Search Results (116)

The conventional treatment of high-concentration volatile organic compounds (VOCs) relies on energy-intensive dilution to avoid explosion risks. This study proposes an efficient catalytic combustion process treating VOCs directly within the explosive range while recovering reaction heat using Pt/γ-Al₂O₃-based catalysts promoted with La and Ce. Catalysts (0.05–0.5 wt% Pt) were synthesized via impregnation and characterized using FE-SEM, BET, and XRD. Catalytic combustion experiments at VOC concentrations up to 13,000 ppm showed combustion initiation below 200 °C, achieving 83–99% conversions at 300 °C with complete oxidation to CO₂. Although 5 vol.% moisture significantly inhibited low-temperature activity through competitive adsorption, La and Ce promoters (10 wt%) effectively overcame this limitation by increasing surface area (up to 194.93 m²/g) and oxygen mobility. The Ce-promoted catalyst demonstrated superior water tolerance, achieving complete conversion at 200–210 °C due to its high Oxygen Storage Capacity (OSC). Bench-scale validation using a 1 Nm³/h system confirmed industrial feasibility. Operating at 220 °C with 13,000 ppm toluene for 100 h, the catalyst maintained >99.98% conversion with negligible deactivation and THC emissions below 2 ppm. The double-jacket heat exchanger effectively managed reaction heat (limiting temperature rise to ~20 °C) and recovered it as steam. Compared to Regenerative Thermal Oxidation, this Regenerative Catalytic Oxidation approach reduced emissions and energy consumption. This work demonstrates a robust “combustion-with-recovery” strategy for high-concentration VOC treatment, offering a sustainable alternative with high efficiency, stability, and safe energy-integrated operation. Full article

Ethanolamine (EA) is a widely used yet toxic volatile organic compound (VOC). However, existing gas sensors for EA detection face persistent challenges in achieving exceptional sensitivity and low detection limits at room temperature (RT). In this study, a novel and high-performance EA sensor based on the MoO₃/BiOI composite was prefabricated using hydrothermal and cyclic impregnation methods. The response value toward 100 ppm EA reached 861.3, which was 3.5-times higher compared to that of pure MoO₃. In addition, the MoO₃/BiOI composite exhibited a low detection limit (0.13 ppm), excellent selectivity, short response/recovery times, exceptional repeatability and long-term stability. The outstanding gas sensing performance of the MoO₃/BiOI is attributed to the formation of a p-n heterojunction, synergistic effects between the two materials, abundant adsorbed oxygen species and superior charge transfer efficiency. The sensor developed in this work effectively addresses the long-standing challenges, demonstrating unprecedented practical application potential for EA gas detection. Simultaneously, this study provides a novel strategy, a new approach and a promising material for the subsequent development of advanced amine sensors. Full article

Intelligent ventilation is positioned as a key axis for reconciling energy efficiency and indoor air quality (IAQ) in residential and non-residential buildings. This review synthesizes 51 recent publications covering control strategies (DCV, MPC, reinforcement learning), IoT architectures and sensor validation, energy recovery (HRV/ERV, anti-frost strategies, low-loss exchangers, PCM-air), active envelope solutions (thermochromic windows) and passive solutions (EAHE), as well as evaluation methodologies (uncertainty, LCA, LCC, digital twin) and smart readiness indicator (SRI) frameworks. Evidence shows ventilation energy savings of up to 60% without degrading IAQ when control is well-designed, but also possible overconsumption when poorly parameterized or contextualized. Performance uncertainty is strongly influenced by occupant emissions and pollutant sources (bioeffluents, formaldehyde, PM2.5). The integration of predictive control, scalable IoT networks, and robust energy recovery, together with life-cycle evaluation and uncertainty analysis, enables more reliable IAQ-energy balances. Gaps are identified in VOC exposure under DCV, robustness to sensor failures, generalization of ML/RL models, and standardization of ventilation effectiveness metrics in natural/mixed modes. Full article

