentration at different locations in a mine environment, a non-source optical fiber sensor for multi-point methane detection has been developed in this paper. A 16-channel fiber splitter and a multi-channel time-sharing acquisition module are employed within the sensor, enabling simultaneous detection of methane gas at 16 points by a host. Furthermore, the methane sensors are connected to the monitoring host via an all-optical method, achieving non-source and long-range detection of methane. To assess the performance of the methane gas sensor, experiments were conducted to evaluate its detection range, response time, and stability. The results indicated that the average detection error was approximately 1.84% across the full range, and the response time did not exceed 10 s. The minimum detection limit of the sensor, as determined by the 1σ criteria, was obtained as 58.42 ppm. Additionally, the concentrations of methane gas measured at varying distances (1 km, 2 km, 5 km) were found to be essentially consistent over an extended period. These results suggest that the development of this non-source optical fiber sensor holds significant potential for providing a method for mine environment, multi-point online methane gas detection.

Fumarate reductase drives methane emissions in the genus Oryza through differential regulation of the rhizospheric ecosystem

The emergence of waterlogged Oryza species ∼15Mya (million years ago) supplied an anoxic warm bed for methane-producing microorganisms, and methane emissions have hence accompanied the entire evolutionary history of the genus Oryza. However, to date no study has addressed how methane emission has been altered during Oryza evolution. In this paper we used a diverse collection of wild and cultivated Oryza species to study the relation between Oryza evolution and methane emissions. Phylogenetic analyses and methane detection identified a co-evolutionary pattern between Oryza and methane emissions, mediated by the diversity of the rhizospheric ecosystems arising from different oxygen levels. Fumarate was identified as an oxygen substitute used to retain the electron transport/energy production in the anoxic rice root, and the contribution of fumarate reductase to Oryza evolution and methane emissions has also been assessed. We confirmed the between-species patterns using genetic dissection of the traits in a cross between a low and high methane-emitting species. Our findings provide novel insights on the evolutionary processes of rice paddy methane emissions: the evolution of wild rice produces different Oryza species with divergent rhizospheric ecosystem attributing to the different oxygen levels and fumarate reductase activities, methane emissions are comprehensively assessed by the rhizospheric environment of diversity Oryza species and result in a co-evolution pattern.

Mid-infrared methane standoff sensor using a frequency channel attention based convolutional neural network filter

Highly sensitive standoff methane detection is vital for atmospheric science, environmental protection, and production safety. We develop a mid-infrared methane standoff sensor using a cooperative target based on tunable diode laser absorption spectroscopy (TDLAS). To enhance sensor sensitivity, we propose a one-dimensional frequency channel attention-based convolutional neural network (1D-FCACNN) filter, which can effectively denoise the methane absorbance signals and its second harmonic signals. The filter model is adequately trained on a simulated spectrum dataset, which we constructed based on data augmentation. In comparison with reported filtering algorithms, the proposed filter shows the best performance in both measuring modes and evaluation metrics. Real-time measurements show that the measuring accuracy and limit of detection (LOD) of the proposed sensor reach a minimum of 30.40 ppb and 5.04 ppb over a 10-meter optical range, a significant improvement compared to previous reports of methane standoff sensors. The proposed methane standoff sensor proves the feasibility of enhancing the performance of TDLAS gas sensors with the attention mechanism, bringing a new option for high-sensitivity measurements of methane and other atmospheric trace gases.

Gas-sensing performance of WS2/WO3 binary thin films for methane and hydrogen detection

Gas-sensing performance of WS2/WO3 binary thin films for methane and hydrogen detection

Harmonic phase-sensitive detection for quartz-enhanced photoacoustic-thermoelastic spectroscopy

Quartz tuning fork (QTF)-based techniques of photoacoustic spectroscopy and thermoelastic spectroscopy play a significant role in trace gas sensing due to unique high sensitivity and compactness. However, the stability of both techniques remains plagued by the inevitable and unpredictable laser power variation and demodulation phase variation. Herein, we investigate the phase change of a QTF when integrating both techniques for enhanced gas sensing. By demonstrating harmonic phase-sensitive methane detection as an example, we achieve stable gas measurement at varying laser power (2.4–9.4 mW) and varying demodulation phase (−90–90°). Besides, this method shows more tolerance to resonant frequency drift, contributing to a small signal fluctuation of ≤ 6.4 % over a wide modulation range (>10 times of the QTF bandwidth). The realization of harmonic-phase detection allows strengthening the stability of QTF-based sensors in a simple manner, especially when stable parameters, such as laser power, demodulation phase, even resonant frequency, cannot always be maintained.

