Methane Sensor Research Articles

In-Situ Study Methods Used in the Discovery of Sites of Modern Hydrothermal Ore Formation on the Mid-Atlantic Ridge

This paper presents in situ methods used in the search for areas of modern hydrothermal activity, as well as the results of their long-term use during cruises within the Russian exploration area of the Mid-Atlantic Ridge (MAR). In this study, the following methods were used: CTD-sounding, methane sounding, teleprofiling and measurements with Eh, pS, pNa, pH and EF sensors. During profiling by towed complexes (RIFT, MAK-1M), various geophysical and geochemical anomalies near high-temperature, low-temperature and inactive fields were detected. Geophysical anomalies are more distinct when profiling near the bottom, and geochemical anomalies are located at a distance from the bottom (~150–200 m). Direct signs of high-temperature discharging (black smoker′s smoke, hydrothermal buildings) and indirect signs of low-temperature discharging (lithified carbonate sediments and accumulations of endemic hydrothermal fauna) were detected during teleprofiling. We have described 6 types of complex geophysical anomalies determined by CTD-sounding that allow the detection of plumes at different stages of formation and from different sources. The use of a methane sensor during sounding makes it more likely to identify a low-temperature discharge. Methane has a highly variable nature of distribution—over distances from the first hundreds of meters to tens of kilometers and a seabed height of ~50–500 m. The use of these methods together makes it more possible to detect low- and high-temperature hydrothermal discharges within mid-oceanic ridges (MOR).

Use of metal oxide semiconductor sensors to measure methane in aquatic ecosystems in the presence of cross‐interfering compounds

AbstractMonitoring dissolved methane in aquatic ecosystems contributes significantly to advancing our understanding of the carbon cycle in these habitats and capturing their impact on methane emissions. Low‐cost metal oxide semiconductors (MOS) gas sensors are becoming an increasingly attractive tool to perform such measurements, especially at the air–water interface. However, the performance of MOS sensors in aquatic environmental sciences has come under scrutiny because of their cross‐sensitivity to temperature, moisture, and sulfide interference. In this study, we evaluated the performance and limitations of a MOS methane sensor when measuring dissolved methane in waters. A MOS sensor was encapsulated in a hydrophobic extended polytetrafluoroethylene membrane to impede contact with water but allow gas perfusion. Therefore, the membrane enabled us to submerge the sensor in water and overcome cross‐sensitivity to humidity. A simple portable, low‐energy, flow‐through cell system was assembled that included an encapsulated MOS sensor and a temperature sensor. Waters (with or without methane) were injected into the flow cell at a constant rate by a peristaltic pump. The signals from the two sensors were recorded continuously with a cost‐efficient microcontroller. Tests specifically focused on the effect of water temperature and sulfide interference on sensor performance. Our experiments revealed that the lower limit of the sensor was in the range of 0.1–0.2 μmol L−1 and that it provided a stable response at water temperatures in the range of 18.5–28°C. Dissolved sulfide at a concentration of 0.4 mmol L−1 or higher interfered with the sensor response, especially at low methane concentrations (0.5 μmol L−1 or lower). However, we show that if dissolved sulfide is monitored, its interference can be alleviated.

BombNose: A Multiple Bomb-Related Gas Prediction Model Using Machine Learning with Electronic Nose Sensor Substitution Technique

The safety and security of an individual is important in our society. Bombing attacks can cause significant destruction and death. Energy efficient and compact bomb removal robots are challenging to develop because these typically involved a large array of sensors individually acquiring gas data. This study addresses this challenge by developing a multiple bomb-related gas prediction model using machine learning and the electronic nose sensor substitution technique. Three models can predict gasses such as ammonia, ethanol, and isobutylene using only carbon monoxide, toluene, and methane sensors. The feedforward artificial neural network (FFNN) with three hidden layers was optimized for the regression of each target gas. Consequently, ammonia, ethanol, and isobutylene predictions achieved R2 values of 1, 1, and 1 as well as MSE values of 0.35696, 0.052995, and 0.0022953, respectively. This study demonstrates that the sensor substitution model (BombNose) is highly reliable and appropriately sensitive in the field of bomb detection.

