Hazardous Gases Research Articles

Covalent-Organic-Framework-Modified Quartz Crystal Microbalance Sensor for Selective Detection of Hazardous Formic Acid.

Covalent organic frameworks (COFs) are a novel family of porous crystalline materials utilized in various advanced applications. However, applying COFs as a hazardous organic acid gas sensor is substantial but still challenging. Herein, a phenylenediamine-based covalent organic framework (TPDA-TPB COF) featuring excellent crystallinity, ultrastable thermal stability, and high surface area was successfully constructed. Then, the TPDA-TPB COF-modified quartz crystal microbalance (QCM) sensor is fabricated by immobilizing the TPDA-TPB COF thin film on the gold-QCM chip. The fabricated TPDA-TPB COF-modified QCM sensor demonstrates a rapid response, excellent reproducibility, high selectivity, and sensitivity to formic gas, arising from hydrogen-bonding interactions between formic acid and the outermost layer of the TPDA-TPB COF, as determined by extensive analysis and density functional theory calculations. The basic sites of the TPDA-TPB COF, which are numerous due to its high nitrogen content, and the carboxylic acid groups present in formic acid exhibit efficient interactions. The sensitivity of the TPDA-TPB COF-modified QCM sensor was found to be 7.75 Hz ppm-1 at standard room temperature and pressure conditions, with a limit of detection (LOD) of formic acid down to 1.18 ppm, which is significantly below the workplace olfactory threshold limit of 5.0 ppm established by the Occupational Safety and Health Administration. The TPDA-TPB COF-modified QCM sensor exhibits remarkable detecting capabilities, making it highly attractive for detecting organic acid vapors in diverse applications that require superior performance.

A plasma partial oxidation approach for removal of leaked LPG in confined spaces

Effective removal of leaked Liquefied petroleum gas (LPG) in poorly ventilated confined spaces remains a huge challenge in emergency response. In this paper, a plasma partial oxidation (PPO) approach was proposed to remove the leaked hazardous gases in confined spaces. A double dielectric barrier discharge (DDBD) reactor with a discharge volume of 13.56 ml was used at an initial Butane (simulate LPG) concentration of 5 % to study the effects of discharge power, gas flow rate and its co-action on the reaction performance, and the feasibility of the removal approach was demonstrated. The main hazardous gases in the exhaust are Butane and CO. By establishing the functional relationship of Butane concentration and CO concentration to energy density, it was calculated that the 5 % initial Butane concentration can be reduced to less than 1.9 % when the energy density (discharge power/gas flow rate) reaches 2.17 kJ/L, which eliminates the explosion risk of leaking Butane, and at the same time, the CO concentration is 0.8 %, which has a low CO toxicity. This paper provides a new approach for removing leaked LPG, which is expected to be practically applied in the disposal of hazardous gas leakage accidents in confined spaces.

Investigation of SnO2-based Thick Film Gas Sensors through Optimization of Artificial Neural Networks Parameters for Hazardous Gas Detection

In the present study, a systematic approach for optimizing artificial neural network parameters for the detection of a greenhouse gas carbon di-oxide (CO2) through SnO2-based thick film gas sensors for a wide range of concentrations is presented. The un-doped and Pd-doped SnO2 thick film is used as the sensor. The approach includes dataset preparation, input and output variable selection, artificial neural network architecture selection, training, validation, hyperparameter tuning, and testing. The input variables were selected based on their relevance to the problem at hand, such as the concentrations of the gas. The output variables were the changes in resistance of the sensor in response to the corresponding input variables. The artificial neural network architecture was carefully chosen, considering factors like the number of layers, neurons per layer, activation functions, and learning rate. Using the prepared dataset, the artificial neural network was trained by adjusting the connection weights between neurons to minimize the disparity between actual and predicted sensor responses. After training, the accuracy of the artificial neural network and generalization ability were assessed using a separate dataset for validation. The approach in the presented study can be used for different types of gases, and can be utilized in various applications

Research on intelligent sensor for industrial hazardous gases monitoring based on Tc doped C3N using density functional theory