This investigation highlights the importance of adopting ethical and sustainable practices in chicken farming, in response to the increasing global demand for poultry products driven by the expanding world population. How ambient gases, such as hydrogen sulfide ($H_{2}S$), nitrous oxide ($N_{2}O$), ammonia ($N{H}_3$), carbon dioxide ($C{O}_2$), and methane ($C{H}_4$), affect the welfare of farm workers and poultry is investigated. The use of various gas sensor technologies is crucial for effective management and monitoring of these gases. The research emphasizes the vital importance of precise gas concentration measurements in mitigating environmental impact. It is noteworthy that there is a closely intertwined relationship between $C{O}_2$ levels and chicken health, requiring vigilant monitoring and care. There are potential risks associated with $N{H}_3$ exposure, and waste management and ventilation practices are necessary. Furthermore, the contribution of $C{H}_4$ sensors to environmental sustainability and safety is addressed. The review also examines $H_{2}S$ emissions, providing mitigation strategies to safeguard avian health. This study identifies an important gap between the limited use of commercially available Metal Oxide Semiconductor (MOS) sensors in the commercial Internet of Things (IoT) systems for poultry farms and their potential to detect a wider range of chemical gases. The pivotal role played by gas sensors in these sustainable efforts is highlighted. Full article

Chemical sensors have undergone transformative advances in recent years, driven by the convergence of nanomaterials, advanced fabrication strategies, and state-of-the-art characterization methods. This review emphasizes recent developments, with particular attention to progress achieved over the past decade, and highlights the role of the United States as a major driver of global innovation in the field. Nanomaterials such as graphene derivatives, MXenes, carbon nanotubes, metal–organic frameworks (MOFs), and hybrid composites have enabled unprecedented analytical performance. Representative studies report detection limits down to the parts-per-billion (ppb) and even parts-per-trillion (ppt) level, with linear ranges typically spanning 10–500 ppb for volatile organic compounds (VOCs) and 0.1–100 μM for biomolecules. Response and recovery times are often below 10–30 s, while reproducibility frequently exceeds 90% across multiple sensing cycles. Stability has been demonstrated in platforms capable of continuous operation for weeks to months without significant drift. In parallel, additive manufacturing, device miniaturization, and flexible electronics have facilitated the integration of sensors into wearable, stretchable, and implantable platforms, extending their applications in healthcare diagnostics, environmental monitoring, food safety, and industrial process control. Advanced characterization techniques, including in situ Raman spectroscopy, X-ray Photoelectron Spectroscopy (XPS, Atomic Force Microscopy (AFM), and high-resolution electron microscopy, have elucidated interfacial charge-transfer mechanisms, guiding rational material design and improved selectivity. Despite these achievements, challenges remain in terms of scalability, reproducibility of nanomaterial synthesis, long-term stability, and regulatory validation. Data privacy and cybersecurity also emerge as critical issues for IoT-integrated sensing networks. Looking forward, promising future directions include the integration of artificial intelligence and machine learning for real-time data interpretation, the development of biodegradable and eco-friendly materials, and the convergence of multidisciplinary approaches to ensure robust, sustainable, and socially responsible sensing platforms. Overall, nanomaterial-enabled chemical sensors are poised to become indispensable tools for advancing public health, environmental sustainability, and industrial innovation, offering a pathway toward intelligent and adaptive sensing systems. Full article

Sample storage is a key factor in odour quantification. This study investigates the loss of odorous compounds in Nalophan™ sampling bags during storage, simulating real-world transport and storage conditions. The goal was to quantify compound leakage over time by varying operational parameters to identify the most significant losses. The tested compounds—sulphur, oxygenated, and hydrocarbon VOCs—were prepared in the laboratory at 10 ppm. Tests were conducted on 12 L Nalophan™ bags with sampling intervals of 0, 6, 30, 48, and 100 h, exceeding the EN 13725 guideline limits (30 h). To evaluate recovery, environmental and internal humidity and temperature were varied. Additionally, the adsorption surface was increased by inserting Nalophan™ flakes inside the bags. The results show that under ambient conditions, losses during 30 h are contained and are within the order of instrumental uncertainty for all the tested compounds. A higher ambient temperature and humidity did not significantly affect recovery. In contrast, internal humidity appeared to have a more noticeable effect, particularly affecting low molecular weight sulphur compounds and oxygenates. These findings suggest optimal storage strategies for olfactometric samples, highlighting that significant losses do not occur within the EN 13725:2022 storage time limits. Moreover, even exceeding these time limits, the observed losses remain limited to 100 h. Full article