Characterization of inexpensive metal oxide sensor performance for trace methane detection

Abstract. Methane, a major contributor to climate change, is emitted by a variety of natural and anthropogenic sources. Commercially available lab-grade instruments for sensing trace methane are expensive, and previous efforts to develop inexpensive, field-deployable trace methane sensors have had mixed results. Industrial and commercial metal oxide (MOx) methane sensors, which are intended for leak detection and safety monitoring, can potentially be repurposed and adapted for low-concentration sensing. As an initial step towards developing a low-cost sensing system, we characterize the performance of five off-the-shelf MOx sensors for 2–10 ppm methane detection in a laboratory setting (Figaro Engineering TGS2600, TGS2602, TGS2611-C00, TGS2611-E00, and Henan Hanwei Electronics MQ4). We identify TGS2611-C00, TGS2611-E00, and MQ4 as promising for trace methane sensing but show that variations in ambient humidity and temperature pose a challenge for the sensors in this application.

Highly sensitive dual-core photonic quasicrystal fiber methane sensor based on surface plasmon resonance.

A highly sensitive dual-core photonic quasicrystal fiber methane sensor based on surface plasmon resonance is designed and analyzed. In this sensor, cryptophane E is doped with polysiloxane and Ag and used as the sensitive film and plasma medium, respectively, for sensitive detection of methane. The influence of the structural parameters on the sensor properties is analyzed by the finite element method. The optimized dual-quasi-D-shape structure has excellent methane-sensing properties such as maximum and average wavelength sensitivities of 14 and 10.98 nm/%, respectively, in the methane concentration range of 0%-3.5%. The sensitivity is better than that of similar sensors reported previously.

Ba-Modified ZnO Nanorods Loaded with Palladium for Highly Sensitive and Rapid Detection of Methane at Low Temperatures

Exploring novel sensing materials to rapidly identify CH4 at low temperatures is crucial for various practical applications. Herein, a novel ZnO-xBa/Pd with Ba of cocatalyst loading from 0 to 2.0 wt% was facilely prepared using a two-step impregnation method to improve the sensitivity of the CH4 gas sensor. The microstructure, chemical states of the elements, and surface properties of ZnO-Ba/Pd were characterized, and the gas-sensitive performance of ZnO-Ba/Pd sensors was investigated. Compared to methane sensors based on other inorganic and organic material sensors, the sensor based on ZnO-1.0Ba/Pd exhibited a faster response/recovery time (1.4 s/8.3 s) and higher response (368.2%) for 5000 ppm CH4 at a lower temperature (170 °C). Moreover, the ZnO-1.0Ba/Pd sensor exhibited full reversibility and long-term stability, as well as excellent selectivity at 170 °C. The excellent performance of the ZnO-Ba/Pd sensor was attributed to the electron donation by Ba, which increases the electron density around Pd, thus enhancing the catalytic activity of Pd and promoting oxygen adsorption on the ZnO surface. The present work provides a method for the rational design and synthesis of sensitive materials in practical CH4 detection.

On-chip mid-infrared silicon-on-insulator waveguide methane sensor using two measurement schemes at 3.291μm.

Portable or even on-chip detection of methane (CH4) is significant for environmental protection and production safety. However, optical sensing systems are usually based on discrete optical elements, which makes them unsuitable for the occasions with high portability requirement. In this work, we report on-chip silicon-on-insulator (SOI) waveguide CH4 sensors at 3.291 μm based on two measurement schemes including direct absorption spectroscopy (DAS) and wavelength modulation spectroscopy (WMS). In order to suppress noise, Kalman filter was adopted in signal processing. By optimizing the waveguide cross-section structure, an etch depth of 220 nm was selected with an experimentally high power confinement factor (PCF) of 23% and a low loss of only 0.71 dB/cm. A limit of detection (LoD) of 155 parts-per-million (ppm) by DAS and 78 ppm by WMS at an averaging time of 0.2 s were obtained for a 2 cm-long waveguide sensor. Compared to the chalcogenide (ChG) waveguide CH4 sensors at the same wavelength, the reported sensor reveals the minimum waveguide loss and the lowest LoD. Therefore the SOI waveguide sensor has the potential of on-chip gas sensing in the mid-infrared (MIR) waveband.

Response time correction of slow-response sensor data by deconvolution of the growth-law equation

Abstract. Accurate high-resolution measurements are essential to improve our understanding of environmental processes. Several chemical sensors relying on membrane separation extraction techniques have slow response times due to a dependence on equilibrium partitioning across the membrane separating the measured medium (i.e., a measuring chamber) and the medium of interest (i.e., a solvent). We present a new technique for deconvolving slow-sensor-response signals using statistical inverse theory; applying a weighted linear least-squares estimator with the growth law as a measurement model. The solution is regularized using model sparsity, assuming changes in the measured quantity occur with a certain time step, which can be selected based on domain-specific knowledge or L-curve analysis. The advantage of this method is that it (1) models error propagation, providing an explicit uncertainty estimate of the response-time-corrected signal; (2) enables evaluation of the solution self consistency; and (3) only requires instrument accuracy, response time, and data as input parameters. Functionality of the technique is demonstrated using simulated, laboratory, and field measurements. In the field experiment, the coefficient of determination (R2) of a slow-response methane sensor in comparison with an alternative fast-response sensor significantly improved from 0.18 to 0.91 after signal deconvolution. This shows how the proposed method can open up a considerably wider set of applications for sensors and methods suffering from slow response times due to a reliance on the efficacy of diffusion processes.

