Real-time Gas Detection Research Articles

IoT - Toxic Gas Detection System in Sewage

The application of IoT and machine learning in gas detection systems for sewage offers an innovative approach to real-time gas detection and enhances environmental safety in industrial and urban environments. This system uses advanced technologies such as the Internet of Things (IoT), machine learning algorithms, and high-performance sensors like the MQ135 and the DHT11 to achieve the best results in the gas concentration measurement and collection. It identifies abnormalities and determines which emission control measures are most effective for specific release points and similar situations. The design of the device includes sensor nodes that are primarily responsible for data collection and a central microcontroller (MCU) that operates a machine learning algorithm for efficient anomaly detection and predictive maintenance. The system uses the IoT connection to regularly send data to cloud platform (Blynk), enabling real time monitoring of gas levels and environmental conditions. The system generates a visual image of the captured data that can be accessed online. Distinctive attributes like accurate gas detection, continuous monitoring, predictive maintenance, remote assessment, and comprehensive data visualization, all contribute to smart decision-making for environmental safety.

Design of smart nose system for hazardous zone

The sole goal of the project is to develop design along with production of a novel smart nose system capable of accurately sensing and identifying a wide range of chemical gases. This innovative technology will utilize advanced gas sensors, data processing, and machine learning techniques to enable real-time gas detection and identification, with potential applications in various fields, including environmental monitoring, industrial safety, and healthcare. The vital goal is to create a reliable, user-friendly, and versatile device that enhances our ability to detect and respond to hazardous gases in our surroundings, ultimately contributing to safety, environmental preservation, and public health. The system will integrate wireless communication for remote monitoring and control, ensuring convenience and accessibility for end-users. Furthermore, the smart nose will be designed to have low power consumption, making it suitable for deployment in remote and resource-limited settings. Prototyping and rigorous testing will be conducted to validate the system's performance and robustness. The project aims to push the boundaries of current gas sensing technologies, paving the way for future advancements in smart environmental sensing.

Fully Inkjet-Printed Gas-Sensing Antenna Based on Carbon Nanotubes for Wireless Communication Applications

This study presents a fully inkjet-printed antenna based on carbon nanotubes (CNTs) for ammonia-gas-sensing. The antenna-embedded CNT film must satisfy strict requirements in terms of resistance, uniformity, and stability. Various resistive-type gas sensors based on CNTs, which are controlled by droplet spacing (DS) and multiple passes, are realized by inkjet-printing technology to investigate their sensing characteristics. The gas response decreases with increasing CNT density; however, the distribution of repeatability is tighter because the connection junctions among CNTs increase and the conductive paths saturate. The relationship between CNT density and gas-sensing response is presented for the first time. According to the response of the resistive-type gas sensors, low resistance and stable sensing response of a DS = 20 μm and 20-pass CNT film is suitable for embedding into an antenna for gas-sensing. The sensitivities of insertion and return losses under exposure to NH3 of the gas-sensing antenna demonstrate a linear relationship and reproducibility. The gas-sensing antenna based on CNT films exhibits two-dimensional value responses, real-time gas detection, reproducibility, and reversibility, which provide a viable option for wireless sensing systems.

Research on the Applicability of Integrated Earth Lifting Machine for 500KV Transmission and Substation Project in Highland Area

In the power construction project, the tower foundation deep pit operation is a fundamental and important process, because of its operation point of the geological foundation structure complex and changeable, large amount of work, many dangerous factors and difficult to observe and identify, the construction is easy to cause safety accidents. State Grid Gansu Power Company has developed a set of intelligent equipment for deep foundation pit operation which is easy to operate, safe, reliable and complete in function, relying on the equipment to actively and timely discover the danger and early warning, and automatically take effective measures to strengthen the protection of personnel, crack the problem of safe operation of deep foundation pit and prevent the occurrence of infrastructure safety accidents. Since this project is located in the plateau area of Tibet, it is intended to study the applicability of the integrated deep foundation pit operation intelligent machine in the plateau area through this project. This project adopts "integrated deep pit operation intelligent machine"; this equipment is specially designed for a series of problems encountered in deep pits and other limited spaces, it integrates a number of practical functions: electric lifting, real-time gas detection, intelligent air supply, automatic sound and light alarm, emergency rescue, differential hanging point, soft ladder hanging point, lighting power, etc. It aims to protect the lives of construction personnel, improve the safety and reliability of construction, enhance construction efficiency, and is a kind of equipment with convenient transportation, installation and operation, diversified and integrated functions and intelligent control.