The recovery and control of volatile organic compounds (VOCs) have gained significant attention. Supported sulfonic acid materials show potential in converting aromatic VOCs into non-volatile sulfonic acid derivatives. However, their effectiveness is closely tied to the anchoring state of the sulfonic acid groups. In this study, two supported sulfonic acids, SSA@CdO and SSA@CaO, were synthesized via the respective reactions of CdO and CaO with chlorosulfonic acid to investigate how the properties of the supports influence sulfonic acid anchoring and reactivity toward o-xylene. Comprehensive characterization and performance tests revealed that sulfonic acid groups on CdO were covalently bonded, forming positively charged sites ([O_0.5Cd–O]^ɗ−–SO₃H^ɗ+) with high loading (9.7 mmol/g), enabling excellent o-xylene removal (≥95.6%) and adsorption capacity (51.67–91.59 mg/g) at 130–150 °C. In contrast, ion-paired bonding on CaO formed negatively charged sites ([O_0.5Ca]⁺:OSO₃H⁻), which were inactive in electrophilic sulfonation. This work provides new insights for enhancing supported sulfonic acid materials in VOC treatment. Full article

Soil degradation resulting from intensive agricultural practices, the excessive use of agrochemicals, and climate-induced stresses has significantly impaired soil fertility, disrupted microbial diversity, and reduced crop productivity. Plant growth-promoting bacteria (PGPB) represent a sustainable biological approach to restoring degraded soils by modulating plant physiology and soil function through diverse molecular mechanisms. PGPB synthesizes indole-3-acetic acid (IAA) to stimulate root development and nutrient uptake and produce ACC deaminase, which lowers ethylene accumulation under stress, mitigating growth inhibition. They also enhance nutrient availability by releasing phosphate-solubilizing enzymes and siderophores that improve iron acquisition. In parallel, PGPB activates jasmonate and salicylate pathways, priming a systemic resistance to biotic and abiotic stress. Through quorum sensing, biofilm formation, and biosynthetic gene clusters encoding antibiotics, lipopeptides, and VOCs, PGPB strengthen rhizosphere colonization and suppress pathogens. These interactions contribute to microbial community recovery, an improved soil structure, and enhanced nutrient cycling. This review synthesizes current evidence on the molecular and physiological mechanisms by which PGPB enhance soil restoration in degraded agroecosystems, highlighting their role beyond biofertilization as key agents in ecological rehabilitation. It examines advances in nutrient mobilization, stress mitigation, and signaling pathways, based on the literature retrieved from major scientific databases, focusing on studies published in the last decade. Full article

The recovery and abatement of volatile organic compounds (VOCs) have received increasing attention due to their significant environmental and health impacts. Supported sulfonic acid materials have shown great potential in converting aromatic VOCs into their non-volatile derivatives through reactive adsorption. However, the anchoring state of sulfonic acid groups, which is closely related to the properties of the support, greatly affects their performance. In this study, two supported sulfonic acid materials, SZO and SMO, were prepared by treating ZrO₂ and MgO with chlorosulfonic acid, respectively, to investigate the influence of the support properties on the anchoring state of sulfonic acid groups and their reactive adsorption performance for o-xylene. The supports, adsorbents, and adsorption products were extensively characterized, and the reactivity of SZO and SMO towards o-xylene was systematically compared. The results showed that sulfonic acid groups are anchored on the ZrO₂ surface through covalent bonding, forming positively charged sulfonic acid sites ([O_1.5Zr-O]^δ−-SO3H^δ+) with a loading of 3.6 mmol/g. As a result, SZO exhibited excellent removal efficiency (≥91.3%) and high breakthrough adsorption capacity (ranging from 38.59 to 82.07 mg/g) for o-xylene in the temperature range of 130 –150 °C. In contrast, sulfonic acid groups are anchored on the MgO surface via ion-paired bonding, leading to the formation of negatively charged sulfonic acid sites ([O_0.5Mg]⁺:OSO₃H⁻), which prevents their participation in the electrophilic sulfonation reaction with o-xylene molecules. This work provides new insights into tuning and enhancing the performance of supported sulfonic acid materials for the resource-oriented treatment of aromatic VOCs. Full article