Research on Methane Measurement and Interference Factors in Coal Mines.

The detection of methane has always been an important part of coal mine safety. In order to improve the methane measurement accuracy in coal mines and to determine the influence of environmental interference factors on the measurement results, we designed a spherical, experimental chamber simulating the on-site environment of an underground coal mine containing methane, in which various environmental interference factors can be superimposed. The simulation chamber can generate a uniform and controllable dust environment, a controllable methane environment with concentrations below that which would trigger an alarm, controllable humidity, and environments characterized by other interference factors. Based on computational simulations of the experimental chamber with varying dust-particle-concentration distributions using a single particle size, an optimal design for the chamber has been realized in terms of the rapid mixing of dust and the flow field. Finally, we constructed an underground methane concentration measurement system for coal mines and assessed the influences of different dust concentrations and relative humidity values on the performance of methane measurements, providing a means for improving the measurement accuracy of underground coal mine, spectral, absorption-type methane sensors.

Grain Storage Management at Distribution

India is an Agriculture country where 70% of the population depends on farming, the storage of grains plays a crucial role in national economy. During the grain storage, temperature, humidity and carbon dioxide and methane concentrations are important atmospheric factors that can affects the quality of the stored grain inside the go-downs and warehouses. The traditional methods are limited to simply testing the temperature and humidity conditions which are relatively backward as the other factors have to be checked independently for contributing to their effective storage. The approach of grain storage system at real-time designed by using some sensors like, DHT11, CO2 sensor, Methane sensor HC05 and Bluetooth module. These experimental results shows that the grain storage management system proposed, helps to check the nutrition levels of the grains that are being stored. This improves the rate of distribution of the grains at governmental distribution centre. The Grain storage management system gives the effective solution over the problem while distributing the grains. Hence at the end of this project the issues regarding distribution are eliminated at certain extent. Ultimately, the food availability to the people in drastic conditions and in case of natural calamities is the moto of government and this project will surely help to fulfil that moto with less manual efforts and with more efficiency.

D-Shaped Tellurite Photonic Crystal Fiber Hydrogen and Methane Sensor Based on Four-Wave Mixing With SPR Effect

A new D-shaped tellurite photonic crystal fiber sensor based on the four-wave mixing (FWM) effect with the surface plasmon resonance (SPR) effect is designed and optimized. The substrate of the D-shaped photonic crystal fiber (D-PCF) is tellurite glass, and the polished surface is plated with the gold film and hydrogen gas-sensitive film. An air hole of the inner cladding, which is plated with the gold film and methane gas-sensitive film, is selected as the second sensing channel to simultaneously measure the concentration of hydrogen and methane. Based on the four-wave mixing, the wavelength shifts of the Stokes and anti-Stokes spectra resulting from the variation of the gas concentration can be used to accurately detect the concentrations of methane and hydrogen. Meanwhile, it is found that the SPR effect can increase the wavelength shifts, which means the sensitivity of methane and hydrogen augment. After parameter optimization, the maximum sensitivities of methane and hydrogen are 4.03 nm/% and −14.19 nm/%, respectively. Both the linearities are up to 99.9%. The resolution of methane is 1.25×10−2% and hydrogen is 7.14×10−3%. Moreover, the fiber length of this sensor is only 20 mm, which is conducive to the construction of a compact or ultra-compact embedded FWM fiber sensor.

Investigation and optimization of PDMS/PES membrane on morphology and performance for methane-water separation

Methane detection is important for exploring combustible ice in energy field. As the core component of the methane sensor, gas-liquid separation membranes play a crucial role in separating methane from water, which can effectively enhance the timeliness and stability of methane detection. Moreover, the correlative permeability and mechanical properties of are contradictory requirements for achieving optimum performance of membranes. Herein, a methane-water separation membrane was developed and optimized by adjusting the PEG as porogen based on PDMS and PES as functional and support layers, respectively. PDMS/PES composite membrane performed an excellent methane permeability and tensile strength. Particularly, the PEG was studied as porogen to affect the morphology and adjust pore structure of the PES layer. Considering the trade-off of methane permeability and mechanical properties, the results indicate the appropriate content of porogen is 4 wt% PEG. This work optimizes a new approach to prepare methane-water separation membrane and give a significant insight to develop methane detection for exploring new energy in seabed.

