Real-time Gas Monitoring Research Articles

High-Performance H2S Sensors to Detect SF6 Leakage.

Detecting H2S in oxygen-deficient conditions is vital for identifying leaks in SF6-insulated electrical equipment. Current infrared-based detection methods are expensive and sensitive to environmental conditions, highlighting the necessity for cost-effective and stable gas sensors. Existing gas sensors based on semiconducting metal oxides (SMOXs) are limited by redox reactions with oxygen and require high operating temperatures. Here, we introduce a room-temperature (RT) H2S sensor for oxygen-deficient environments using the intrinsic conducting two-dimensional (2D) metal-organic framework (MOF), Co1.8Ni1.2(hexaiminotriphenylene)2 [Co1.8Ni1.2(HITP)2], overcoming the limitations of SMOX gas sensors. Remarkably, Co1.8Ni1.2(HITP)2 sensors exhibit exceptional selectivity for H2S with negligible cross-responses and a sensitivity drift of less than 4.13% in an SF6 atmosphere over 60 days. The Co1.8Ni1.2(HITP)2 gas sensor shows significant promise for real-time and stable monitoring of H2S gas in oxygen-deficient environments.

Wearable Inorganic Oxide Chemiresistor Based on Flexible Al2O3-Stabilized ZrO2 Ceramic Sponge Substrate for NO2 Sensing.

Wearable gas sensors, possessing the advantages of high sensitivity, excellent flexibility, high permeability, low weight, and workability at ambient conditions, hold great promise for real-time health monitoring and early warnings of poisonous gases. However, obtaining high-performance wearable gas sensors utilizing the current well-developed inorganic semiconductor oxide sensing materials is still very limited due to their fragile and rigid nature. Herein, a newly designed wearable gas sensor based on an all-inorganic ASZ (Al2O3-stabilized ZrO2)/ZnO/SnO2 nanofibers is introduced for the first time. The flexible ASZ ceramic sponge substrate (with a Young's modulus of 4.15 MPa) and ultrathin ZnO/SnO2 sensing layer endow the wearable gas sensor with promising properties such as super flexibility (with a bending radius of 5 mm), high gas permeability, and low weight. Furthermore, driven by UV light irradiation, this all-inorganic wearable sensor also demonstrates a stable NO2 sensing response under different bending states at room temperature, which enables the gas sensor to be more compatible with wearable sensing applications. This work offers a general method to achieve a high-performance wearable gas sensor based on inorganic materials and provides new insights into their potential in wearable gas-sensing applications.

In-situ controllable preparation of ZIF-8-wrapped ZnO heterojunction nanorods array for ppb-level Cl2 detection operated at near room temperature

Real-time monitoring of chlorine gas (Cl2) with a strong irritating odor is an indispensable means of avoiding its contamination of the air and harm to the body. To achieve this purpose, a novel gas-sensitive material capable of detecting low concentrations of Cl2 at near room temperature is developed in this paper. ZIF-8 with a kinetic diameter of 3.4 Å in the designed ZnO@ZIF-8 composites is able to sieve gas molecules with different kinetic diameters, enabling effective improvement of the selectivity of the gas-sensitive materials. In addition, the porous structure of ZIF-8 can provide more reactive sites, which is conducive to improving gas adsorption capacity and providing of channels for gas diffusion. In this study, ZnO@ZIF-8 heterojunction composites are prepared by combining the advantages of ZnO and ZIF-8, which provides a new method for real-time monitoring of low concentrations of Cl2, and also has potential practical applications in fields such as environmental monitoring and industrial safety.

Real-Time Monitoring of Toxic Gases using Artificial Neural Networks with SnO2-based Thick Film Gas Sensors

