Search Results (462)

The development of high-performance electrochemical sensors is crucial for ammonia–nitrogen detection. Therefore, in this study, we successfully prepared one ternary PtNiCo nanosheet via the one-step electrodeposition technique. The ratio of H₂PtCl₆·6H₂O, Ni(NO₃)₂·6H₂O and Co(NO₃)₂·6H₂O and electrodeposition time were controlled. Under optimal conditions, Pt₆Ni₂Co₂-2000 demonstrated outstanding electrocatalytic performance, exhibiting a high oxidation peak current of 45.27 mA and excellent long-term stability, retaining 88.09% of its activity after 12 h. Furthermore, the sensing performance of Pt₆Ni₂Co₂-2000 was evaluated, revealing high sensitivity (10.01 μA μM⁻¹), a low detection limit (0.688 µM), strong anti-interference capability, great reusability, great reproducibility, and remarkable long-term stability. Additionally, recovery tests conducted in tap water, lake water, and seawater yielded highly favorable results. This study demonstrated that designing Pt-based alloys can not only enhance the electrochemical performance of Pt but also serve as an effective strategy for improving electrocatalytic ammonia oxidation and ammonia–nitrogen detection. Full article

The emissions from biomass combustion systems have recently been the subject of increased attention. In addition to elevated concentrations of particulate matter and hydrocarbons (HCs) in the flue gas, significant levels of NO_x emissions occur depending on the used fuel, such as biogenic residues. In response to legal requirements, owners of medium-sized plants (≈100 kW) are now also forced to minimize these emissions by means of selective catalytic reduction systems (SCR). The implementation of a selective sensor is essential for the efficient dosing of the reducing agent, which is converted to ammonia (NH₃) in the flue gas. Preliminary laboratory investigations on a capacitive NH₃ sensor based on a zeolite functional film have demonstrated a high sensitivity to ammonia with minimal cross-influences from H₂O and NO_x. Further investigations concern the application of this sensor in the real flue gas of an ordinary wood-burning stove and of combustion plants for biogenic residues with an ammonia dosage. The findings demonstrate a high degree of agreement between the NH₃ concentration measured by the sensor and an FTIR spectrometer. Furthermore, the investigation of the long-term stability of the sensor and the poisoning effects of SO₂ and HCl are of particular relevance to the laboratory measurements in this study, which show promising results. Full article

Quantifying air emissions from livestock pastures remains challenging due to spatial variability and temporal fluctuations in emissions due to weather conditions. In this study we used a small unmanned aerial vehicle (sUAV) equipped with real-time sensors and an air sample collection system to directly measure carbon dioxide (CO₂), methane (CH₄), ammonia (NH₃), nitrous oxide (N₂O), nitrogen dioxide (NO₂), hydrogen sulfide (H₂S), total volatile organic compound (VOC), and particulate matter (PM₁, PM_2.5, PM₁₀) emissions across two dairy pastures, two beef pastures, and one sheep pasture in Wisconsin. Emission rates were calculated using the Lagrangian mass balance model and validated against ground-level dynamic flux chamber (DFC) measurements. UAV-based CO₂ concentrations showed a strong correlation with DFC measurements (R² = 0.86, RMSE = 21.5 ppm, MBE = +9.7 ppm). Dairy 1 yielded the highest emissions for most compounds, with average emission rates of 0.50 ± 0.28 g m⁻² day⁻¹ head⁻¹ for CO₂, 8.48 ± 2.75 mg m⁻² day⁻¹ head⁻¹ for CH₄, and 0.20 ± 0.60 mg m⁻² day⁻¹ head⁻¹ for NH₃. The sheep pasture, on the other hand, had the lowest CH₄ and NH₃ emission rates, averaging 0.35 ± 0.22 mg m⁻² day⁻¹ head⁻¹ and 0.02 ± 0.05 mg m⁻² day⁻¹ head⁻¹, respectively. Rainfall events (≥ 5 mm within five days of sampling) significantly elevated N₂O emissions (0.56 ± 0.40 vs. 0.13 ± 0.17 mg m⁻² day⁻¹ head⁻¹). Particulate matter emissions were significantly affected by forage density. PM_2.5 emission rates reached 1.25 × 10⁻⁴ g m⁻² day⁻¹ head⁻¹ under low vegetative cover. It was concluded that emissions were affected by both animal species and the environmental conditions. The findings of this study provide a foundation for further development of emission inventories for pasture-based livestock production systems. Full article

