Gas Detection Research Articles

Characterization of o-B2N2 monolayer surface for effective sensing and detection of toxic nitrogen oxides

Using density functional theory (DFT), researchers examined how the o-B2N2 monolayer interacts with toxic gases such as NO2 and NO. They investigated various aspects of the adsorption system, including its state density, differential charge density, optimal structure, and other features. The analysis revealed that the o-B2N2 monolayer remained stable throughout the adsorption process. The researchers also examined additional properties of the adsorption process, including work function, sensitivity, response time, recovery time, and energy gap to gain a deeper comprehension of adsorption features of NO2 and NO gases. The findings indicate that the o-B2N2 monolayer exhibits a shorter recovery time at higher temperatures, suggesting improved performance. Furthermore, the monolayer demonstrates higher sensitivity and a greater variation in the work function, indicating its superior adsorption capabilities. The findings suggest that the o-B2N2 monolayer has potential with great promise as an adsorbent for toxic gases like NO2 and NO. This has practical implications for applications such as air purification and environmental protection. Our findings demonstrate that the o-B2N2 monolayer serves as an effective gas sensor at room temperature, exhibiting both high sensitivity and selectivity in detecting NO2 and NO gases. Moreover, it can be reused multiple times for gas detection purposes.

Multi-component Freon gas detection based on infrared tunable Fabry-Perot detector

A multi-component Freon gas sensing system was developed based on the non-dispersive infrared (NDIR) spectroscopy, which had the advantages of simple structure and low cost. This system mainly consisted of a broadband infrared light source, a tunable Fabry-Perot (FP) detector and a small brass absorption cell. In order to improve the utilization rate of infrared light source, the absorption cell was optimized. H1301, R134a and R410A were simultaneously detected through the measurement of gas absorption spectrum. The spectral range of the FP detector was from 8.0 to 10.5 μm, and the entire spectrum was recorded by wavelength scanning. The area of measured absorbance was directly proportional to the concentration of the target gas, which was used to calibrate the sensing system. The temperature and frequency response characteristics of the system were tested. The experimental results showed that the higher the temperature, the lower the responsiveness. In addition, the system had the best signal-to-noise ratio (SNR) at 10 Hz. The minimum detection limits of H1301, R134a and R410A were achieved to be 2.5 ppm, 5.3 ppm and 4.8 ppm, respectively. The cross-interference among the gases was suppressed by using BP neural network.

Development of a Neural Network for Target Gas Detection in Interdigitated Electrode Sensor-Based E-Nose Systems.

In this study, a neural network was developed for the detection of acetone, ethanol, chloroform, and air pollutant NO2 gases using an Interdigitated Electrode (IDE) sensor-based e-nose system. A bioimpedance spectroscopy (BIS)-based interface circuit was used to measure sensor responses in the e-nose system. The sensor was fed with a sinusoidal voltage at 10 MHz frequency and 0.707 V amplitude. Sensor responses were sampled at 100 Hz frequency and converted to digital data with 16-bit resolution. The highest change in impedance magnitude obtained in the e-nose system against chloroform gas was recorded as 24.86 Ω over a concentration range of 0-11,720 ppm. The highest gas detection sensitivity of the e-nose system was calculated as 0.7825 Ω/ppm against 6.7 ppm NO2 gas. Before training with the neural network, data were filtered from noise using Kalman filtering. Principal Component Analysis (PCA) was applied to the improved signal data for dimensionality reduction, separating them from noise and outliers with low variance and non-informative characteristics. The neural network model created is multi-layered and employs the backpropagation algorithm. The Xavier initialization method was used for determining the initial weights of neurons. The neural network successfully classified NO2 (6.7 ppm), acetone (1820 ppm), ethanol (1820 ppm), and chloroform (1465 ppm) gases with a test accuracy of 87.16%. The neural network achieved this test accuracy in a training time of 239.54 milliseconds. As sensor sensitivity increases, the detection capability of the neural network also improves.