The present study aims to develop an advanced system for real-time identification and measurement of harmful gases using Pd-doped SnO2-based thick film gas sensors integrated with Artificial Neural Networks. These sensors, renowned for their exceptional sensitivity and selectivity, undergo testing in a meticulously controlled gas chamber environment. Here, gas concentrations, temperature, and humidity are meticulously controlled. The gas chamber setup allows for mixing gases like carbon monoxide, nitrogen dioxide, and sulfur dioxide with nitrogen gas to achieve desired concentrations ranging from to . Temperature is kept at and relative humidity is maintained between to . The sensitivity analysis of the sensor demonstrates its adeptness in detecting low concentrations of target gases, with sensitivity increasing as gas concentrations rise, also the selectivity assessments highlight their ability to accurately differentiate between target gases and common interferents, ensuring precise detection even in complex gas mixtures. Response time testing indicates rapid detection capabilities, crucial and useful for emergencies. The Artificial Neural Network model, trained via the backpropagation algorithm, demonstrates remarkable accuracy, precision, recall, and F1 scores in predicting gas concentrations. The findings indicate that this integrated system offers a reliable and efficient solution for toxic gas detection, with potential applications in industrial safety and environmental monitoring.

WO3 Nanoplates Decorated with Au and SnO2 Nanoparticles for Real-Time Detection of Foodborne Pathogens.

Nowadays, metal oxide semiconductor gas sensors have diverse applications ranging from human health to smart agriculture with the development of Internet of Things (IoT) technologies. However, high operating temperatures and an unsatisfactory detection capability (high sensitivity, fast response/recovery speed, etc.) hinder their integration into the IoT. Herein, a ternary heterostructure was prepared by decorating WO3 nanoplates with Au and SnO2 nanoparticles through a facial photochemical deposition method. This was employed as a sensing material for 3-hydroxy-2-butanone (3H-2B), a biomarker of Listeria monocytogenes. These Au/SnO2-WO3 nanoplate-based sensors exhibited an excellent response (Ra/Rg = 662) to 25 ppm 3H-2B, which was 24 times higher than that of pure WO3 nanoplates at 140 °C. Moreover, the 3H-2B sensor showed an ultrafast response and recovery speed to 25 ppm 3H-2B as well as high selectivity. These excellent sensing performances could be attributed to the rich Au/SnO2-WO3 active interfaces and the excellent transport of carriers in nanoplates. Furthermore, a wireless portable gas sensor equipped with the Au/SnO2-WO3 nanoplates was assembled, which was tested using 3H-2B with known concentrations to study the possibilities of real-time gas monitoring in food quality and safety.

Enhanced selectivity of novel sea urchin-like h-/m-WO3 hetero nanoflowers for highly sensitive detection of ammonia by double filtration method

Real-time monitoring of toxic gases and breath markers can be performed by metal oxide semiconductor-based chemo-resistive gas sensors due to their simple structures, low cost, and faster and better recovery and response. Due to the effective electron-hole separation, tuning the development of phase junctions of the metal oxide is an efficient method for improving their gas sensing properties. In this study, two distinct hydrothermal methods were used to synthesize h-/m-WO3 hetero-nanoflowers. In the first method, L-Cysteine was used to facilitate the formation of phase junctions between h-WO3 and m-WO3 nanostructures, whereas Thiourea was used in the second. Various characterisation approaches validated the hexagonal-monoclinic phase junction development in the nanoflowers. The sensing material fabricated using Thiourea with a concentration of 0.01M performed better than other sensors. At 350°C, the selected sample displayed outstanding ammonia sensing capacity with the experimental detection limit of 1 ppm (response=3.33). The theoretical detection limit is determined to be 27 ppb. The selectivity of the sensor towards NH3 over H2S and acetone is increased by combining the devices with a double filtration setup. The effectiveness of the selected h-/m-WO3 sensor for diagnosing kidney dysfunction was evaluated by analyzing simulated breath samples.

SnS2 nanosheets/Ag2S quantum dots heterojunction supported by YSZ nanofibers enable real-time room-temperature NO2 gas sensor

Air pollution has emerged as a critical global issue, necessitating the development of effective real-time gas monitoring technologies. However, conventional gas sensors face limitations such as high energy consumption and sluggish response/recovery kinetics. In this study, we report a novel SnS2/Ag2S heterojunction supported by yttria-stabilized zirconia nanofibers (YSZ@SnS2/Ag2S) through a facile electrospinning, hydrothermal and subsequent cation-exchange method. This hierarchical composite nanofiber consists of well-distributed Ag2S quantum dots (QDs) attached to vertical anchored SnS2 nanosheets, forming an interconnected network supported by YSZ nanofibers. The integrated sensor exhibited high response of 3.27 and swift response/recovery times of 12/27 s to 1 ppm NO2 at room temperature, respectively. Benefited from highly permeable three-dimensional continuous conductive nanofibers, along with the hierarchical nanosheets/QDs structure, the gas transmission/diffusion and number of reactive sites were ensured. Additionally, the interfacial built-in electric field of SnS2/Ag2S heterojunction further enhanced the electron transfer, carrier migration and gas-sensitive reaction kinetics. This study provides insights into the development of functional composite nanofibers and paves the way for the exploitation of real-time room-temperature gas sensing applications.