Application of Artificial Intelligence to the Alert of Explosions in Colombian Underground Mines

The use of Artificial Intelligence (AI), particularly of Artificial Neural Networks (ANN), in alerting possible scenarios of methane explosions in Colombian underground mines is illustrated by the analysis of an explosion that killed twelve miners. A combination of geological analysis, a detailed characterization of samples of coal dust and scene evidence, and an analysis with physical modeling tools supported the hypothesis of the existence of an initial methane explosion ignited by an unprotected tool that was followed by a coal dust explosion. The fact that one victim had a portable methane detector at the moment of the methane explosion suggested that the ubiquitous use of these systems in Colombian mines could be used to alert regulatory agencies of a possible methane explosion. This fact was illustrated with the generation of a database of possible readouts of methane concentration based on the recreation of the mine atmosphere before the explosion with Computational Fluid Dynamics (CFD). This database was used to train and test an ANN that included an input layer with two nodes, two hidden layers, each with eight nodes, and an output layer with one node. The inner layers applied a rectified linear unit activation function and the output layer a Sigmoid function. The performance of the ANN algorithm was considered acceptable as it correctly predicted the need for an explosion alert in 971.9 per thousand cases and illustrated how AI can process data that is currently discarded but that can be of importance to alert about methane explosions.

A technique for detecting hydrogen and methane using refractive index sensitivity

Hydrogen and methane, as lightweight, high-energy fuels, occupy an important position in energy utilization and industrial production, and have broad application prospects. The safe extraction and production of new energy gases, as well as the safe utilization of hydrogen and methane is increasingly essential. The problem of detecting the type and concentration of hydrogen and methane is very important. This paper proposed a multi-peak high performance metamaterial refractive index sensor to fulfill the research gap for hydrogen or methane detection. We made unique designs on the patterns of the metasurface and made innovative combinations of materials. Through discussing different shapes and geometrical parameters, we get an optimal structure with a perfect absorption state, high sensitivity, and polarization independence. In the near-infrared spectral range spanning from 1700 nm to 2600 nm, resonance peaks were observed in the absorption spectrum at wavelengths of 1738.27 nm, 1843.17 nm, and 2368.13 nm. These peaks exhibited absorption rates respectively are 99.28 %, 89.71 %, and 99.69 %. The high sensitivity of the sensor under different environmental refractive indices was up to 715.43 nm/RIU. This leads to more precise and reliable identification of hydrogen and methane. The polarization dependence of our structure ensures consistent results under light illumination from various angles. This feature enhances the convenience of using it in both production and daily life, with fewer constraints and more accurate outcomes. The possible applications of this study lie in detecting the presence of hydrogen and methane gases, the exploration and extraction of new energy gases, as well as to monitoring and alarm systems for hydrogen and methane leaks. Moreover, it can serve as a validation system for the results of hydrogen and methane production processes. These applications are of significant importance for environmental protection and process safety.

Sustainability and environmental impact in the LNG value chain: Current trends and future opportunities

The liquefied natural gas (LNG) industry plays a crucial role in the global energy landscape, offering a cleaner alternative to traditional fossil fuels. However, the LNG value chain presents environmental challenges that must be addressed to ensure long-term sustainability. This paper examines current trends and future opportunities for enhancing sustainability and reducing environmental impact across the LNG value chain. The LNG value chain comprises several stages, including natural gas extraction, liquefaction, transportation, regasification, and distribution. Each stage presents unique sustainability challenges, such as methane emissions during extraction and transportation, energy-intensive liquefaction processes, and the carbon footprint of regasification and distribution. Current trends in the LNG industry focus on mitigating these challenges through various strategies. These include the adoption of advanced technologies for methane detection and reduction, the use of renewable energy sources for liquefaction, and the implementation of efficient regasification and distribution practices. Additionally, there is a growing emphasis on stakeholder engagement, transparency, and reporting to enhance sustainability performance across the value chain. Future opportunities for improving sustainability in the LNG value chain lie in the continued development and deployment of innovative technologies. These include carbon capture and storage (CCS) technologies to reduce emissions, the use of renewable natural gas (RNG) as a feedstock, and the integration of LNG with renewable energy sources to create hybrid energy systems. Addressing sustainability and environmental impact in the LNG value chain requires collaboration among industry stakeholders, governments, and regulatory bodies. By implementing best practices, embracing innovation, and prioritizing sustainability, the LNG industry can continue to play a vital role in the transition to a cleaner and more sustainable energy future.

Highly sensitive detection of methane based on LITES and H-LITES techniques

In this paper, a highly sensitive methane (CH4) sensor utilizing light-induced thermoelastic spectroscopy (LITES) and heterodyne light-induced thermoelastic spectroscopy (H-LITES) techniques was developed for the first time. The sensor employed a quartz tuning fork (QTF) with a resonance frequency (f0) of 32762.80 Hz and a quality factor (Q) of 12009.824 as the detector. A continuous wavelength distributed feedback (CW-DFB) diode laser emitting at 1650.96 nm with a maximum output power of 30 mW was used for strong exciting of the target gas. Detailed testing was conducted to assess the sensor’s performance. The results revealed that both CH4-LITES and CH4-H-LITES sensors had excellent linearity concentration response. The minimum detection limit (MDL) for these two sensors was found to be 5.402 ppm and 9.742 ppm, respectively. Compared to CH4-LITES sensor, CH4-H-LITES had the ability to acquire the heterodyne signal and identify the f0 of QTF in just 2 s, showing a fast response capability.