With the ongoing global industrialization, a considerable amount of hazardous gases is emitted into the atmospheric environment. Achieving intelligent monitoring of industrial hazardous gases is a crucial pathway toward realizing the modernization of electronic information. In this study, the use of Tc doped C3N nanosheet for the intelligent online monitoring of industrial hazardous gases (CO, NO2, O3) is firstly proposed. The doping and modification mechanism of Tc nanoparticle on the C3N surface are revealed. Simultaneously, the adsorption and sensing mechanisms of the three target gases on the surface of pure C3N and Tc doped C3N nanosheet is deeply elucidated through density of states, band structure, and differential charge density analysis. The results indicate that Tc particle can stably bind to the C3N surface, providing numerous active sites for the adsorption of target gases, enabling selective capture and scavenging of hazardous gases. Furthermore, the adsorption type for all three gases shifts from physical adsorption to physicochemical adsorption, with significant changes in the electronic signals during surface reactions, manifested by drastic alterations in the band structure and enhanced electron mobility. The adsorption capacity order for the three gases is O3 > NO2 > CO. The achievements of this work not only provide a theoretical foundation for the design and preparation of Tc doped C3N nanosensors, but also offer a new direction for environmental protection and intelligent detection in industrial applications.

IoT Based Detection and Alerting of Hazardous Gas Detection for the welfare of Sewer Labourers

IoT Based Detection and Alerting of Hazardous Gas Detection for the welfare of Sewer Labourers

Cyclodextrin-based host-guest hierarchical fire retardants: Synthesis and novel strategy to endow polylactic acid fire retardancy and UV resistance

β-Cyclodextrin (β-CD) with a cage-like supramolecular structure possesses the hydrophobic internal ring and external hydroxyl groups, which are beneficial for intramolecular interactions known as “host-guest” chemistry. This study presents a β-CD-based three-functions-in-one and host-guest fire retardant (βCD-MOF@Schiff base), which incorporates self-crosslinking Schiff base into its cavity and modification of its surface by metal-organic framework (MOF). With the presence of 5 wt% of βCD-MOF@Schiff base, the LOI value of PLA composites increased to 29 % and showed 15 %, 17 % and 62 % reductions in peak heat release rate (pHRR), total heat release (THR), and the yield of hazard gas carbon monoxide, respectively. The mode action of FR on fire retardation of PLA showed that the FR promoted the char formation with higher thermal stability and graphitization, and modified the decomposition path of PLA. Additionally, the PLA composites exhibited enhanced UV resistance in the UVA and UVB areas with improved UV absorbance and the UPF values improving and doubling. This work develops a new approach to preparing biodegradable FR, which simultaneously endows fire safety and anti-UV properties for PLA.

Pure and doped graphene as a suitable material for the detection of hazardous gases

The need for highly sensitive toxic gas sensors is significant, and achieving this sensitivity is possible. Researchers have utilized density functional theory (DFT) computations to examine adsorption of different gas molecules (COX2, where X is F, Cl, or Br) over Au-doped and pristine graphene. This investigation aims to determine possibility of employing Au-doped graphene as a basis for gas sensors. Multiple adsorption positions and orientations were examined for each gas molecule. The configuration that exhibited the highest stability was identified, and adsorption energy (Eads) values, considering van der Waals (vdW) interactions, have been computed using NCI analyses. In addition, the electronic properties, including LUMOHOMO orbital density and charge transfer, were analyzed to acquire a deeper understanding of process by which adsorption occurs. Findings indicated that gas molecules under investigation exhibited weak adsorption on pristine graphene, with low Eads values. In contrast, Eads values of every gas molecule on Au-doped graphene displayed varying degrees of increase. Notably, COX2 exhibited a high sensitivity to Au-doped graphene. Furthermore, in the effect of doping Au into graphene, the Eads absorption of COCl2 gas has increased from 0.155 to 1.028 eV. This indicates the strong tendency of Au-doped graphene to interact with gas COCl2 compared to two other COX2 gas species. This strong adsorption can be ascribed to significant substantial charge transfer (CT) between COCl2 and Au-doped graphene and orbital hybridization. The charge transfer amount of the doped states has increased by more than double compared to the pure state. The band gap of Au-doped graphene has decreased from 2.14 to 1.83 eV due to the absorption of gas COCl2, indicating the highest amount of reduction compared to other gases. Moreover, the enhanced electrical conductivity of Au-doped graphene renders it more valuable and sensitive in the context of sensing COX2 gases.