Volatile organic compounds (VOCs) are presently posing a rather considerable threat to both human health and environmental sustainability. Among these, n-butanol is commonly identified as bringing potential hazards to environmental integrity and individual health. This study presents the creation of a highly sensitive n-butanol gas sensor utilizing cobalt-doped zinc oxide (ZnO) derived from a metal–organic framework (MOF). A series of x-Co/MOF-ZnO (x = 1, 3, 5, 7 wt%) nanomaterials with varying Co ratios were generated using the homogeneous co-precipitation method and assessed for their gas-sensing performances under a low operating temperature (191 °C) and UV excitation (220 mW/cm²). These findings demonstrated that the 5-Co/MOF-ZnO sensor presented the highest oxygen vacancy (O_v) concentration and the largest specific surface area (SSA), representing the optimal reactivity, selectivity, and durability for n-butanol detection. Regarding the sensor’s response to 100 ppm n-butanol under UV excitation, it achieved a value of 1259.06, 9.80 times greater than that of pure MOF-ZnO (128.56) and 2.07 times higher than that in darkness (608.38). Additionally, under UV illumination, the sensor achieved a rapid response time (11 s) and recovery rate (23 s). As a strategy to transform the functionality of ZnO-based sensors for n-butanol gas detection, this study also investigated potential possible redox reactions occurring during the detection process. Full article

The organic volatile compound gases (VOCs) emitted by the rubber running tracks in the park pose a threat to human health. Currently, the challenge lies in how to detect the VOC gas concentration to ensure it is below the level that is harmful to human health. This study developed a low-power acetone gas sensor based on SnO₂/Nb₂C MXene composites, designed for monitoring acetone gas in pocket park rubber tracks at room temperature. Nb₂C MXene was combined with SnO₂ nanoparticles through a hydrothermal method, and the results showed that the SnO₂/Nb₂C MXene composite sensor (SnM-2) exhibited a response value of 146.5% in detecting 1 ppm acetone gas, with a response time of 155 s and a recovery time of 295 s. This performance was significantly better than that of the pure SnO₂ sensor, with a 6-fold increase in response value. Additionally, the sensor exhibits excellent selectivity against VOCs, such as ethanol, formaldehyde, and isopropanol, with good stability (~20 days) and reversibility (~50). It can accurately recognize acetone gas concentrations and has been successfully used to simulate rubber track environments and provide accurate acetone concentration data. This study provides a feasible solution for monitoring VOCs in rubber tracks and the foundation for the development of low-power, high-performance, and 2D MXene gas sensors. Full article

Acetone detection is crucial for non-invasive health monitoring and environmental safety, so there is an urgent demand to develop high-performance gas sensors. Here, platinum (Pt)-functionalized layered flower-like nickel ferrite (NiFe₂O₄) sheets were efficiently fabricated via facile hydrothermal synthesis and wet chemical reduction processes. When the Ni/Fe molar ratio is 1:1, the sensing material forms a Ni/NiO/NiFe₂O₄ composite, with performance further optimized by tuning Pt loading. At 1.5% Pt mass fraction, the sensor shows a high acetone response (R_g/R_a = 58.33 at 100 ppm), a 100 ppb detection limit, fast response/recovery times (7/245 s at 100 ppm), and excellent selectivity. The enhancement in performance originates from the synergistic effect of the structure and Pt loading: the layered flower-like morphology facilitates gas diffusion and charge transport, while Pt nanoparticles serve as active sites to lower the activation energy of acetone redox reactions. This work presents a novel strategy for designing high-performance volatile organic compound (VOC) sensors by combining hierarchical nanostructured transition metal ferrites with noble metal modifications. Full article

To overcome the shortcomings of traditional polyurethane, such as poor weather resistance and susceptibility to hydrolysis, this study systematically explores the preparation techniques of organic silicon-modified polyurethane and its application in intelligent sports fields. By introducing siloxane into the polyurethane matrix through copolymerization, physical blending, and grafting techniques, the microphase separation structure and interfacial properties of the material are effectively optimized. In terms of synthesis processes, the one-step method achieves efficient preparation by controlling the isocyanate/hydroxyl molar ratio (1.05–1.15), while the prepolymer chain extension method optimizes the crosslinked network through dual reactions. The modified material exhibits significant performance improvements: tensile strength reaches 60 MPa, tear resistance reaches 80 kN/m, and the elastic recovery rate ranges from 85% to 92%, demonstrating outstanding weather resistance. In sports field applications, the 48% impact absorption rate meets the requirements for athletic tracks, wear resistance of <15 mg suits gym floors, and the impact resistance for skate parks reaches 55%–65%. Its environmental benefits are notable, with volatile organic compounds (VOC) <50 g/L and a recycling rate >85%, complying with green building material standards. However, its development is still constrained by multiple factors: insufficient material interface compatibility, a comprehensive cost of 435 RMB/m², and the lack of a quality evaluation system. Future research priorities include constructing dynamic covalent crosslinked networks (e.g., self-healing systems), adopting bio-based raw materials to reduce carbon footprint by 30%–50%, and integrating flexible sensing technologies for intelligent responsiveness. Through multidimensional innovation, this material is expected to evolve toward multifunctionality and environmental friendliness, providing core material support for the intelligent upgrading of sports fields. Full article

