Search Results (768)

In industrial combustion processes, high concentrations of propane (C₃H₈) gas are employed. Therefore, developing gas-detecting devices that operate under high concentrations, elevated temperatures, and short response times is crucial. This paper presents the design, simulation, and construction of a novel propane (C₃H₈) gas detector. The design was based on the dynamic electrical response of a gas sensor fabricated with cobalt antimoniate (CoSb₂O₆). The simulation considered the device structure and programming criteria, and the final prototype was constructed according to the sensor response, design parameters, and operating principles. Design, simulation, and fabrication results were in concordance, confirming the correct operation of the detector at high gas concentrations. A mathematical model was derived from the sensor’s electrical response, establishing a resistance value that allowed a two-second response time. This resistance was used to adapt the signal between the gas sensor and the PIC18F2550 microcontroller. Input/output signals, safety criteria, and functionality principles were considered in the programming device. The resulting propane (C₃H₈) gas detector operates at 300 °C, detects high C₃H₈ concentrations, and achieves a 2 s response time, making it ideal for industrial applications where combustion monitoring is essential. Full article

Lithium-ion batteries (LIBs) face the safety hazard of thermal runaway (TR). Gas-sensing-based monitoring is one of the viable warning approaches for batteries during operation, and TR warning using semiconductor gas sensors has garnered widespread attention. This review presents a comprehensive analysis of the latest advances in this field. It details the gas release characteristics during the TR failure process and identifies H₂, electrolyte vapor, CO, CO₂, and CH₄ as effective TR warning markers. The core of this review lies in an in-depth critical analysis of gas-sensing materials designed for these target gases, systematically summarizing the design, performance, and application research of semiconductor gas-sensing materials for each aforementioned gas in battery monitoring. We further summarize the current challenges of this technology and provide an outlook on future development directions of gas-sensing materials, including improved selectivity, integration, and intelligent advancement. This review aims to provide a roadmap that directs the rational design of next-generation sensing materials and fast-tracks the implementation of gas-sensing technology for enhanced battery safety. Full article

This study quantifies energy consumption and tank-to-wheel (TTW) emissions of urban buses under varying traffic conditions and passenger loads in Osnabrück, Germany, to support emission-aware route assessment in sustainable mobility applications. Exemplary bus trajectories were modeled on a representative 6.17 km route of line M5 (18 m articulated bus; diesel and battery-electric) within a 22.31 km² traffic net using the Simulation of Urban MObility (SUMO) software, and were calibrated with traffic sensor data. To assess the influence of trajectories in different traffic situations, three different 90 min scenarios were compared (morning peak, noon, night). Trajectory-based energy consumption and greenhouse gas emissions were compared by using the SUMO-implemented emission models HBEFA and PHEMlight, as well as data from the literature. Both diesel and electric buses showed variations in energy consumption depending on the traffic conditions, with generally lower energy consumption for electric propulsion. Temporal differences in the TTW emissions of the diesel bus were modest, with slightly higher morning values, while spatial analysis showed PM peaks in pedestrian zones, NO_x peaks during acceleration phases, and CO₂ increases after stops and in low-speed areas. The results provide spatially resolved TTW factors for integration into routing applications, excluding upstream and non-exhaust processes in line with the defined system boundary. Full article

Two-dimensional carbide crystals (MXenes) are emerging as a promising platform for the development of novel gas sensors, offering advantages in energy efficiency and tunable analyte selectivity. One of the most effective strategies to enhance and tailor their functional performance involves forming hetero-structured composites with metal oxides. In this work, we explore a chemiresistive effect in double-metal MXene of Ti_0.2V_1.8C and its composites with 2 mol. % SnO₂ and Co₃O₄ nanocrystalline oxides toward feasibility tests with alcohol and ammonia vapor probes. The materials were characterized by simultaneous thermal analysis, X-ray diffraction analysis, Raman spectroscopy, and scanning/transmission electron microscopy. Gas-sensing experiments were carried out on composite layers deposited on multi-electrode substrates to be exposed to the test gases, 200–2000 ppm concentrations, at an operating temperature of 370 °C. The developed sensor array demonstrated clear analyte discrimination. The distinct sensor responses enabled a selective identification of vapors through linear discriminant analysis, demonstrating the further potential of MXene-based materials for integrated electronic nose applications. Full article