Multi-Domain Smart Safety Helmet

Motorcycle accidents and head injuries are critical concerns globally, especially where helmet non-compliance is prevalent. To address this, a multi-domain smart safety helmet is proposed for rider safety and accident prevention through advanced sensor technology. This smart helmet integrates sensors like an MQ-3 alcohol sensor and helmet detection sensors to ensure safety conditions before the motorcycle engine starts. If alcohol levels exceed a threshold, the ignition system disables, preventing intoxicated riding. Helmet detection promotes helmet use, reducing head injury risk. The scalable sensor infrastructure enables multi-domain applications beyond motorcycles. This helmet can integrate into industries like coal mining and firefighting. In coal mining, it monitors environmental conditions and worker vital signs. In firefighting, it detects hazardous gases and monitors firefighter status. During motorcycle operation, the helmet continuously monitors critical parameters—speed, tilt, and environment—providing immediate feedback on unsafe behaviours. In accidents, the helmet's accelerometer detects impacts, activating GPS to pinpoint the location and GSM to alert emergency contacts. This smart helmet aims to enhance motorcycle safety, prevent accidents, and expedite emergency responses. It represents progress towards reducing motorcycle-related injuries and fatalities, with adaptable features for broader safety applications across industries and domains.

Design of adaptive hybrid MPPT controllers with universal input voltage DC–DC converter for RES’s

At present, conventional energy production is absent because of the more hazardous gases released into the environment, the high effect on human health, more cost required for maintenance, plus less usefulness for highly populated areas. So, the Renewable Energy Sources are more focused for the present automotive industry application. In this work, the Proton Exchange Membrane Fuel Stack is considered for analyzing the proposed DC–DC converter circuit. The advantages of this fuel stack are high energy density, fast functioning nature, more robustness, and more usefulness for the various water membrane conditions of the fuel stack. However, the disadvantages of the fuel stack are excessive current generation, plus more current conduction losses. So, the wide voltage supply single switch power converter is introduced in this work for optimizing the current production of the fuel stack network. The merits of this converter circuit are high stability, good reliability, low voltage appearing across the switches, plus a uniform power supply. Here, the converter switching pulses are obtained by proposing the Modified Continuous Step Change Adaptive Fuzzy Logic with Grey Wolf Optimization hybrid controller. This controller provides high maximum power extraction efficiency from the fuel stack which is equal to 99.421%. Also, this controller's Maximum Power Point Tracking time is 0.0285 s.

Biologically templated Fe2O3–CuO heterojunction for ppb-level styrene gas detection

The widespread industrial use of styrene highlights the urgent need for detecting and monitoring its harmful gases. The innovation of this study lies in synthesizing uniform Fe2O3 nanoparticles using Ginkgo biloba and calcining them with CuOHF to prepare nanostructured materials, enhancing the performance of gas sensors. The effects of varying α-Fe2O3 additions and calcination temperatures on the structure and gas-sensitive properties of the composites were systematically investigated. The results revealed that the optimal operating temperatures of the CuO–10%Fe2O3-400 sensor were reduced by 80 and 50 °C, and its response to 100 ppm styrene at 200 °C was significantly enhanced by 3.2-fold and 4.2-fold, respectively, compared to the single Fe2O3 and CuOHF sensors. It is particularly noteworthy that the sensor exhibits excellent detection performance at a 50 ppb styrene concentration, with a response value of 1.2. The lamellar structure of CuOHF serves as a precursor for CuO, thereby increasing the surface area of the sensor in contact with the gas. During the calcination process, the released gases contribute to the formation of a porous structure, which together improves the adsorption capacity and stability of the sensor. Additionally, the heterojunction formed by the calcination of CuO (p-type) and Fe2O3 (n-type) enhances charge transfer and increases gas adsorption capacity, highlighting its potential as a gas sensor. This composite material exhibits good sensitivity and reliability in monitoring hazardous gases like styrene, underscoring the significant value of this study in environmental monitoring and safety control.