The monitoring of gases and vapors using portable instruments is critical in a variety of fields, such as industrial and household safety, environmental monitoring, process control, and national security, owing to gas pollution. In this study, we design a portable and simple two-dimensional photonic crystal microcavity sensor for detecting gases such as ammonia, methane, carbon monoxide, acetylene, ethylene, and ethane. The basic structure of the sensor consists of silicon rods arranged in a square lattice pattern in air. Waveguide input and output channels are realized by engineering line defects within the lattice structure. Moreover, the sensor’s performance is continuously optimized by adding point defects, introducing a ring cavity, and varying the radius of the dielectric rods in the microcavity. Using the transmission spectrum obtained from the output waveguide, the performance parameters of the gas sensor are calculated. Based on the simulation analysis, the optimized gas sensor exhibits excellent performance, achieving a sensitivity S of 932.43 nm/RIU and a quality factor Q of 2421.719. Full article

It is critical to provide proper environmental conditions in poultry houses to maintain birds’ health, boost productivity, and improve the overall economic viability of the poultry industry. Among the myriad of environmental elements, indoor air quality has been a determining factor that directly affects poultry well-being. Elevated concentrations of harmful gases—in particular Carbon Dioxide ($C{O}_2$), Methane ($C{H}_4$), and Ammonia ($N{H}_3$)—decomposition products of poultry litter, feed wastage, and biological processes have draconian effects on bird health, feed efficiency, the growth rate, reproduction efficiency, and mortality rate. Despite their importance, traditional air quality monitoring systems are often operated manually, labor intensive, and cannot detect sudden environmental changes due to the lack of real-time sensing. To overcome these limitations, this paper presents an interdisciplinary approach combining cloud computing, Artificial Intelligence (AI), and Internet of Things (IoT) technologies to measure real-time poultry gas concentrations. Real-time sensor feeds are transmitted to a cloud-based platform, which stores, displays, and processes the data. Furthermore, a machine learning (ML) model was trained using historical sensory data to predict the next-day gas emission levels. A web-based platform has been developed to enable convenient user interaction and display the gas sensory readings on an interactive dashboard. Also, the developed system triggers automatic alerts when gas levels cross safe environmental thresholds. Experimental results of $C{O}_2$ concentrations showed a significant diurnal trend, peaking in the afternoon, followed by the evening, and reaching their lowest levels in the morning. In particular, $C{O}_2$ concentrations peaked at approximately 570 ppm during the afternoon, a value that was significantly elevated (p < 0.001) compared to those recorded in the evening (~560 ppm) and morning (~555 ppm). This finding indicates a distinct diurnal pattern in $C{O}_2$ accumulation, with peak concentrations occurring during the warmer afternoon hours. Full article

The livestock sector significantly contributes to atmospheric emissions of various pollutants, such as ammonia (NH₃) and particulate matter of diameter under 2.5 µm (PM2.5) from activity and barn management. The objective of this study was to evaluate the reliability of low-cost sensors integrated with an IoT system for monitoring PM2.5 concentrations in a dairy barn. To this end, data acquired by a PM2.5 measurement device has been validated by using a high-precision one. Results demonstrated that the performances of low-cost sensors were highly correlated with temperature and humidity parameters recorded in its own IoT platform. Therefore, a parameter-based adjustment methodology is proposed. As a result of the statistical assessments conducted on this data, it has been demonstrated that the analysed sensor, when corrected using the proposed correction model, is an effective device for the purpose of monitoring the mean daily levels of PM2.5 within the barn. Although the model was developed and validated by using data collected from a dairy barn, the proposed methodology can be applied to these sensors in similar environments. Implementing reliable and affordable monitoring systems for key pollutants is crucial to enable effective mitigation strategies. Due to their low cost, ease of transport, and straightforward installation, these sensors can be used in multiple locations within a barn or moved between different barns for flexible and widespread air quality monitoring applications in livestock barns. Full article