Integrated coaxial graphene-based yarn with multidimensional architecture for self-powered photoelectrochemical methane sensor

Effective and reliable methane detection is essential and critical to environment protection, and life and property safety. However, how to enable a sensing platform for methane monitoring with high sensitivity and stability, and meanwhile with excellent structural flexibility and ease of integration remains a significant challenge. Herein, an integrated coaxial yarn-shaped self-powered photoelectrochemical methane sensor was successfully designed and constructed by employing wet-spun graphene (G) fiber as the inner electrode, G-based mixed-dimensional materials hybridization as the outer electrode, and a polymer gel coated in-between as the electrolyte separator. The high surface roughness and significant transmittance of obtained device enabled effective adsorption capacity and essential light penetration. A continuous and thin gel electrolyte interlayer in the sensor and single-walled carbon nanotube (SWNT)-bridged interconnected conductive network in a multidimensional hybrid facilitated fast ion/electron transport. Ascribed to above structural merits, the resulting sensor delivered a wide linear range from 0.05% to 0.47% and a low detection limit of 0.02%, while maintaining rapid response (0.3 s), outstanding selectivity and perfect working performance during bending. This work provides useful guidelines for designing and fabricating photoelectrochemical sensors toward highly sensitive gas detection.

Theoretical and experimental investigation of on-chip mid-infrared chalcogenide waveguide CH4 sensor based on wavelength modulation spectroscopy

Compared with direct absorption spectroscopy (DAS), wavelength modulation spectroscopy (WMS) with good ability of suppressing noise is rarely used in waveguide sensor. The influence of waveguide parameters on WMS sensing performance is required to clarify in both theory and experiment. Three mid-infrared waveguide methane (CH4) sensors (trapezoid, rectangular, suspended) were proposed and optimized for WMS simulation. Theoretical formulation and the experimentally derived sensing noise were used to numerically evaluate the key figure of merit more accurately. Simulation results were presented to show the influence of waveguide parameters on sensing performance. Two chalcogenide on magnesium fluoride (ChG-on-MgF2) trapezoid waveguide CH4 sensors were fabricated using lift-off method, and CH4 measurement was carried out using WMS. The waveguide loss was measured to be 1.52 dB/cm, leading to an optimal sensor length of 2.7 cm. The limit of detection (LoD) of CH4 for the 1cm- and 2cm-long waveguide sensors are 0.68% and 0.17% with an averaging time of 0.2 s. The experimentally achieved second harmonic (2f) signal amplitude and LoD show good agreement with the theoretical results, verifying the feasibility of the design and analysis model of the WMS-based waveguide sensor. Due to the use of WMS and a reduction of waveguide loss from 3.6 to 1.52 dB/cm, the LoD of the 2cm-long ChG-on-MgF2 sensor is 24 times lower than our previously reported 2cm-long ChG-on-SiO2 sensor based on DAS. This work provides a systematic guidance for the design of waveguide gas sensor based on WMS and contributes to improving the sensitivity of on-chip gas sensing.

Simulated Methane Emission Detection Capabilities of Continuous Monitoring Networks in an Oil and Gas Production Region

Simulations of the atmospheric dispersion of methane emissions were created for a region containing 26 oil and gas production sites in the Permian Basin in Texas. Virtual methane sensors were placed at 24 of the 26 sites, with at most 1 sensor per site. Continuous and intermittent emissions from each of the 26 oil and gas production sites, over 4 week-long meteorological episodes, representative of winter, spring, summer, and fall meteorology, were simulated. The trade-offs between numbers of sensors and precision of sensors required to reliably detect methane emissions of 1 to 10 kg/h were characterized. A total of 15 sensors, able to detect concentration enhancements of 1 ppm, were capable of identifying emissions at all 26 sites in all 4 week-long meteorological episodes, if emissions were continuous at a rate of 10 kg/h. More sensors or sensors with lower detection thresholds were required if emissions were intermittent or if emission rates were lower. The sensitivity of the required number of sensors to site densities in the region, emission dispersion calculation approaches, meteorological conditions, intermittency of the emissions, and emission rates, were examined. The results consistently indicated that, for the conditions in the Permian Basin, a fixed monitoring network with approximately one continuous monitor per site is likely to be capable of consistently detecting site-level methane emissions in the range of 5–10 kg/h.