Synthesis and Characterization of Calcium Oxide Nanoparticles (CaO NPS) from Snail Shells Using Hydrothermal Method

Calcium oxide (CaO) holds significant importance as a catalyst and effective chemisorbent for hazardous gases. This study presents the synthesis of CaO nanoparticles (NPs) using the hydrothermal technique with snail shells' calcium carbonate (CaCO3) as the starting material. The hydrothermal method offers several advantages over alternative approaches for producing metal oxide NPs, including its simplicity, cost-effectiveness, and ability to operate at low temperatures and pressures. By utilizing waste materials like snail shells as a precursor, the entire process becomes more economical, environmentally friendly, and sustainable. The synthesized NPs were analyzed using various techniques, including Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), the Barrett-Joyner-Halenda (BJH) model for pore structure quantification, Brunauer-Emmett-Teller (BET) for surface area calculation, and thermo gravimetric analysis (TGA/DTA-DSC). XRD analysis confirmed that the size of the synthesized CaO NPs was 43.14 nm, determined using the Debye-Scherrer equation. The transmission electron microscopy (TEM) image provided valuable insight into the morphology of the nano-catalyst. The analysis revealed that the nano-catalyst displayed a spherical shape, with an average particle size measuring 50 nanometers. The FTIR and XRD results unequivocally demonstrated the successful conversion of calcium carbonate (CaCO3) derived from snail shells into calcium oxide (CaO). TGA exhibited a significant weight loss peak at 700 °C, indicating the transformation of CaCO3 into CaO. The DTA-DSC curve exhibited sharp endothermic peaks at 700 °C, suggesting a decomposition reaction and the formation of a new compound. SEM images displayed porous, rough, and fragile surfaces that became agglomerated at higher temperatures. In other words, the FE-SEM images of NPs illustrated that the particles were predominantly spherical in morphology. Hence, waste snail shells hold promise as a valuable source of calcium for various applications in different fields.

Synergistic Effect of ZIF-8 and Pt-Functionalized NiO/In2O3 Hollow Nanofibers for Highly Sensitive Detection of Formaldehyde.

A rapid and accurate monitoring of hazardous formaldehyde (HCHO) gas is extremely essential for health protection. However, the high-power consumption and humidity interference still hinder the application of HCHO gas sensors. Hence, zeolitic imidazolate framework-8 (ZIF-8)-loaded Pt-NiO/In2O3 hollow nanofibers (ZPNiIn HNFs) were designed via the electrospinning technique followed by hydrothermal treatment, aiming to enable a synergistic advantage of the surface modification and the construction of a p-n heterostructure to improve the sensing performance of the HCHO gas sensor. The ZPNiIn HNF sensor has a response value of 52.8 to 100 ppm HCHO, a nearly 4-fold enhancement over a pristine In2O3 sensor, at a moderately low temperature of 180 °C, along with rapid response/recovery speed (8/17 s) and excellent humidity tolerance. These enhanced sensing properties can be attributed to the Pt catalysts boosting the catalytic activity, the p-n heterojunctions facilitating the chemical reaction, and the appropriate ZIF-8 loading providing a hydrophobic surface. Our research presents an effective sensing material design strategy for inspiring the development of cost-effective sensors for the accurate detection of indoor HCHO hazardous gas.

Beyond the Spectrum: Unleashing the Potential of Infrared Radiation in Poultry Industry Advancements.

The poultry industry is dynamically advancing production by focusing on nutrition, management practices, and technology to enhance productivity by improving feed conversion ratios, disease control, lighting management, and exploring antibiotic alternatives. Infrared (IR) radiation is utilized to improve the well-being of humans, animals, and poultry through various operations. IR radiation occurs via electromagnetic waves with wavelengths ranging from 760 to 10,000 nm. The biological applications of IR radiation are gaining significant attention and its utilization is expanding rapidly across multiple sectors. Various IR applications, such as IR heating, IR spectroscopy, IR thermography, IR beak trimming, and IR in computer vision, have proven to be beneficial in enhancing the well-being of humans, animals, and birds within mechanical systems. IR radiation offers a wide array of health benefits, including improved skin health, therapeutic effects, anticancer properties, wound healing capabilities, enhanced digestive and endothelial function, and improved mitochondrial function and gene expression. In the realm of poultry production, IR radiation has demonstrated numerous positive impacts, including enhanced growth performance, gut health, blood profiles, immunological response, food safety measures, economic advantages, the mitigation of hazardous gases, and improved heating systems. Despite the exceptional benefits of IR radiation, its applications in poultry production are still limited. This comprehensive review provides compelling evidence supporting the advantages of IR radiation and advocates for its wider adoption in poultry production practices.