Design and implementation of shear-horizontal mode surface acoustic wave formaldehyde gas sensor

Surface acoustic wave (SAW) sensors have garnered significant attention due to high performance in gas detection. Love wave as the special mode of SAW has greater attractiveness for molecules detection in gas or liquid. To investigate the detection performance of Love wave SAW (Love-SAW) devices towards toxic gases, the influence of interdigitated electrodes on the propagating performance of Rayleigh and shear horizontal (SH) waves along ST-X and ST-90°X quartz substrates was analyzed by finite element method (FEM). The effect of the guiding layer based on different substrates of SAW devices has been discussed by changing the thickness of the zinc oxide (ZnO) layer. The results indicate that the SH wave excited on ST-90°X quartz substrate couples into the Love wave mode through the ZnO guiding layer, and Rayleigh waves in ST-X quartz do not exhibit mode coupling. The Love-SAW sensor covering the ZnO guiding layer (0.5 µm) has shown high mass sensitivity. The vibration distribution of SH wave excited by different thicknesses of electrodes is different, indicating that the mass-loading is one of the reasons for the transformation of SH wave into Love wave. Furthermore, the 50 ppm, 95 ppm and 110 formaldehyde-load leads the short-circuit resonance frequency of the polyetherimide (PEI) covered Love-SAW devices to drop by 100.1 kHz, 200.2 kHz and 300.3 kHz, respectively. The maximum detection sensitivity reached about 2.73 kHz/ppm. This work can provide valuable insights for designing and investigating the SAW formaldehyde gas sensors.

Designing of three-dimensional hierarchical Co3O4@g-C3N4 nanostructure with highest surface area for sensitive detection of NO2 gas at room temperature

Designing of three-dimensional hierarchical Co3O4@g-C3N4 nanostructure with highest surface area for sensitive detection of NO2 gas at room temperature

Room temperature ppb level-NO2 sensor based on WS2 with Fe -ni co-catalyst modification

Rapid and highly sensitive detection of toxic gases at room temperature is crucial for ensuring health protection. However, the sensing materials reported thus far face the challenge of exhibiting relatively low response values at room temperature. In this study, we propose a facile synthesis method to prepare a Fe and Ni co-doped WS2 gas sensor capable of detecting nitrogen dioxide (NO2) at parts-per-billion (ppb) levels even at room temperature. The Fe-Ni@WS2 gas sensor exhibits excellent response performance (The response value is 81 at 1 ppm NO2). The incorporation of Fe and Ni into WS2 forms a Schottky junction that effectively regulates charge transfer behavior within the sensor material through conduction tunneling. Furthermore, the catalytic properties of Fe and Ni promote gas-sensitive reactions on the surface of WS2. This investigation elucidates the sensing mechanism of WS2 towards NO2 under bimetallic modification and provides an effective approach for constructing high-performance gas sensors through rational design of active sites.

Optimization for hydrogen gas quantitative measurement using tunable diode laser absorption spectroscopy

Gas sensors are vital for safety, quality control, and environmental preservation across diverse industries. Tunable diode laser absorption spectroscopy (TDLAS) is widely employed for gas analysis due to its high sensitivity and selectivity. However, quantifying low concentrations of hydrogen gas using TDLAS in the infrared region proves challenging due to its lower absorption compared to other gases in the NIR region. This study introduces an innovative method for precise hydrogen gas measurement through the analysis of absorption spectra at different pressures and the employment of a high-pressure gas cell. By applying these methodologies to a calibration-free technique, we can realize the optimal measurement conditions for increasing the gas detection limit and stabilizing the wavelength lock for the laser diode. As a result, a linear relationship between hydrogen gas concentration and sensor response is achieved in the wide detection range of 0.01% to 100%. Under the optimal measurement conditions, our TDLAS system’s stability is demonstrated with minimum detection limits of 0.2866% and 0.0055% at integration times of 1 s and 30 s, respectively.

Design of Intelligent Window Automatic Monitoring System Based on Microcontroller Control

To ensure the safety of residents’ lives and property by using automatic opening and closing of ordinary windows, this article designs an intelligent window automatic monitoring system. The article proposes a software and hardware design scheme for the system, which comprises a microcontroller control module, temperature and humidity detection module, harmful gas detection module, rainfall detection module, human thermal radiation induction module, Organic Light-Emitting Diode (OLED) display module, stepper motor drive module, Wi-Fi communication module, etc. Users use this system to monitor environmental data such as temperature, humidity, rainfall, harmful gas concentrations, and human health. Users can control the opening and closing of windows through manual, microcontroller, and mobile application (app) remote methods, providing users with a more convenient, comfortable, and safe living environment.