Effectiveness of Pregnane Glycoside Compound Extracts from Sansevieria Trifasciata Laurentii and Sansevieria Trifasciata Plants in Reducing Carbon Monoxide Gas

This study aimed to evaluate the effectiveness of pregnane glycoside compound extracts obtained from the Sansevieria trifasciata Laurentii plant in reducing carbon monoxide (CO) emissions. The extraction method uses solvent extraction techniques with organic solvents to isolate active compounds from the plant. After extraction, the pregnane glycoside compound was tested at various concentrations to determine its effectiveness in reducing CO gas concentrations. Tests were carried out using tools that allow real-time gas monitoring. The results of this study show that the pregnane glycoside compound extract from the Sansevieria trifasciata Laurentii and Sansevieria trifasciata plants can reduce carbon monoxide gas with significant effectiveness. This research contributes to the development of natural solutions for reducing air pollution caused by CO gas and can develop plant-based air treatment technology.

Deep-learning assisted biomimetic self-powered wireless electronic noses system enabled by triboelectric discharge

Electronic noses, inspired by the mammalian olfactory system, have revolutionized various industries including food, environment, medicine, and rescue. They detect gases by using sensor arrays and provide real-time information. However, current electronic noses demand batteries as the power source, limiting their applications and requiring frequent maintenance. Wireless transmitter modules can also increase battery dependence and introduce high energy consumption. Moreover, gas sensors used in electronic noses lack multidimensional information identification ability. Here, we have developed a self-powered, wireless electronic nose system based on triboelectric discharge. By converting mechanical energy into high-voltage power, this system can initiate gas discharge and produce wireless signals that contain valuable gas information. With deep learning, it can recognize up to 60 different types of gas atmospheres with 93.8% accuracy, including 5 gas components with 12 different gas pressures. The system with a visualization program is designed for wireless, real-time gas monitoring toward applications in health monitoring, precise manufacturing, and high-tech fire rescue operations.

Colorimetric and fluorometric bimodal amine chemosensor based on deprotonation-induced intramolecular charge transfer: Application to food spoilage detection

Amine derivatives are signature organic compounds generated from rotten protein food. Thus, sensitive detection of the presence of amines in protein foods can be a critical technique for monitoring their quality. In this study, we develop an organic chemosensor probe, 4-(2-(3-(dicyanomethylene)− 5,5-dimethylcyclohex-1-en-1-yl)vinyl)-N,N-diethylbenzenaminium chloride (DEAH), to effectively detect amines through discernible bimodal (colorimetric and fluorometric) changes. By exploiting the amine-triggered intramolecular charge transfer behavior, DEAH exhibits rapid color changes (<1 s) with an excellent detection limit (36.9 nM) and also fluorescence turn-on in response to amine gas. Moreover, it possesses detection capabilities in versatile conditions, including solutions, solids, and coated films, suggesting its practical applicability. In particular, DEAH shows dramatic color change from yellow to violet with exceptional color difference (△Eab) over 98, repeatable usability, and excellent selectivity to amines. Based on these compelling advantages, we successfully demonstrate real-time monitoring of amine gas generated from spoiled protein foods using DEAH-coated films.