Interface engineering of 2D/2D MoS2/In2S3 heterostructure for highly sensitive NO2 detection at room temperature gas sensor

Rapid industrial growth and technological advancements in recent decades have led to an urbanized society at the expense of the environment due to toxic gas emissions. Therefore, it is imperative to detect trace concentrations of harmful gas with reliable techniques. NO2 is essentially being targeted for detection among all other gases due to its enormous abundance and serious health risks. Recently, 2D-MoS2 with enormous adsorption sites and high chemical reactivity have been employed selectively to detect NO2 at room temperature (RT). Herein, uniform and high surface area MoS2/In2S3 nanosheets (NSs) heterostructures were synthesized by hydrothermal method. HR-TEM analysis confirmed the 2D/2D heterostructures of MoS2/In2S3 nanosheets (NSs), and the increase in surface area of MoS2 from 10 m2/g to 27.2 m2/g upon addition of In2S3, measured through BET analysis. The MoS2/In2S3 sensor demonstrated excellent sensing capability with a response of 185% towards 50 ppm of NO2 at RT (30 ℃). Additionally, the sensor exhibited stable detection for about 42 days, repeatability over 5 cycles, and a detection limit of 450 ppb towards NO2. The synergistic influence of 2D/2D heterostructure with significant active sites and surface area is responsible for the considerable improvement in sensing performance. Therefore, the MoS2/In2S3 nanocomposites demonstrate a facile approach for developing high-performance potentially hazardous gas detectors for RT applications.

Autonomous Artificial Olfactory Sensor Systems with Homeostasis Recovery via a Seamless Neuromorphic Architecture.

Neuromorphic olfactory systems have been actively studied in recent years owing to their considerable potential in electronic noses, robotics, and neuromorphic data processing systems. However, conventional gas sensors typically have the ability to detect hazardous gas levels but lack synaptic functions such as memory and recognition of gas accumulation, which are essential for realizing human-like neuromorphic sensory system. In this study, a seamless architecture for a neuromorphic olfactory system capable of detecting and memorizing the present level and accumulation status of nitrogen dioxide (NO2) during continuous gas exposure, regulating a self-alarm implementation triggered after 147 and 85 s at a continuous gas exposure of 20 and 40ppm, respectively. Thin-film-transistor type gas sensors utilizing carbon nanotube semiconductors detect NO2 gas molecules through carrier trapping and exhibit long-term retention properties, which are compatible with neuromorphic excitatory applications. Additionally, the neuromorphic inhibitory performance is also characterized via gas desorption with programmable ultraviolet light exposure, demonstrating homeostasis recovery. These results provide a promising strategy for developing a facile artificial olfactory system that demonstrates complicated biological synaptic functions with a seamless and simplified system architecture.

Multi unmanned aerial vehicle odor source location based on improved artificial fish swarm algorithm

Odor source location technology has important application value in environmental monitoring, safety emergency and search and rescue operations. For example, it can be used in post-disaster search and rescue, detection of hazardous gas leakage, and fire source detection. Existing odor source location methods have problems such as low search efficiency, inability to adapt to complex environments, and inaccurate odor source location. In this study, based on unmanned aerial vehicle technology and using swarm intelligence optimization algorithm, an improved artificial fish swarm algorithm (IAFSA) is proposed by combining curiosity in psychology on the basis of retaining the good optimization performance of the artificial fish swarm algorithm. The algorithm quantifies the curiosity of artificial fish searching high-concentration areas through a model, dynamically adjusts the artificial fish's field of vision and step length with the calculated curiosity factor, and avoids the oscillation phenomenon in the later stage of the algorithm. Simulation results show that the IAFSA has a higher success rate and smaller location error. Finally, odor source location experiments were carried out in an indoor physical environment, the feasibility of the odor source location method proposed in this study is verified in actual scenarios.