DFT study of Cun (n = 1–3) cluster-modified WSe2 monolayers for the detection of SF6 decomposition gases (H2S, SO2, SOF2 and SO2F2)

The role of SF6 in gas-insulated equipment is crucial for insulation and arc extinction. When subjected to electrical faults, thermal faults, and mechanical failures in insulation devices, SF6 can decompose into H2S, SO2, SOF2, and SO2F2. This study conducted first-principles simulations to analyze the effect of Cun (n = 1–3) cluster-modified monolayer WSe2 on sensing H2S, SO2, SOF2, and SO2F2 gases during SF6 decomposition. The optimal doping positions of Cun-WSe2 clusters at n = 1, 2, and 3 were determined, showcasing their superiority over pure WSe2 in terms of band gap, frontier orbitals, and density of states. Subsequently, a detailed investigation was carried out on the adsorption effects of the four gases at the optimal doping positions, including parameters such as adsorption distance, adsorption energy, charge transfer, differential charge density, and density of states. It was found that when the number of copper atoms in the cluster increased to 3, the banding energy sharply rose to -2.395 eV. Simulation results demonstrated the adsorption behavior of H2S on Cun (n = 1–3)-WSe2 demonstrates remarkable affinity, with an increasing adsorption energy as the number of Cu atoms rises. Notably, Cun (n = 1–3)-WSe2 exhibits favorable desorption times for SO2. At 378 K, the desorption time for SO2@Cu3-WSe2 is 14.7 s. At 498 K, desorption times for SO2@Cu1-WSe2 and SO2@Cu2-WSe2 are 7.59 s and 2.85 s, respectively. When the number of copper atoms is 2, the cluster-modified system desorbs SOF2 at room temperature in only 0.89 s. Moreover, Cun (n = 1–3)-WSe2 displays strong adsorption towards SO2F2, characterized by prolonged desorption times, rendering it a promising adsorbent for SO2F2 removal. This study aims to highlight the adsorption effects of different numbers of Cu atomic clusters in the Cun-WSe2 system on SF6 decomposition gas, further advancing research for developing high-performance SF6 decomposition gas sensors.

A Magnetic Flux Leakage Detector for Ferromagnetic Pipeline Welds with a Magnetization Direction Perpendicular to the Direction of Travel.

For the sake of realizing the safety detection of natural gas and petroleum pipeline welds, this paper designs a ferromagnetic pipeline weld magnetic flux leakage detector based on the calculation of the magnetic circuit of the detection probe, with the magnetization direction perpendicular to the traveling direction. The traditional pipeline magnetic flux leakage detection device uses a detection system mode in which the magnetization direction is parallel to the direction of travel. However, due to the structural characteristics of the weld, the traditional detection system mode is not applicable. Since the weld magnetic flux leakage detector needs to travel along the direction of the weld, the detector designed in this paper rotates the magnetizer 90 degrees along the direction of the weld seam so that the magnetization direction is perpendicular to the direction of travel, breaking through the technical barrier that make traditional magnetic flux leakage detection devices unsuitable for weld detection. The detection device includes a magnetizing structure, a data sampling device, and a driving and traveling device. The magnetic flux leakage signal collected by the detector is converted into a digital image in the form of a grayscale matrix. Using mathematical morphology and chain code algorithms in image processing technology, a pipeline weld defect inversion software system is developed, and a preliminary quantitative analysis of pipeline weld defects is achieved. The application of this technology enables the inspection and protection of oil and gas pipeline welds throughout their life cycle, broadens the scope of existing inspection objects, and is of great safety significance for ensuring national public security.

Ag–CdS-Coated Cladding-Modified Fiber-Optic Sensor for Detection of NO2 Gas

In this study, a cladding-modified fiber-optic sensor is reported for NO2 gas detection. Monitoring the purity of air is the need of the hour, with the growing technologies. Hence, in this work, Ag-doped CdS is coated over the cladding-removed region of 3[Formula: see text]cm length by Chemical Bath Deposition (CBD) which acts as the gas-sensing medium. Ag–CdS is coated over four fibers each with a different coating duration of 10[Formula: see text]min, 20[Formula: see text]min, 30[Formula: see text]min and 40[Formula: see text]min. Characterization of Ag–CdS is done by Scanning Electron Microscope (SEM), UV-visible absorption spectroscopy and UV-visible reflectance spectroscopy. The spectral response for different concentrations of NO2 is studied on the four fibers separately, and it is analyzed and compared. The minimum detection limit is 100[Formula: see text]ppm and the response time is 10[Formula: see text]min. Out of the four fibers, Ag–CdS-30 exhibited the best response with a sensitivity of 15.5%. The results are compared with cladding-modified fiber coated with CdS, which was reported previously. The effect of Ag-doping is analyzed.