High-performance specific detection of NO2 based on PCN-222-Mn doped with Bi electron mediator

Continuous real-time monitoring of hazardous gases is essential for safeguarding public health and environmental integrity, necessitating the utilization of highly selective and sensitive gas sensors. Herein, the chemiresistive sensor for NO2 was fabricated based on Bi nanoparticles (NPs) doped Mn-porphyrin MOF PCN-222-Mn (Bi@PCN-222-Mn) through a straightforward in situ reduction process. Bi NPs serve as electron donors, facilitating the migration of electrons towards the PCN-222-Mn through the formation of a Schottky barrier caused by electrostatic interaction. Besides the Mn-porphyrin as detecting site in PCN-222-Mn, the Bi NPs in Bi@PCN-222-Mn act as supportive sites, enabling NO2 adsorption and electron transfer to NO2 simultaneously, thus enhancing the response signal amplification. Consequently, Bi@PCN-222-Mn exhibited the specific sensing for NO2 with 5 times higher sensitivity (238.6 % ppm−1) in the range of 0.05–2 ppm and a lower limit of detection (LOD) (13 ppb) compared to PCN-222-Mn. Moreover, the Bi@PCN-222-Mn-based chemiresistive sensor also exhibited outstanding long-term stability and moisture resistance, applicable for the practical detection of NO2. These results prove the feasibility of improvement of gas detection performance by introducing metal NPs as electron mediator and an idea for the application of MOFs in gas sensing.

A study on PM2.5 concentration in Bangkok, Thailand: A case study of Bang Na Station

In contemporary times, air pollution has emerged as a pressing concern in major metropolises worldwide. Particulate matter, particularly PM2.5, has been identified as a key contributor to elevated pollution levels. While previous studies in Thailand have primarily focused on PM2.5 in agricultural, forestry, and industrial regions, they often examine its relationship with precursor gases (e.g., SO2, NOx, VOCs, and NH3) and hotspots. However, research pertaining to the capital city, Bangkok, remains limited due to its complex source composition and unnatural urban structure, leading to unique airborne conditions. This study seeks to explore the interplay between PM2.5, precursor gases, and meteorological factors in Bangkok. To assess the influence of precursor gases and meteorological variables on PM2.5 concentrations, correlation analysis and regression techniques were applied to monitoring data obtained from relevant government agencies. Notably, PM2.5 exhibited strong correlations with precursor gases, especially NO2 (correlation coefficient, R, ranging from 0.11 to 0.87), while SO2 showed more variable correlations (R ranging from -0.45 to 0.85). Furthermore, meteorological factors exhibited significant but slightly weaker correlations with PM2.5 compared to SO2 and NO2. This suggests that NO2 plays a dominant role in driving the secondary formation of PM2.5 in the Bang Na area. Regression analysis confirmed the strong association of NO2, SO2, and relative humidity with PM2.5, while other meteorological parameters displayed less significance, even the planetary boundary layer. Contrary to previous studies that primarily rely on real-time monitoring for short durations and emphasize potential pollution sources, our research underscores the pivotal role of precursor gases, particularly under high relative humidity conditions. To elucidate the secondary formation of PM2.5 from precursor gases within urban settings, future studies should encompass longer-term real-time monitoring of both precursor gases and meteorological variables, especially in urban areas

Communication performance optimization of coal mine goaf LoRa AD hoc network sensor system based on tree topology

Real-time accurate monitoring of goaf gas, temperature, and other parameters is of key significance for the prevention and control of spontaneous coal combustion in the goaf. In this paper, a tree network and communication protocol for LoRa technology are proposed to solve the problems of small communication coverage and short endurance of the traditional goaf wireless monitoring system. In this paper, a geometric topology structure is designed for wireless sensor nodes to find relay nodes through the path loss model, and an energy-saving optimization algorithm routing scheme is designed. In addition, LoRa AD hoc network experiment platform and upper computer system are developed and designed. Through numerical analysis and experimental results in coal mines, it is proved that the proposed method significantly reduces the energy consumption of the whole network. The coverage radius is increased by 300% compared with the traditional star wireless sensor system, which makes the endurance time and communication quality have higher reliability.