In this study, first-principles density functional theory (DFT) calculations were employed to investigate the effects of single and binary gas adsorption of NO, CO, CO₂, NO₂, H₂S, and O₃ on the ZnGa₂O₄(111) surface. For single-gas adsorption, O₃ adsorbed on surface Ga sites induces a pronounced work-function increase of 0.97 eV, whereas H₂S adsorption at surface O sites yields the strongest adsorption energy (−1.21 eV), highlighting their distinct electronic interactions with the surface. For binary co-adsorption, the NO₂-O₃ pair adsorbed at Ga-coordinated sites produces the largest work-function shift (1.88 eV), while adsorption at Zn sites results in the most stable configuration, with an adsorption energy reaching −3.98 eV. These results indicate that co-adsorption of highly electronegative gases can significantly enhance charge transfer and sensing response. In contrast, mixed oxidizing–reducing gas pairs, such as NO₂-H₂S, lead to a markedly suppressed work-function variation (−0.02 eV), suggesting reduced sensor sensitivity due to compensating charge-transfer effects. Overall, this work demonstrates that gas-sensing behavior on ZnGa₂O₄(111) is governed not only by individual gas–surface interactions but also by cooperative and competitive effects arising from binary co-adsorption, providing insights into realistic multi-gas sensing environments. Full article

Volatile organic compounds (VOCs) released from automotive interior materials and exchanged with external air seriously compromise cabin air quality and pose health risks to occupants. Electronic noses (E-noses) based on metal oxide semiconductor (MOS) micro-electro-mechanical system (MEMS) sensor arrays provide an efficient, real-time solution for in-vehicle gas monitoring. This review examines the use of SnO₂-, ZnO-, and TiO₂-based MEMS sensor arrays for this purpose. The sensing mechanisms, performance characteristics, and current limitations of these core materials are critically analyzed. Key MEMS fabrication techniques, including magnetron sputtering, chemical vapor deposition, and atomic layer deposition, are presented. Commonly employed pattern recognition algorithms—principal component analysis (PCA), support vector machines (SVM), and artificial neural networks (ANN)—are evaluated in terms of principle and effectiveness. Recent advances in low-power, portable E-nose systems for detecting formaldehyde, benzene, toluene, and other target analytes inside vehicles are highlighted. Future directions, including circuit–algorithm co-optimization, enhanced portability, and neuromorphic computing integration, are discussed. MOS MEMS E-noses effectively overcome the drawbacks of conventional analytical methods and are poised for widespread adoption in automotive air-quality management. Full article

This work presents results on laser-induced fabrication of metal and oxide structures on glass substrates. The Laser-Induced Reverse Transfer (LIRT) technique is applied using Zn and Sn, sintered ZnO and SnO₂, and oxide composite targets. The processing is performed by nanosecond pulses of a Nd:YAG laser system operated at wavelength of 1064 nm. Detailed analyses of the deposited material morphology, composition and structure are presented, as the role of the processing conditions is revealed. It is found that at the applied conditions of using up to five laser pulses, the deposited material is composed of a nanostructured film covered in microsized nanoparticle clusters or droplets. The use of metal targets leads to formation of structures composed of metal and oxide phases. The adhesion test shows that part of the deposited material is stably adhered to the substrate surface. It is demonstrated that the deposited materials can be used as resistive gas sensors with sensitivity to NH₃, CO, ethanol, acetone and N₂O, at concentrations of 30 ppm. The ability of the method to deposit composite structures that consist of a mixture of both investigated oxides is also demonstrated. Full article

rGO/ZnO composites have been widely studied for use as toxic gas sensors due to the synergistic effect between the materials and the reduction in sensor operating temperature promoted by rGO. However, few studies have employed rGO/ZnO sensors for ozone detection, as graphene materials are oxidized and/or degraded when exposed to ozone. This paper reports on a study of ZnO/rGO/ZnO-based sensors with different ZnO NP morphologies for ozone sensing. ZnO nanoparticles with needle-like and donut-like morphologies were synthesized by the precipitation method, and bare ZnO and ZnO/rGO/ZnO composite sensors were fabricated by layer-deposition of ZnO and/or rGO via drop-casting, forming a “sandwiched” structure that protects the rGO sheets. Bare ZnO and ZnO/rGO/ZnO composites were analyzed by varying the temperature from 200 to 300 °C. The ZnO/rGO/ZnO sensor provided a high 13.3 response (R_gas/R_air) and recovery times of 442 s and 253 s, respectively, for 50 ppb of O₃, as well as high selectivity to ozone gas compared to CO, NH₃, and NO₂ gases. No oxidation or degradation of the sensor was observed during ozone detection measurements, indicating that the adopted manufacturing methodology was successful. Full article