Nowadays, nanosized mesoporous oxides are of increasing interest to scientists. They can be used as components of heterogeneous catalysts, for photo- and electrocatalysis, as gas sensors, etc. For instance, the desired properties in catalysts include a nano size and homogeneity of the particles that form the catalyst. The particle sizes of oxides are set at the initial stage of their formation, as precursors of precipitation in the context of wet chemistry. The creation of optimal conditions is possible through the use of homogeneous precipitation, where the precipitant is formed within the solution itself as a result of a hydrolysis reaction. The resolution of this issue involved the utilization of urea in our experimental setup, obtaining the hydrolysis products of ammonia and carbon dioxide. Consequently, precipitation reactions can be utilized to obtain hydroxides, carbonates, or hydroxy carbonates of metals. The precursors were calcined, obtaining nanosized mesoporous oxides, which can have a wide range of applications. Nanosized 0.1–50 nm metal oxides were obtained, including those aluminum, iron, indium, zinc, nickel, and cobalt. Full article

Ammonia, as a toxic and corrosive gas, is widely present in industrial emissions, agricultural activities, and disease biomarkers. Detecting ammonia is of vital importance to environmental safety and human health. Sensors based on MXene have become an effective means for detecting ammonia gas due to their unique hierarchical structure, adjustable surface chemical properties, and excellent electrical conductivity. This study reviews the latest progress in the use of MXene and its composites for the low-temperature detection of ammonia gas. The strategies for designing MXene composites, including heterojunction engineering, surface functionalization, and active sites, are introduced, and their roles in improving sensing performance are clarified. These methods have significantly improved the ability to detect ammonia, offering high selectivity, rapid responses, and ultra-low detection limits within the low-temperature range. Successful applications in fields such as industrial safety, food quality monitoring, medical diagnosis, and agricultural management have demonstrated the multi-functionality of this technology in complex scenarios. The challenges related to the material’s oxidation resistance, humidity interference, and cross-sensitivity are also discussed. This study aims to briefly describe the reasonable design based on MXene sensors, aiming to achieve real-time and energy-saving environmental and health monitoring networks in the future. Full article

Ammonia (NH₃) is one of the characteristic gases used to detect food spoilage. In this study, the 10 wt% Ag-NiFe₂O₄ nanocomposite was synthesized via the hydrothermal method. Characterization results from SEM, XRD, and XPS analyzed the microstructure, elemental composition, and crystal lattice features of the composite, confirming its successful fabrication. Under the optimal working temperature of 280 °C, the composite exhibited excellent gas-sensing properties towards NH₃. The 10 wt% Ag-NiFe₂O₄ sensor demonstrates rapid response and recovery, as well as high sensitivity, towards 30 ppm NH₃, with response and recovery times of merely 3 s and 9 s, respectively, and a response value of 4.59. The detection limit is as low as 0.1 ppm, meeting the standards for food safety detection. Additionally, the sensor exhibits good short-term repeatability and long-term stability. Additionally, density functional theory (DFT) simulations were conducted to investigate the gas-sensing advantages of the Ag-NiFe₂O₄ composite by analyzing the electron density and density of states, thereby providing theoretical guidance for experimental testing. This study facilitates the rapid detection of food spoilage and promotes the development of portable food safety detection devices. Full article