Expanding the range of trackable environmental pollutants for Nafion based fuel cell type gas sensor

Expanding the application field of fuel cell type gas sensors is of great significance for achieving high sensitivity, real-time gas detection at room temperature. Here, we developed a fuel cell type NO2 gas sensor based on MoS2 nanosheet/Carbon fiber (CF) sensing electrode and Nafion proton conductor, enabling the fuel cell type sensor to detect NO2 for the first time. The effect of MoS2 loadings(10, 20, 40, 60 and 80 mg) on the sensors’ performance and the sensitizing mechanism are investigated, the sensor containing 40 mg MoS2 exhibits the best performance, which can be attributed to the large number of active sites and efficient carrier transport properties of the corresponding electrode materials. At room temperature, the optimal sensor demonstrates a sensitivity of 45.6 nA/ppm to 1–50 ppm NO2 and an ultra-low detection limit of 28.8 ppb, both the response and recovery time are less than 60 s. This work not only develops a highly sensitive, reversible and fast response NO2 gas sensor, but also provides insights into sensing electrochemistry, material selection and catalyst/support materials, offering guidelines for future fuel cell type gas sensors to monitor a wider range of environmental pollutants.

Ocimum sanctum Leaf Extract-Assisted Green Synthesis of Pd-Doped CuO Nanoparticles for Highly Sensitive and Selective NO2 Gas Sensors.

In view of facile, cost-effective, and environmentally friendly synthetic methods, palladium-doped copper oxide (Pd-CuO) nanoparticles have been synthesized from Ocimum sanctum (commonly known as "Tulsi") phytoextract for gas-sensing applications. The structural, morphological, and compositional properties of Pd-doped CuO nanoparticles were studied using various techniques such as XRD, FESEM, XPS, and EDX. The characterization results confirmed the doping of Pd on CuO nanoparticles, and Pd-CuO nanostructures appear as nanoflakes in FESEM analysis. The gas-sensing response of Pd (1.12 wt %)-CuO nanoflake-based sensor was measured at 5-100 ppm concentration of different gases, NO2, H2S, NH3, and H2, at 125 °C. Gas-sensing tests reveal that the sensitivity of the sensor were 81.7 and 38.9% for 100 and 5 ppm concentrations of NO2, respectively, which was significantly greater than that of pure CuO. The response and recovery times of the sensor were 72 and 98 s for 100 ppm of NO2 gas, while they were 90 and 50 s for 5 ppm NO2. The calculated limit of detection (LOD) value of the sensor is 0.8235. This appealing LOD is suitable for real-time gas detection. The gas sensor was found to exhibit excellent selectivity toward NO2 gas and repeatability and stability in humid (80%) conditions. The Pd doping in CuO nanostructures plays a significant role in escalating the sensitivity and selectivity of CuO-based NO2 gas sensor suitable to work at low operating temperatures.

High-precision nanosecond detection of a gas absorption spectrum based on optical frequency comb time-frequency mapping.