An Eco-Friendly and Dual-Functional Ag@BMO-Chitosan-Based Triboelectric Nanogenerator for Self-Powered Gas Sensing Application

Real-time monitoring of hazardous gases is critical to determine the influence of the air environment on the lives of people. Herein, a highly biodegradable chitosan-based triboelectric gas sensor has been successfully fabricated by using Ag@Bi2MoO6-Chitosan (Ag@BMO-CS) bilayer film obtained with the facile drop-casting method, which paired with polydimethylsiloxane (PDMS) film as triboelectric layers. The output voltage of the 5% Ag-modified chitosan bilayer-based triboelectric nanogenerator reaches up to 8.35 V at 2 Hz operating frequency. The integrated self-powered gas sensor based on the triboelectrification and gas adsorption effects shows the ability to distinguish ammonia (NH3) gas sensitively, and the response to 20 ppm NH3 (ΔV/Va = 59.1%) is 1.4 times larger than the separated self-powered sensor. In addition, the sensing mechanism combining the triboelectric effect and gas-sensing reaction was proposed to demonstrate the enhancement of NH3 sensing performance for Ag@BMO-CS-based triboelectric gas sensors. This work presents a sustainable self-supply gas sensor based on TENG for the detection of NH3 gas without any external power supply, which has a broad spectrum of applications in hazardous gas leaks, non-invasive diagnostics and fresh food transportation.

Maltodextrin-overlaid polyvinyl acetate nanofibers for highly sensitive and selective room-temperature ammonia sensors

Various ammonia sensors based on different materials have continuously been developed and employed to enable real-time monitoring of ammonia gas in the environment. Efforts are put not only to improve their sensitivity and selectivity towards the target gas but also to operate them at room temperature. Here, we investigated the effect of overlaying maltodextrin with different concentrations on the surface of polyvinyl acetate (PVAc) nanofibers on ammonia sensing performances, in which quartz crystal microbalance (QCM) was utilized as a transducer to measure the resonance frequency shift affected by the adsorbed gas molecules. Higher concentrations of the overlaying maltodextrin led to larger nanofiber diameter and more functional active groups on the active nanofibrous layers. PVAc nanofibers with 0.05% maltodextrin overlay demonstrated the highest sensitivity of 0.525 Hz·ppm−1 at room temperature, which was 6.4 times higher than their bare counterpart (nanofiber without maltodextrin overlay). That sensor also possessed fast response and recovery times of 32 s and 17 s with a low detection limit (1.92 ppm). Besides its high reproducibility, reversibility, and repeatability, the sensor exhibited outstanding selectivity to other gas analytes and good long-term stability for 32 days of testing. This research shows that maltodextrin overlay can be used as a low-cost alternative route to increase the performance of organic material-based ammonia sensors, especially polymer nanofibers.

Optical CO2 gas sensor based on liquid crystals in a textile grid

Given the increasing concerns about global warming, it is undeniable that measuring and controlling carbon dioxide (CO2) levels, a colorless and odorless greenhouse gas, is of great value. In this respect, liquid crystals (LCs) as an anisotropic material hold promise for fabricating such gas sensors. Here, we report a sensitive optical gas sensor for real-time monitoring of CO2 gas, exploiting a textile grid impregnated with LC and diethanolamine (DEA) as a CO2-sensitive material. The sensing mechanism relies on the reorientation of LC molecules upon the interaction of gas analytes with DEA. By tracing optical texture changes and extracting the corresponding intensities, CO2 gas concentrations ranging from 300 to 10,000 ppm were detected. The sensor exhibits a response time of 12 seconds and a recovery time of 7 seconds at 800 ppm. The sensor is simple and cost-effective.

Location and Complex Status Update Strategy Optimization in UAV-Assisted IoT

Complex status updates have attracted widespread attention in real-time monitoring services (e.g., real-time fire gas monitoring and wildfire spread prediction). In complex status updates, the status information needs to be obtained by processing the perceived original data. However, as lightweight terminals, temporarily deployed Internet of things (IoT) devices have no computing modules. Unmanned aerial vehicle (UAV) can act as a edge server to help IoT devices complete computing tasks by mobile edge computing (MEC). To this end, this paper considers a complex status update in UAV-assisted IoT, where an UAV moves in hovering-flight-hovering mode to ensure that it can serve IoT devices in different areas. When the UAV hovers, it obtains the status information based on the original data transmitted by the IoT device and sends it to the control center. During the complex status update, the short packet communication and time-varying channel are considered. To realize the trade-off optimization of the average age of information (AoI) and average power consumption of both IoT device and UAV within a long time, we formulate a location and dynamic status update strategy optimization problem for UAV hovering-flight-hovering mode. In order to solve the problem with Markov properties, we derive the state probability equations and further establish the linear programming problem with fixed UAV location. Then, we propose a probability-based algorithm to obtain UAV location and dynamic status update strategy. To adapt to more urgent scenarios, we propose a AoI threshold based strategy to reduce the complexity of the problem. State probability equations are derived under the strategy and a linear programming problem with a fixed AoI threshold are established. Next, we propose a low-complexity algorithm to obtain the optimal AoI threshold. Simulation results show that the proposed algorithms can optimize the three performance metrics in a balanced way and we need to select the appropriate transmit power of the IoT device and the computing capacity of the UAV to achieve better performance.