Heat stress has been identified as one of the main challenges for dairy production systems, particularly in the context of global warming. This one-year study aimed to evaluate the impact of heat stress on milk yield and composition in a dairy farm located in the Elvas region of Portugal. A pack of electronic sensors was installed in the lactating animal facilities, allowing continuous recording of environmental data (temperature, humidity, ammonia and carbon dioxide). Based on these data, the Temperature-Humidity Index (THI) was automatically calculated on a daily basis, with the values subsequently aggregated into 7-day moving averages and integrated with milk production records, somatic cell count, and milk fat and protein content. The results indicate a significant influence of THI on both milk yield and composition, particularly on protein and fat content. The relationships between the variables were found to be non-linear, which contrasts with some results described in the literature. These discrepancies may be related to genetic differences between animals, variations in diets, production levels, management conditions, or the statistical models used in previous studies. Dry matter intake proved to be an important predictive variable. These findings reinforce the importance of ensuring animal welfare through continuous environmental monitoring and the implementation of effective heat stress mitigation strategies in the dairy sector. Full article

Ammonia is emerging as a promising zero-carbon marine fuel due to its high hydrogen density, low storage pressure, and long-term stability, making it well-suited for supporting sustainable maritime energy systems. However, its adoption introduces serious safety challenges, as its toxic, flammable, and corrosive properties pose greater risks than many other alternative fuels, necessitating rigorous risk assessment and safety management. This study presents a comprehensive investigation of potential ammonia leakage scenarios that may arise during the fuel gas supply process within confined compartments of marine vessels, such as the fuel preparation room and engine room. The simulations were conducted using FLACS-CFD V22.2, a validated computational fluid dynamics tool specialized for flammable gas dispersion and explosion risk analysis in complex geometries. The model enables detailed assessment of gas concentration evolution, toxic exposure zones, and overpressure development under various leakage conditions, providing valuable insights for emergency planning, ventilation design, and structural safety reinforcement in ammonia-fueled ship systems. Prolonged ammonia exposure is driven by three key factors: leakage occurring opposite the main ventilation flow, equipment layout obstructing airflow and causing gas accumulation, and delayed sensor response due to recirculating flow patterns. Simulation results revealed that within 1.675 s of ammonia leakage and ignition, critical impact zones capable of causing fatal injuries or severe structural damage were largely contained within a 10 m radius of the explosion source. However, lower overpressure zones extended much further, with slight damage reaching up to 14.51 m and minor injury risks encompassing the entire fuel preparation room, highlighting a wider threat to crew safety beyond the immediate blast zone. Overall, the study highlights the importance of targeted emergency planning and structural reinforcement to mitigate explosion risks in ammonia-fueled environments. Full article

Ammonia (NH₃) detection in liquids and biological fluids is essential for monitoring environmental contamination and industrial processes, ensuring food safety, and diagnosing health conditions. Existing detection techniques are often unsuitable for point-of-care (POC) use due to limitations including complex sample handling, lack of portability, and poor compatibility with miniaturized systems. This study introduces a proof-of-concept for a compact, portable device tailored for POC detection of gaseous ammonia released from liquid samples. The device combines a polyaniline (PANI)-based chemoresistive sensor with interdigitated electrodes and a resistance readout circuit, enclosed in a gas-permeable hydrophobic membrane that permits ammonia in the vapor phase only to reach the sensing layer, ensuring selectivity and protection from liquid interference. The ink formulation was optimized. PANI nanoparticle suspension exhibited a monomodal, narrow particle size distribution with an average size of 120 nm and no evidence of larger aggregates. A key advancement of this device is its ability to limit the impact of water vapor, a known source of interference in PANI-based sensors, while maintaining a simple sensor design. A tailored signal processing strategy was implemented, extracting the slope of resistance variation over time as a robust metric for ammonia quantification. The sensor demonstrated reliable performance across a concentration range of 1.7 to 170 ppm with strong logarithmic correlation (R² = 0.99), and very good linear correlations in low (R² = 0.96) and high (R² = 0.97) subranges. These findings validate the feasibility of this POC platform for sensitive, selective, and practical ammonia detection in clinical and environmental applications. Full article