There is an increasing demand for high-precision gas absorption spectroscopy in basic research and industrial applications, such as gas tracking and leak warning. In this Letter, a novel, to the best of our knowledge, high-precision and real-time gas detection method is proposed. A femtosecond optical frequency comb is used as the light source, and a broadening pulse containing a range of oscillation frequencies is formed after passing through a dispersive element and a Mach-Zehnder interferometer. Four absorption lines of H13C14N gas cells are measured at five different concentrations within a single pulse period. A single scan detection time of only 5 ns is obtained along with a coherence averaging accuracy of 0.0055 nm. High-precision and ultrafast detection of the gas absorption spectrum is accomplished while overcoming complexities related to the acquisition system and light source that are encountered in existing methods.

Simultaneous Detection of Methane, Ethane, and Propane by QEPAS Sensors for On-Site Hydrocarbon Characterization and Production Monitoring.

Natural gas is sampled and produced throughout the lifespan of a petroleum field. Gas composition and isotope data are critical inputs in the exploration and field development, such as gas show identification, petroleum system analysis, fluid characterization, and production monitoring. On-site gas analysis is usually conducted within a mud gas unit, which is operationally unavailable after drilling. Gas samples need to be taken from the field and shipped back to the laboratory for gas chromatography and isotope-ratio mass spectrometry analyses. Results are usually without sufficient resolution to fully characterize the heterogeneity and dynamics of fluids within the reservoir and the production system. In addition, it often takes a considerable time to obtain the results using the traditional method. A novel QEPAS (quartz-enhanced photoacoustic spectroscopy) sensor system was developed to move gas composition analyses to field for quasi-real-time characterization and monitoring. With respect to previously reported QEPAS prototypes for trace gas detection, the new system realized measuring concentrations of methane (C1), ethane (C2), and propane (C3) in gas phase within the percentage range that is typically encountered in natural gas samples from oil and gas fields. A gas mixing enclosure was used to dilute the natural gas-like mixtures in nitrogen gas (N2) to avoid the saturation of QEPAS signals. An iterative analysis based on multilinear regression of QEPAS spectra was developed to filter out the influence of gas matrix variation from multiple hydrocarbon components. The advance in simultaneous measuring hydrocarbon gases and expanded linearity range of QEPAS, with previously reported detection of H2S, CO2, and gas isotopes (12CO2/13CO2, 13CH4/12CH4), opens a way to use the advanced sensing technology for in situ and real-time gas detection and chemical analysis in the oil industry.

High Accuracy Real-Time Multi-Gas Identification by a Batch-Uniform Gas Sensor Array and Deep Learning Algorithm.

Semiconductor metal oxide (SMO) gas sensors are attracting great attention as next-generation environmental monitoring sensors. However, there are limitations to the actual application of SMO gas sensors due to their low selectivity. Although the electronic nose (E-nose) systems based on a sensor array are regarded as a solution for the selectivity issue, poor accuracy caused by the nonuniformity of the fabricated gas sensors and difficulty of real-time gas detection have yet to be resolved. In this study, these problems have been solved by fabricating uniform gas sensor arrays and applying the deep learning algorithm to the data from the sensor arrays. Nanocolumnar films of metal oxides (SnO2, In2O3, WO3, and CuO) with a high batch uniformity deposited through glancing angle deposition were used as the sensing materials. The convolutional neural network (CNN) using the input data as a matrix form was adopted as a learning algorithm, which could conduct pattern recognition of the sensor responses. Finally, real-time selective gas detection for CO, NH3, NO2, CH4, and acetone (C3H6O) gas was achieved (minimum response time of 1, 8, 5, 19, and 2 s, respectively) with an accuracy of 98% by applying preprocessed response data to the CNN.

Facile synthesis of reduced graphene oxide encapsulated selenium nanoparticles prepared by hydrothermal method for acetone gas sensors

The Selenium nanoparticles (Se NPs) have been encapsulated with uniformly reduced graphene oxide by a simple hydrothermal approach. The crystallinity of rGO-Se nanocomposite was controlled by heat treatment. The crystal structure, morphology, and composition were confirmed by XRD, SEM, TEM, EDS, and XPS. The results showed that as-fabricated Se NPs with a mean diameter of 200–400 nm are assembled and well adhered to the rGO matrix. The rGO-Se nanocomposite based sensor exhibits a higher response, under exposure to 100 ppm acetone, with their response 3.2, which is 2 times higher than pristine Se at 135 °C. Furthermore, the prepared sensor also showed more rapid response-recovery time and good reproducibility. It was observed that a high specific area was accomplished by an increase in porosity of rGO-Se nanocomposite. The as-prepared graphene-based composite can be used in a wide range of applications due to high performance in real-time gas detection.