Current gene-based PCR diagnostics involving reverse-transcription polymerase chain reaction (RT-PCR) require at least several hours, expensive tools, and complicated sample collection methods to obtain results. A test for detecting volatile organic compounds (VOCs) in exhaled breath is advantageous as a simple, non-invasive, and fast screening method. In this study, a VOC detection system of array sensors was applied for the classification of breath control and COVID-19 virus infection. The ability to classify VOCs in the breath with COVID-19 virus infection has been studied with two metal-oxide (MOX) gas sensor arrays, commercially available sensors, and in-house sensors. The dataset of gas response signals from the array-type semiconductive gas sensors of the VOC detection system was analyzed using machine learning; principal component analysis (PCA) was used as a dimensionality-reduction method, and random forest (RF) and a convolutional neural network (CNN) were used as classification methods for the VOC concentration patterns in each breath. For the RF model, the accuracy results for the classification by two gas sensor arrays was 0.917 and this was improved by CO₂ calibration to 0.967, and the feature importance analysis revealed the importance of specific gas sensors. For the CNN, an input layer of a transformed gray-scale image with the shape of 12 data points × 8 sensors was used, and its accuracy reached 100% within a relatively small number of epochs, demonstrating a short training time, which is beneficial for breath detectors or e-nose devices. Full article

Octachlorinated metal phthalocyanines (MPcCl₈, M = Co, Zn, VO) represent an underexplored class of functional materials with promising potential for chemiresistive sensing applications. This work is the first to determine the structure of single crystals of CoPcCl₈, revealing a triclinic (P-1) packing motif with cofacial molecular stacks and an interplanar distance of 3.381 Å. Powder XRD, vibrational spectroscopy, and elemental analysis confirm phase purity and isostructurality between CoPcCl₈ and ZnPcCl₈, while VOPcCl₈ adopts a tetragonal arrangement similar to its tetrachlorinated analogue. Thin films were fabricated via physical vapor deposition (PVD) and spin-coating (SC), with SC yielding highly crystalline films and PVD resulting in poorly crystalline or amorphous layers. Electrical measurements demonstrate that SC films exhibit n-type semiconducting behavior with conductivities 2–3 orders of magnitude higher than PVD films. Density functional theory (DFT) calculations corroborate the experimental findings, predicting band gaps of 1.19 eV (Co), 1.11 eV (Zn), and 0.78 eV (VO), with Fermi levels positioned near the conduction band, which is consistent with n-type character. Chemiresistive sensing tests reveal that SC-deposited MPcCl₈ films respond reversibly and selectively to ammonia (NH₃) and hydrogen sulfide (H₂S) at room temperature. ZnPcCl₈ shows the highest NH₃ response (45.3% to 10 ppm), while CoPcCl₈ exhibits superior sensitivity to H₂S (LOD = 0.3 ppm). These results suggest that the films of octachlorinated phthalocyanines produced by the SC method are highly sensitive materials for gas sensors designed to detect toxic and corrosive gases. Full article

Photoacoustic spectroscopy (PAS) is a powerful technique for selective gas detection; however, its performance in non-resonant configurations is fundamentally constrained by the poor low-frequency response of conventional acoustic detectors. Commercial MEMS microphones, although compact and cost effective, exhibit limited infrasound sensitivity, which restricts the development of truly miniaturised and broadband PAS systems. To address this limitation, we present a novel MEMS fluidic microphone (f-mic) that operates on a thermal sensing principle and is explicitly optimised for the infrasound regime. The sensor demonstrates a constant sensitivity of 32 μV/Pa for frequencies below 20 Hz. A detailed analytical model incorporating frequency-dependent effects is developed to identify and investigate the critical design parameters that influence system performance. The overall system exhibits a band-pass frequency response, enabling broadband operation. Based on these insights, a miniaturised photoacoustic cell is fabricated, ensuring efficient optical coupling and f-mic integration. Experimental validation using a CO₂-targeted laser system demonstrates a linear response up to 5000 ppm, a sensitivity of 6 nV/ppm, and a theoretical detection limit of 300 ppb over 100 s, resulting in an NNEA of $6\times {10}^{-6}$ W cm⁻¹ Hz^−0.5. These results establish the f-mic as a robust, scalable solution for non-resonant PAS, effectively overcoming a significant bottleneck in compact gas sensing technologies. Full article

Anaerobic digestion (AD) is a nonlinear and disturbance-sensitive process in which instability is often induced by feedstock variability and biological fluctuations. To address this challenge, this study develops an entropy-guided machine learning framework that integrates parameter prediction, uncertainty quantification, and entropy-based evaluation of AD operation. Using six months of industrial data (~10,000 samples), three models—support vector machine (SVM), random forest (RF), and artificial neural network (ANN)—were compared for predicting biogas yield, fermentation temperature, and volatile fatty acid (VFA) concentration. The ANN achieved the highest performance (accuracy = 96%, F1 = 0.95, root mean square error (RMSE) = 1.2 m³/t) and also exhibited the lowest prediction error entropy, indicating reduced uncertainty compared to RF and SVM. Feature entropy and permutation analysis consistently identified feed solids, organic matter, and feed rate as the most influential variables (>85% contribution), in agreement with the RF importance ranking. When applied as a real-time prediction and decision-support tool in the plant (“sensor → prediction → programmable logic controller (PLC)/operation → feedback”), the ANN model was associated with a reduction in gas-yield fluctuation from approximately ±18% to ±5%, a decrease in process entropy, and an improvement in operational stability of about 23%. Techno-economic and life-cycle assessments further indicated a 12–15 USD/t lower operating cost, 8–10% energy savings, and 5–7% CO₂ reduction compared with baseline operation. Overall, this study demonstrates that combining machine learning with entropy-based uncertainty analysis offers a reliable and interpretable pathway for more stable and low-carbon AD operation. Full article