Multi-modal flexible and inexpensive plasmonic metasurface for wide range of refractive index sensing

Abstract A plasmonic metasurface containing nanobumps of sub-wavelength feature size arranged in a hexagonal pattern on a flexible substrate and covered with a thin film of gold is investigated as a refractive index sensor. The chosen polymer patterns coated with gold aid in activating the surface plasmon polariton modes. Using numerical calculations, it is shown that this surface can exhibit plasmonic effect with an extremely shallow pattern height of 92.5 nm and minimal thickness of 25 nm of gold over it. The excitation of the plasmonic modes is confirmed using electric field profiles calculated at the relevant wavelengths. As the surface is highly sensitive to changes in the cladding index, and the chosen design aids in exciting three plasmon modes that are suitably well-separated in wavelength, this surface can be used for an extremely wide range of refractive index sensing because each mode contributes uniquely to a different range of refractive index. The results establish that the metasurface is suitable for a variety of applications, including gas detection with a sensitivity of 633 nm/RIU using mode-1, identifying SARS-CoV-2 viral molecules with a sensitivity of 428 nm/RIU using mode-2 and 238 nm/RIU using mode-3, and discriminating between normal and diseased brain tissues in the cerebrospinal fluid in the high-index range using mode-3. The prototype metasurface is made using a cost-effective soft lithography technique using an economical master mould. The inexpensive technique of fabrication, use of very thin metal film, and wavelength of detection lying within the visible to near infrared range imply a low-cost sensor. The structural and optical characterization of the prototype validates the numerical study of the sample.

High sensitivity gas detection based on Au waveguide

Hydrogen, as a representative of the new energy source, has great potential for development, and the detection of the low concentration of hydrogen plays a crucial role in process safety control. In this paper, a gas sensor based on a metal-insulator-metal (MIM) structure with high sensitivity and low response time is proposed. The sensor is bilaterally coupled with a ring resonant cavity and a metal nanopillar, and the outgoing field superposition is achieved by bilaterally coupling the resonant cavity to achieve a lower transmittance, which facilitates detection. The symmetry of the structure was broken by different methods, and the results showed that the complexity and response time of the structure increased significantly after breaking the symmetry, but the transmittance appeared to decrease and the transmission characteristic curve was independent of the coupling phase. The waveguide structure shows a highly linear relationship between resonance wavelength and refractive index with a maximum sensitivity of 2433.00 nm/RIU when used for refractive index sensing, and a maximum sensitivity of −2.61 nm/% when used for hydrogen gas sensing. For methane gas sensing, the maximum sensitivity is −9.22 nm/%.

Transition-metal-doped biphenylene for enhanced CO detection: A high-throughput first-principles study

Our study examines the CO detection capabilities of pristine biphenylene (BP) and transition-metal-doped BP (M–BPx, x = 1 or 2), a novel two-dimensional nano-carbon material. Despite BP’s potential in toxic gas detection, its low CO adsorption energy (Eads) of 0.134 eV limits its application. Utilizing a combined strategy of high-throughput computational screening and DFT technique, we accurately identified the ground state configurations of CO-adsorbed M–BPx (M = Sc, Ti, Fe, Co, Ni, and Zn) and confirmed M doping significantly boosts BP’s CO adsorption ability, with Eads values ranging from 2.809 to 8.206 eV. This improvement is supported by ab initio molecular dynamics simulations and ionic d-p bonding interactions observed in electron localization functions, densities of state (DOS), and bond population analysis. Additionally, CO adsorption induces notable DOS changes in the M–BPx systems, validating the potential of M–BPx as suitable materials for CO detection. Notably, Zn-BP2 show notable work function shift after CO adsorption (0.36 eV), outperforming pristine BP’s 0.30 eV shift, and favorable recovery time (8.940 s). These shifts underscore the potential of Zn-BP2 for work function-based high-temperature CO sensor, marking it as a promising material for sensitive CO detection.