Polyaniline (PANI) is an important conductive-polymer gas-sensing material with working temperature and mechanical flexibilities superior to those of conventional metal oxide sensing materials. However, its applicability is limited by its low sensitivity, high detection limits, and long response/recovery times. In this study, we prepared PANI/WS₂ composites via chemical oxidative polymerization and mechanical blending. A multilayer sensor structure—sequentially printed silver-paste heating electrodes, fluorene polyester insulating layer, silver interdigitated electrodes, and sensing material layer—was fabricated on a polyimide substrate via flexible microelectronic printing and systematically characterized using scanning electron microscopy, X-ray diffraction, and Fourier-transform infrared spectroscopy. The optimized 5 wt% WS₂ composite showed enhanced gas-sensing performance, with 219.1% sensitivity to 100 ppm ammonia (2.4-fold higher than that of pure PANI) and reduced response and recovery times of 24 and 91 s, respectively (compared to 81 and 436 s for pure PANI, respectively). Notably, the PANI/WS₂ sensor detected an ultralow ammonia concentration (100 ppb) with 0.104% sensitivity. The structural characterization and performance analysis results were used to deduce a mechanism for the enhanced sensing capability. These findings highlight the application potential of PANI/WS₂ composites in flexible gas sensors and provide fundamental insights for PANI-based sensing materials research. Full article

Unmanned aerial vehicles, also termed as unarmed aerial vehicles, are used for various purposes in and around the environment, such as delivering things, spying on opponents, identification of aerial images, extinguishing fire, spraying the agricultural fields, etc. As there are multi-functions in a single UAV model, it can be used for various purposes as per the user’s requirement. The UAVs are used for faster communication of identified information, entry through the critical atmospheres, and causing no harm to humans before entering a collapsed path. In relation to the above discussion, a UAV system is designed to classify and transmit information about the atmospheric conditions of the environment to a central controller. The UAV is equipped with advanced sensors that are capable of detecting air pollutants such as carbon monoxide (CO), carbon dioxide (CO₂), methane (CH₄), ammonia (NH₃), hydrogen sulfide (H₂S), etc. These sensors present in the UAV model monitor the quality of air, time-to-time, as the UAV navigates through different areas and transmits real-time data regarding the air quality to a central unit; this data includes detailed information on the concentrations of different pollutants. The central unit analyzes the data that are captured by the sensor and checks whether the quality of air meets the atmospheric standards. If the sensed levels of pollutants exceed the thresholds, then the system present in the UAV triggers a warning alert; this alert is communicated to local authorities and the public to take necessary precautions. The developed UAV is furnished with cameras which are used to capture real-time images of the environment and it is processed using the YOLO V3 algorithm. Here, the YOLO V3 algorithm is defined to identify the context and source of pollution, such as identifying industrial activities, traffic congestion, or natural sources like wildfires. Full article

Modern commercial poultry farming typically occurs indoors, where partial or complete environmental control is employed to enhance production efficiency. Maintaining optimal conditions, such as temperature, relative humidity, carbon dioxide, and ammonia levels, is essential for ensuring bird comfort and maximizing productivity. Monitoring the conditions of poultry houses requires reliable and intelligent management systems. This study introduces a Wireless Monitoring and Control System developed to regulate environmental conditions within poultry facilities. The system continuously monitors key parameters via a network of distributed sensor nodes, which transmit data wirelessly to a centralized control unit using Wi-Fi. The control unit processes the incoming data, stores it in a database, and adjusts actuators accordingly to maintain ideal conditions. A web-based dashboard allows users to monitor and control the environment in real time. Field testing confirmed the system’s effectiveness in keeping conditions optimal, supporting poultry welfare and operational efficiency. Full article