Mechanism of adsorption and gas-sensing of hazardous gases by MoS2 monolayer decorated by Pdn (n=1–4) clusters

The adsorption behaviors of hazardous gases (NO2 and SO2) on MoS2 monolayer decorated by Pdn (n=1–4) clusters (Pdn-MoS2) are investigated by first-principles calculations, and the performance of Pdn-MoS2 gas sensors has also been studied in this work. Our results show that Pdn nanocluster can enhance the electrical conductivity of MoS2 monolayer with band gaps of Pdn-MoS2 (n=1–4) sharply decreased, and the adsorption performance of NO2 and SO2 with higher adsorption energy and larger charge transfer. Thus, Pdn-MoS2 show higher sensitive to NO2 and SO2 molecules compared with pristine MoS2. The band gaps of Pdn-MoS2 can be further decreased upon the adsorption of NO2 and SO2 (except for SO2/Pd4-MoS2), indicating Pdn-MoS2 (n=1–3) gas sensor shows improved gas-sensitive response to both NO2 and SO2, and Pd4-MoS2 shows improved gas-sensitive response only to NO2. The recovery time analysis indicates that Pdn-MoS2 (n=1–4) is applicable to be utilized as NO2 and SO2 scavenger at ambient temperature, and the recovery process can be realized simply by heating. Our results indicate that Pdn-MoS2 (n=1–4) monolayer is a favorable candidate as adsorbent for detection and removal of NO2 and SO2, and also provides guidance for further study of the MoS2 monolayer decorated by transition metal clusters.

Computational Studies on the Nitrogen-Doped Graphene Quantum Dots as Potential Sensor for Hazardous Gases

Computational Studies on the Nitrogen-Doped Graphene Quantum Dots as Potential Sensor for Hazardous Gases

Early Detection System for Gas Leakage and Fire in Smart Home Using IOT

Gas leakage is an important environmental, commercial and residential problem that threatens human health, safety and the environment. Traditional gas detection methods often lack the ability to track time and do not provide timely information, leading to accidents and damage. In this context, Internet of Things (IoT) technology has been found to provide a reliable solution for continuous monitoring and remote management of gas detection systems. This paper presents an IoT-based gas leak detection system to improve safety and reduce the risk of gas leakage. leak. The proposed system integrates gas sensors, microcontrollers, communication modules and cloud computing infrastructure to enable real-time monitoring, data analysis and remote monitoring. Gas detectors used in designated areas indicated the presence of hazardous gases, and data was collected directly from the 's central processing unit for analysis. The system provides rapid rescue and intervention by sending instant notifications via mobile applications in case of a gas leak. The main features of the proposed system are scalability, flexibility and interoperability, allowing integration with existing infrastructure and integration of different protocols. Additionally, the use of a cloud-based platform facilitates data storage, analysis and visualization, allowing partners to gain insight intogasleaks, processes and possible consequences

Vacancy–assisted exposed Sn atoms enhancing NO2 room temperature sensing of SnSe2 nanoflowers

NO2 is a hazardous gas extremely harmful to the ecosystem and human health, so effective detection of NO2 is critical. SnSe2 is a promising candidate for gas sensors owing to its unique layered configuration that facilitates the diffusion of gas molecules. Here, ultrathin self–assembled nanoflowers F–SnSe2 rich in defects were synthesized by a simple solvothermal method. It exhibits excellent gas sensing performances for NO2 at room temperature (25 °C), with a high gas sensing response of 8.6 for 1 ppm NO2 and a lower detection limit as low as 200 ppb, capable of sensitively detecting ppb–level NO2. DFT calculations revealed that the presence of Se vacancies assists the central Sn atoms to break through the shielding effect of the surface Se atoms and become exposed active sites. The higher reactivity leads to more charge transfer and higher adsorption energy, which strongly promoted the adsorption of NO2. This work verifies the important role of vacancies for the exposed active sites and provides new guidance for defect engineering to modulate the gas sensing performances of SnSe2.