The current research presents the synthesis, characterization, and application of a novel gas sensor based on polypyrrole/strontium titanate (PPy/STO) nanocomposites for the selective detection of CO₂. Utilizing chemical oxidative polymerization, PPy and PPy/STO nanocomposites with varying STO (10–50) wt.% were synthesized and characterized. The structural and morphological analysis confirms the formation of spherical structure and well-dispersed PPy nanoparticles with increasing crystallinity and interaction of STO in PPy chain particle compactness as the STO content increases. The integration of perovskite STO within the conducting polymer matrix enhances the electronic structure, porosity, and surface area of the composite, promoting improved gas sensing performance. Electrical impedance spectroscopy reveals that the composites exhibit a frequency-dependent dielectric response and conduction attributed to charge carrier mobility and interfacial polarization effects. PPy/STO 20% exhibits highest conductivity and dielectric constants of 0.03604 Scm⁻¹ and 1.074 × 10⁴, respectively. Real-time CO₂ sensing experiments conducted at 50 °C demonstrate good sensitivity, stability, and rapid response/recovery characteristics, particularly for the PPy/STO 10% and 40% composites. These findings highlight the potential of PPy/STO nanocomposites as flexible, lightweight, and efficient materials for portable CO₂ gas sensors, addressing the growing needs for environmental and health monitoring. Full article

This study investigates the spatiotemporal changes in land use and land cover (LULC) in the Kağıthane basin, Istanbul, a region experiencing rapid urban growth, to assess its environmental sustainability. Sentinel-1 and Sentinel-2 satellite images processed on the Google Earth Engine (GEE) platform were used for 2017, 2020, and 2023. Optical data from Sentinel-2, after atmospheric and geometric corrections, combined with co- and cross-polarized radar backscatter from Sentinel-1, supported land cover classification. Additionally, 14 spectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Urban Index (UI), enhanced discrimination between classes. To estimate LULC projections for 2035, 2050, 2065, 2080, and 2095, the Modules for Land Use Change Evaluation (MOLUSCE) model was used, which integrates artificial neural networks with a cellular automata framework. Six driving variables, roads, streams, topographic parameters (elevation, slope, and aspect), and population density, were incorporated into multiple scenarios. Model performance was evaluated using overall accuracy, Kappa statistics, and confusion matrices, yielding strong results (91.88% accuracy; Kappa = 0.84). The simulations indicate a significant decline in forest cover and barren lands, while vegetation and built-up areas are projected to grow steadily, raising concerns about long-term urban sustainability. Water bodies are projected to remain relatively stable. Under these changes, future direct carbon emissions were estimated using carbon emission coefficients by land class. Indirect carbon emissions were estimated based on natural gas and electricity consumption data. Considering both direct and indirect emissions, the results indicate a decrease in carbon emissions from 2023 to 2035, followed by an increase of up to 13% between 2035 and 2095. These findings emphasize the importance of combining multi-sensor remote sensing data with spatially explicit modeling to accurately assess land use changes in rapidly urbanizing basins. The study emphasizes the critical need to adopt sustainability measures that address changes in carbon emissions and guide future urban planning towards a more sustainable path. Full article

The development of highly sensitive gas sensors for toxic industrial gases (TIGs) is paramount for environmental monitoring and public safety. Here, the first-principles calculations were employed to systematically investigate the potential of Pt- and Rh-decorated InS (Pt-InS and Rh-InS) monolayers as advanced gas sensing materials for the five TIGs (SO₂, NH₃, NO, CO, and NO₂). The results reveal that Pt and Rh atoms can be stably anchored at the InS monolayer, inducing significant modulation of its electronic properties. The Pt-InS system exhibits strong chemisorption of NH₃ and CO, while the other TIGs interact via physisorption. In contrast, the Rh-InS monolayer demonstrates strong chemisorption and distinct electronic responses to all five gases, driven by robust hybridization between the Rh-d and TIG-p orbitals. Based on comprehensive analyses of sensitivity and recovery time, Rh-InS is identified as a theoretically promising candidate for a reusable SO₂ sensor at room temperature, boasting a calculated rapid theoretical recovery time of 2.20 s. The Pt-InS system, conversely, shows potential for high-temperature NH₃ sensing. Our findings highlight the exceptional and tunable gas sensing capabilities of Pt- and Rh-decorated InS monolayers, offering a theoretical foundation for designing InS-based sensing devices. Full article