Graphene Oxide-Loaded SnO2 Quantum Wires with Sub-4 Nanometer Diameters for Low-Temperature H2S Gas Sensing

Chemiresistive gas sensors with low power consumption, high sensitivity, and selectivity are of great significance for low-cost and efficient real-time gas detection. Herein, we present a highly se...

Instrumentation for quantitative analysis of volatile compounds emission at elevated temperatures. Part 2: Analysis of carbon fibre reinforced epoxy composite

We have investigated the release of gases and volatile organic compounds (VOCs) from a carbon fibre reinforced epoxy composite matrix used in aircraft structural components. Analysis was performed at several temperatures both up to and above the recommended operating temperature (121 °C) for the material, to a maximum of 250 °C. Gas chromatography-mass spectrometry (GC-MS) combined with thermal desorption (TD-GC-MS) was used to identify and quantify VOCs, and in parallel real-time gas detection with commercial off-the-shelf (COTS) gas sensors. Under hydrocarbon free air, CO, SO2, NO, NO2 and VOCs (mainly aldehydes, ketones and a carboxylic acid) were detected as the gaseous products released during the thermal exposure of the material up to 250 °C, accompanied by increased relative humidity (4%). At temperatures up to 150 °C, gas and volatile emission was limited.

Ultrasensitive ppb-level H2S gas sensor at room temperature based on WO3/rGO hybrids

Gas sensors with high performance and low energy consumption are necessary for system integration in the environment monitoring of explosives. High-performance room-temperature hydrogen sulfide (H2S) gas sensors were fabricated based on the hybrids of tungsten oxide (WO3)/reduced graphene oxide (rGO). WO3 nanocubes modified with different contents of rGO were synthesized by acid-based hydrothermal method. The optimal amount of rGO in the composite is 5 wt%, at which the room-temperature operating sensor presents the highest sensitivity of approximately 32.7 to 500 ppb H2S, a fast recovery time of ~ 180 s, as well as an excellent sensing selectivity. The overall improvement in sensing performances demonstrated an important progress made in H2S sensor fabrication for portable and real-time gas detection. The improved gas-sensing performance is attributed to the large specific surface area of the WO3 nanocubes, the formation of WO3/rGO heterojunctions, and the enhanced electron transfer efficiency.

Revolutionising confined space safety management: a case study

Maintaining safety during confined space work is one of the most challenging aspects of a turnaround, shutdown or outage. To handle the safety risks associated with confined space entry, a safety attendant is normally assigned to the entry point of a confined space to maintain communication with workers inside. However, the duties of the attendant are restricted to the outside of the vessel. So how do we ensure that the workers continue to be safe when they are inside the confined space? Harnessing technological advancements, a solution was designed that combines gas detection, video surveillance, two-way communication, access control and a command centre to enhance safety while improving productivity and reducing costs. The solution combines real-time gas detection, cameras with day–night vision installed outside and inside vessels, video recording and two-way communication. It delivers and stores data useful in alerting workers of hazardous environments, providing video evidence of safe behaviour, enables operators to see work inside the vessels in real time, and stores video and overlaid gas data logs to protect against future liabilities. The innovation revolutionises the role of the safety watch by replacing multiple safety attendants with two highly qualified personnel who monitor all confined spaces from the command centre. The paper discusses examples and provides data on the potential savings resulting from the implementation of the technology. The present paper also discusses further enhancements, including body cameras, monitoring of employee wellbeing and facility access control. This is the path to the digital turnaround of the future.