The management of municipal solid waste remains a critical environmental and energy challenge across the European Union (EU), where a significant portion of waste still ends up in landfills, generating landfill gas (LFG) rich in methane and harmful impurities. In Latvia, despite national strategies to enhance circularity, untreated LFG is underutilized due to inadequate purification infrastructure, particularly in meeting biomethane standards. This study addressed this gap by proposing and evaluating an innovative, multistep LFG purification system tailored to Latvian conditions, with the aim of enabling the broader use of LFG for energy cogeneration and potentially biomethane injection. The research objective was to design, describe, and preliminarily assess a pilot-scale LFG purification prototype suitable for deployment at Latvia’s largest landfill facility—Landfill A. The methodological approach combined chemical composition analysis of LFG, technical site assessments, and engineering modelling of a five-step purification system, including desulfurization, cooling and moisture removal, siloxane filtration, pumping stabilization, and activated carbon treatment. The system was designed for a nominal gas flow rate of 1500 m³/h and developed with modular scalability in mind. The results showed that raw LFG from Landfill A contains high concentrations of hydrogen sulfide, siloxanes, and volatile organic compounds (VOCs), far exceeding permissible thresholds for biomethane applications. The designed prototype demonstrated the technical feasibility of reducing hydrogen sulfide (H₂S) concentrations to <7 mg/m³ and siloxanes to ≤0.3 mg/m³, thus aligning the purified gas with EU biomethane quality requirements. Infrastructure assessments confirmed that existing electricity, water, and sewage capacities at Landfill A are sufficient to support the system’s operation. The implications of this research suggest that properly engineered LFG purification systems can transform landfills from passive waste sinks into active energy resources, aligning with the EU Green Deal goals and enhancing local energy resilience. It is recommended that further validation be carried out through long-term pilot operation, economic analysis of gas recovery profitability, and adaptation of the system for integration with national gas grids. The prototype provides a transferable model for other Baltic and Eastern European contexts, where LFG remains an underexploited asset for sustainable energy transitions. Full article

Deep neural networks provide a powerful driving force for breakthroughs in semantic segmentation technology. However, the current mainstream architecture generally falls into the “parameter redundancy trap” in pursuit of accuracy improvement, which brings a large number of calculations and model parameters, forcing researchers to seek a new structural paradigm balance between pixel-level parsing accuracy and the limited computing power of embedded devices. We propose a lightweight semantic segmentation network with multi-level feature fusion and dual attention coordination. In view of the large number of parameters in the traditional backbone network and the fact that it only outputs semantic features at the end of the network but lacks shallow feature information, it will cause significant information loss in the decoder stage, which may lead to fuzzy segmentation results and the misclassification of categories. We design a lightweight backbone network with multi-level feature fusion capability. The detail recovery capability is enhanced in the reconstruction process layer by constructing a cross-stage feature aggregation module system; secondly, in view of the lack of effective feature attention in previous methods, we propose a new DCA module in the proposed network and introduce CBAM in the multi-level special fusion network at a shallow level, which improves the model’s category discrimination ability with minimal parameter overhead, thereby optimizing feature expression and improving segmentation performance. The results show that in the Cityscapes dataset, the mIoU reaches 75.29% with only 5.82 M parameters. In the Pascal VOC 2012 dataset experiment, the proposed model achieves an mIoU of 74.24% with only 5.869 M parameters. Compared with DCN-Deeplabv3+ network, the parameters comprise 48% of it, but the accuracy is improved by 1.66%. Compared with the UNet and PSPNet models, the parameters are reduced by 86.63% and 87.44%, respectively. Full article