Ultrasensitive and selective detection of ammonia gas at room temperature of La-doped ZnO thin films

Ultrasensitive and selective detection of ammonia gas at room temperature of La-doped ZnO thin films

Detection of hydrogen gas leak using distributed temperature sensor in green hydrogen system

This study addresses the challenges of hydrogen gas detection in pipelines, focusing on the highly flammable nature and low ignition energy of hydrogen. It highlights the limitations of traditional point-based detection methods and emphasizes the need for advanced safety technologies. An integrated approach combining onsite monitoring with Internet of Things (IoT) technology is proposed to enhance systematic safety management and quick leak detection in hydrogen infrastructures. The study reviews various hydrogen detection methods, emphasizing distributed temperature sensing (DTS) techniques to identify leaks through temperature variations. Over nine months of monitoring, the results indicate that DTS temperature variations are more influenced by sensor location than chamber configuration, suggesting that external DTS chamber installation is more effective for leak detection. Additionally, the study integrates Gaussian Process Regression with Machine Learning to predict temperature distribution in pipelines, providing valuable insights for future hydrogen leak detection research.

Design and Performance Assessment of a Mid-Wave Infrared InAsSb-based AlSb/InAlSb Barrier Photodetector for Carbonyl Sulfide Gas Detection as an Ultra-High Sensitivity Device in Industrial Applications

Design and Performance Assessment of a Mid-Wave Infrared InAsSb-based AlSb/InAlSb Barrier Photodetector for Carbonyl Sulfide Gas Detection as an Ultra-High Sensitivity Device in Industrial Applications

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.

An E-nose system for identification and quantification of hazardous gas mixtures using a combined strategy of CNNs and attentional mechanisms

The chemical industry generates a broad spectrum of hazardous gases, presenting significant challenges for conventional detection methods due to their diverse chemical properties and low concentration levels. E-nose systems, employing sensor arrays, offer significant potential for the determination of gas mixtures. This study presents a novel E-nose algorithm, CNN-ECA, which integrated CNNs and attention mechanisms to improve the recognition accuracy of E-nose systems. By integrating the attention mechanism module into CNN’s convolutional operations, the algorithm emphasizes critical feature information. Three hazardous gases (ammonia, methanol, and acetone) and their mixtures were chosen as target gases. CNNs were combined with various attention mechanism networks (SENet, ECA, and CBAM) to construct models, which were then employed to train and evaluate data collected from the sensor array. The results were compared with traditional network models (KNN, SVM, and CNN). Experimental findings indicated that the prediction performance of CNN models combined with attention mechanism networks surpassed that of traditional network models. Particularly, the CNN-ECA network model demonstrated the highest performance in both qualitative and quantitative analyses. This study presents a promising solution for mixed gas detection by synergizing CNN and attention mechanism networks, thereby enhancing the accuracy and reliability of mixed gas measurements. Moreover, capitalizing on the lightweight architecture of the CNN-ECA model, transfer learning techniques were employed to adapt it for deployment on the Raspberry Pi hardware platform. This facilitates the development of a real-time E-nose system for gas detection.

Architectures of MoS2/SnO2 nanoflowers for NO2 gas detection

In this research, a two-step hydrothermal technique is used to fabricate a NO2 gas sensor based on MoS2 nanoflowers with SnO2 nanoparticles. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS), were used to determine the morphologies, nanostructures, and compositions of the materials. The nanocomposites consisting of MoS2/SnO2 demonstrated a remarkable sensing response (38.6) towards 100 ppm of NO2. Moreover, the nanocomposites showed a rapid response and recovery time (42/147 S) when exposed to 100 ppm at temperature (250 °C). The MoS2/SnO2 nanocomposites demonstrated exceptional selectivity to NO2 against CO, NH3, and H2S, as well as demonstrated good repeatability. The significant gas detection characteristics could potentially be controlled by the distinctive structures of thin layers assembled into flower-like formations of two-dimensional MoS2. The multi-joint nanostructures promote the process of transferring the electrical charge of the carrier and the response to MoS2/SnO2 and NO2. The fabricated sensors are considered as potential candidates for the commercial in the nitrogen dioxide gas-sensing applications.