Charge transfer in graphene/polymer interfaces for CO2 detection

Understanding charge transfer processes between graphene and functional materials is crucial from the perspectives of fundamental sciences and potential applications, including electronic devices, photonic devices, and sensors. In this study, we present the charge transfer behavior of graphene and amine-rich polyethyleneimine (PEI) upon CO2 exposure, which was significantly improved after introduction of hygroscopic polyethylene glycol (PEG) in humid air. By blending PEI and PEG, the number of protonated amine groups in PEI was remarkably increased in the presence of water molecules, leading to a strong electron doping effect on graphene. The presence of CO2 gas resulted in a large change in the resistance of PEI/PEG-co-functionalized graphene because of the dramatic reduction of said doping effect, reaching a maximum sensitivity of 32% at 5,000 ppm CO2 and an applied bias of 0.1 V in air with 60% relative humidity at room temperature. This charge transfer correlation will facilitate the development of portable graphene-based sensors for real-time gas detection and the extension of the applications of graphene-based electronic and photonic devices.

Photocatalytic air-purification: a low-cost, real-time gas detection method

This research demonstrates the use of a gas detector as a feasible alternative to the standardized analytical methods typically found in photocatalytic air-purification ISO standard tests and academic literature.

CFD-Based Transient Ignition Probability Modeling of Gas Leaks in Enclosures

Risk management of loss of containment at facilities processing or storing liquid flammable fluids is crucial in order to ensure safe operations. To control the risk, an extensive set of safety functions are in general implemented in design, for example systems that minimize the occurrence for initiating events (e.g., spontaneous leak of flammable material due to fatigue) and measures that reduce explosion loads arising in case of delayed ignition of the dispersed fluid mixed with air. Effective ventilation of the released fluid that potentially generates an explosive atmosphere (gas and/or droplets generated from the liquid phase) is one of the crucial barrier elements to mitigate the explosion hazard. Hence, the gas explosion hazard in enclosed modules with poor ventilation is of particular concern as a flammable mixture may accumulate even for small release rates. This may result in both high likelihood of ignition and considerable explosion loads in case of ignition due to the big amount of chemical energy taking part in the combustion process relative to the size of the enclosure. Computational fluid dynamics (CFD) methods are increasingly being used to characterize the consequences of leaks of flammable fluid in complex geometries, both modeling of the initial gas dispersion process and the resulting explosion and fire loads following from the combustion process in case of ignition. This paper presents an advanced methodology based on the CFD tool OpenFOAM for detailed assessment of the transient gas dispersion process and the associated likelihood of ignition for leaks of flammable fluid inside enclosures. The objective is to understand how to optimize the design of safety functions that affect the fire and explosion risk picture. This custom made tool, denoted cloudIgnitionFoam, accounts for the transient gas leak behavior based on real-time gas detection, subsequent initiation of emergency shutdown (ESD) and blow down systems and computes ignition probability based on the transient history of the dispersed gas cloud. The consistent methodology based on the CFD technology available in OpenFOAM and its ability to present the results in detail leverages the risk-based decision process. Measures that can be assessed quantitatively includes number and types of gas detectors and their optimal positioning, ignition source isolation, gas detection system voting philosophy, capacity of depressurization system and structural integrity of explosion barriers.

Silicone Containing Amphiphilic Co-Networks as Immobilisation Matrices for Enzyme based Biosensors: Optochemical Detection of Gaseous Hydrogen Peroxide

Silicone containing amphiphilic co-networks are used as immobilisation matrix for ABTS (chromophor) and horseradish peroxidase. The good gas diffusion properties as well as the good immobilisation properties of the silicone containing APCNs, reffered to as silicone hydrogels, makes this type of sensors suitable for fast real-time gas detection. Detection of 1ppm of gaseous peroxide (used as explosive or precursor) is feasible within seconds.