Development of a Sub-ppb Resolution Methane Sensor Using a GaSb-Based DFB Diode Laser near 3270 nm for Fugitive Emission Measurement.

A challenge for mobile measurement of fugitive methane emissions is the availability of portable sensors that feature high sensitivity and fast response times, simultaneously. A methane gas sensor to measure fugitive emissions was developed using a continuous-wave, thermoelectrically cooled, GaSb-based distributed feedback diode laser emitting at a wavelength of 3.27 μm to probe methane in its strong ν3 vibrational band. Direct absorption spectra (DAS) as well as wavelength-modulated spectra (WMS) of pressure-broadened R(3) manifold lines of methane were recorded through a custom-developed open-path multipass cell with an effective optical path length of 6.8 m. A novel metrological approach was taken to characterize the sensor response in terms of the linearity of different WMS metrics, namely, the peak-to-peak amplitude of the X2f component and the peak and/or the integrated area of the background-subtracted quadrature signal (i.e., Q(2f - 2f0)) and the background-subtracted 1f-normalized quadrature signal (i.e., Q(2f/1f - 2f0/1f0)). Comparison with calibration gas concentrations spanning 1.5 to 40 ppmv indicated that the latter WMS metric showed the most linear response, while fitting DAS provides a traceable reference. In the WMS mode, a sensitivity better than 1 ppbv was achieved at a 1 s integration time. The sensitivity and response time are well-suited to measure enhancements in ambient methane levels caused by fugitive emissions.

Adaptively Optimized Gas Analysis Model with Deep Learning for Near-Infrared Methane Sensors.

Noise significantly limits the accuracy and stability of retrieving gas concentration with the traditional direct absorption spectroscopy (DAS). Here, we developed an adaptively optimized gas analysis model (AOGAM) composed of a neural sequence filter (NSF) and a neural concentration retriever (NCR) based on deep learning algorithms for extraction of methane absorption information from the noisy transmission spectra and obtaining the corresponding concentrations from the denoised spectra. The model was trained on two data sets, including a computationally generated one and the experimental one. We have applied this model for retrieving methane concentration from its transmission spectra in the near-infrared (NIR) region. The NSF was implemented through an encoder-decoder structure enhanced by the attention mechanism, improving robustness under noisy conditions. Further, the NCR was employed based on a combination of a principal component analysis (PCA) layer, which focuses the algorithm on the most significant spectral components, and a fully connected layer for solving the nonlinear inversion problem of the determination of methane concentration from the denoised spectra without manual computation. Evaluation results show that the proposed NSF outperforms widely used digital filters as well as the state-of-the-art filtering algorithms, improving the signal-to-noise ratio by 7.3 dB, and the concentrations determined with the NCR are more accurate than those determined with the traditional DAS method. With the AOGAM enhancement, the optimized methane sensor features precision and stability in real-time measurements and achieves the minimum detectable column density of 1.40 ppm·m (1σ). The promising results of the present study demonstrate that the combination of deep learning and absorption spectroscopy provides a more effective, accurate, and stable solution for a gas monitoring system.

Highly sensitive methane detection based on light-induced thermoelastic spectroscopy with a 2.33 µm diode laser and adaptive Savitzky-Golay filtering.

In this manuscript, a highly sensitive methane (CH4) sensor based on light-induced thermoelastic spectroscopy (LITES) using a 2.33 µm diode laser with high power is demonstrated for the first time. A quartz tuning fork (QTF) with an intrinsic resonance frequency of 32.768kHz was used to detect the light-induced thermoelastic signal. A Herriot multi-pass cell with an effective optical path of 10 m was adopted to increase the laser absorption. The laser wavelength modulation depth and concentration response of this CH4-LITES sensor were investigated. The sensor showed excellent long term stability when Allan deviation analysis was performed. An adaptive Savitzky-Golay (S-G) filtering algorithm with χ2 statistical criterion was firstly introduced to the LITES technique. The SNR of this CH4-LITES sensor was improved by a factor of 2.35 and the minimum detection limit (MDL) with an integration time of 0.1 s was optimized to 0.5 ppm. This reported CH4-LITES sensor with sub ppm-level detection ability is of great value in applications such as environmental monitoring and industrial safety.

Digital recognition method of methane sensor based on improved CNN-SVM

Digital recognition method of methane sensor based on improved CNN-SVM

High-Robustness Near-Infrared Methane Sensor System Using Self-Correlated Heterodyne-Based Light-Induced Thermoelastic Spectroscopy

High-Robustness Near-Infrared Methane Sensor System Using Self-Correlated Heterodyne-Based Light-Induced Thermoelastic Spectroscopy