A Visual Color Response Test Paper for the Detection of Hydrogen Sulfide Gas in the Air.

Hydrogen sulfide (H2S) is widely found in oil and natural gas wells and industrial wastewater tanks. Owing to its high toxicity, the monitoring and detection of H2S in the air is essential. However, recent techniques for the quantitative detection of H2S gas suffer from limitations such as high cost, complicated operation, and insufficient sensitivity, preventing their practical application in industry. Thus, we have developed a portable test paper for real-time and inexpensive monitoring of H2S gas by color changes. The test paper had a significantly low H2S detection limit of 200 ppb, which is considered safe for humans. Moreover, the color of the test paper did not change noticeably when exposed to CO2, N2, O2, and air environments, indicating that the test paper is selective for H2S gas and can be stored for a long time. In addition, we fitted a color difference linear model between the color difference values (ΔE) and the concentrations of H2S gas. The establishment of the linear model substantiates that the test paper can provide accurate intensity information when detecting H2S gas leakage.

Fourier spectral resolution enhancement algorithm based on linear prediction

Fourier transform infrared spectroscopy (FTIR) has been widely used for real-time monitoring of ambient gases, but for quantitative analysis of certain gases, higher spectral resolution is required to quantify more accurately. In this paper, a Fourier spectral resolution enhancement algorithm based on linear prediction is proposed. Firstly, the linear prediction model for interference signals is studied, utilizing the forward and backward linear prediction total least squares method for parameter estimation, minimum error criterion for determining the autoregressive (AR) model order, and sliding window technique for multi-step prediction. Then, the impact of the signal-to-noise ratio, enhancement factor, and initial resolution of the FTIR spectrum on the accuracy of the algorithm was studied through simulations. Finally, the algorithm is applied to spectral resolution enhancement and quantitative analysis of ammonia (NH3). The experimental results show that when the spectral resolution of NH3 is enhanced from 4 cm−1 to 2 cm−1 and 1 cm−1, the spectral error of the characteristic band is 0.16% and 0.26%, respectively. Additionally, the accuracy of concentration inversion is improved by 2.13% and 4.06%, respectively. The relative error of concentration inversion is 0.69% and 2.15%, respectively, when compared to the measured spectrum at corresponding resolutions. The algorithm proves effective in enhancing spectral resolution and improving the accuracy of quantitative analysis.

Electronic nose based on multiple electrospinning nanofibers sensor array and application in gas classification

To mimic the human olfactory system, an electronic nose (E-nose, also known as artificial olfactory) has been proposed based on a multiple gas sensor array and a pattern recognition algorithm. Detection of volatile organic components (VOCs) has many potential applications in breath analysis, food quality estimation, and indoor and outdoor air quality monitoring, etc. In this study, a facile single-needle electrospinning technology was applied to develop the four different semiconductor metal oxide (MOS) nanofibers sensor arrays (SnO2, CuO, In2O3 and ZnO, respectively). The array shows a smooth surface and constant diameter of nanofiber (average of 150 nm) resulting in high sensitivity to multiple target analyte gases. Five human health related VOCs gases were measured by fabricated E-nose and different response patterns were obtained from four MOS nanofibers sensors. Combined with feature extraction from the response curves, a principal component analysis (PCA) algorithm was applied to reduce the dimension of feature matrix, Thus, the fabricated E-nose system successfully discriminated five different VOCs gases. Real-time and non-invasive gas monitoring by E-nose is very promising for application in human health monitoring, food monitoring, and other fields.