Search Results (377)

Developing cost-effective alternatives to platinum-based catalysts remains paramount for commercializing anion exchange membrane fuel cells (AEMFCs). We report a metal-free nitrogen-doped carbon catalyst derived from a rationally designed polyurea–polyimine copolymer that outperforms commercial 20 wt% Pt/C in superior relative durability and methanol tolerance. Strategic integration of polyurea’s pore-forming capability with polyimine’s thermal stability enabled the synthesis of a catalyst (NC-1000N) featuring ultrahigh surface area (1276.5 m² g⁻¹), optimal nitrogen speciation (20.5% pyridinic-N, 45.3% graphitic-N), and enhanced graphitization, which improves the electrical conductivity of catalysts. NC-1000N exhibited exceptional oxygen reduction performance with an onset potential of 0.96 V, almost four-electron selectivity (n = 3.87), a medium Tafel slope (105 mV dec⁻¹), and minimal charge transfer resistance (46.74 Ω). When evaluated in single-cell AEMFCs, NC-1000N delivered a peak power density of 372.1 mW cm⁻², which is 26% higher than Pt/C at equivalent loading, while demonstrating superior stability (94.8% retention after 7 h) and complete methanol tolerance. Systematic pyrolysis temperature optimization (800–1000 °C) revealed critical structure–property relationships governing catalyst evolution from disordered precursor to highly graphitic, nitrogen-enriched carbon with precisely engineered active sites. This work establishes polymer-derived carbons and provides design principles for scalable synthesis of high-performance metal-free electrocatalysts for sustainable energy conversion technologies. Full article

This study investigates the effects of Cobalt Oxide (Co₃O₄) nanoparticles on engine performance as well as emission characteristics under various engine load situations in test fuel (MB10). Response Surface Methodology (RSM) was used to examine the experimental results to assess the impact of nanoparticle concentration (0–150 ppm) on combustion behavior. Brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) were performance metrics, and CO, HC, CO₂, and NO_x were emission characteristics. The findings demonstrated that the inclusion of nanoparticles and biodiesel had a major impact on emission behavior and performance. Because biodiesel contains more oxygen than diesel fuel, it reduces CO emissions while increasing CO₂ and NO_x emissions. By boosting heat transmission, the use of nanoparticles increased combustion efficiency; however, fuel atomization was adversely affected by high concentrations. With error rates under 10% for every response, RSM models showed excellent prediction accuracy. To achieve 21% BTE, 458.21 g/kWh BSFC, and minimum emission levels of 0.048% CO, 9.478 ppm HC, 5.415% CO₂, and 601.09 ppm NO_x, the optimization study identified the optimal operating condition with a 1.31 kW engine load and 80.36 ppm Co₃O₄ addition. The results verify that the proper dosage of nanoparticles can enhance the combustion performance of biodiesel while preserving acceptable emission levels. Full article

This paper introduces a refined criterion for evaluating and optimizing the aerodynamic efficiency of compound planform wings, specifically those whose half span is formed by multiple trapezoidal segments. While elliptical lift distribution is known to minimize induced drag, practical manufacturing constraints have led to widespread adoption of tapered wings. However, conventional single-trapezoid planforms deviate significantly from the ideal elliptical distribution, resulting in increased induced drag and reduced fuel efficiency. This study proposes an adjustment to the ellipticity factor, enabling quantitative assessment of how well a multi-trapezoid wing approximates elliptical chord distribution. The methodology is validated through analysis of existing transport aircraft, identifying configurations with ellipticity factors below 5% (e.g., Lockheed C-5A, Antonov An-124) that achieve near-optimal induced drag performance. A comparative case study of a virtual 40-ton aircraft with a 100 m² wing area quantifies trade-offs between three planform configurations. Computational fluid dynamics simulations confirm that increasing trapezoidal segmentation improves spanwise loading and delays flow separation. Results demonstrate that two-trapezoid configurations with total inverse taper ratios of 3.3–4.2 and break coordinates at 35–45% half span achieve ellipticity factors under 7%, offering an optimal balance between aerodynamic efficiency, structural feasibility, and tail surface requirements. The proposed criterion provides aircraft designers with a rapid, computationally efficient tool for planform optimization at the conceptual design stage. The proposed criterion is valid for subsonic cruise conditions (M ≤ 0.85) and does not account for wave drag or aeroelastic effects. Full article

In steam turbines, blades operate in a high-speed wet steam environment and are often damaged by combined erosion from liquid droplets and solid particles. To reveal the mechanism of composite modification via high velocity oxy-fuel spraying (HVOF) and laser shock peening (LSP) on improving blade erosion resistance, an accelerated erosion experimental method was designed in this work. Five different processes were proposed, including UT, LSP, UT-HVOF, LSP-HVOF, and HVOF-LSP. The results indicate that compared with UT specimens, LSP treatment induces high compressive residual stress in the surface layer of 1Cr12Ni3Mo2VN stainless steel, which leads to shallower compound erosion pits. Compared with UT-HVOF and LSP-HVOF specimens, the HVOF-LSP specimen has the lowest coating porosity and the highest surface microhardness of 1500 HV0.5, representing an increase of 14.5% and 8.7% respectively. This demonstrates that LSP post-treatment can enhance the load-bearing capacity of HVOF coatings effectively. Microstructural analysis further reveals that the HVOF-LSP specimen presents the shallowest erosion pits and the longest penetration lifetime of the WC coating. Accordingly, the HVOF-LSP treatment can effectively improve the service life and protection performance of materials under accelerated erosion conditions, providing a technical reference for the long-term service of turbine blades. Full article

Efficient seedbed preparation under conservation-oriented tillage requires balanced aggregate fragmentation, surface microrelief and energy demand. This study investigated the influence of passive toothed roller parameters on soil fragmentation, surface roughness, draft force and fuel consumption during chisel tillage under medium-loam Calcisol conditions. Three configurations were compared: a chisel plow without a roller, with a slat roller and with a toothed roller. An analytical framework describing aggregate capture, tooth–soil contact frequency and resistance formation was combined with field experiments and regression-based response surface analysis. The toothed roller improved measured soil treatment indicators compared with the no-roller and slat-roller configurations due to discrete tooth–soil interaction, localized stress concentration and repeated loading of loosened aggregates. Rational parameter ranges were identified: a roller diameter of 0.45–0.46 m, 13–15 teeth, transverse spacing of 8.0–8.6 cm, a tooth height of 7.5–8.5 cm and specific load of 0.9–1.1 kN m⁻¹. Under the selected configuration, aggregates smaller than 50 mm increased from 76.1% to 88.0%, surface roughness decreased from 6.8 to 3.7 cm and residue retention remained above 60%. Fuel consumption increased to 28.4–28.5 L ha⁻¹, reflecting the additional energetic cost of fragmentation and levelling. The approach supports rational selection of passive toothed roller parameters under the tested conditions. Full article

Proton exchange membrane fuel cells (PEMFCs) exhibit high energy efficiency and rapid load response, but challenges are faced in membrane fabrication, including the need for renewable resources and cost-effective, non-toxic solvents. This study analyzes the morphological and structural properties of perfluorosulfonic acid (PFSA) ionomer membranes, FS-930 and F-14100, after the dissolution of membranes via ratios of 50:50, 80:20, and 20:80 by volume of dimethyl sulfoxide (DMSO) and water. Bode plot analysis indicates that membranes rich in DMSO show lower frequency phase angle peaks, suggesting better segmental motion and ionic conductivity. Additionally, higher DMSO content correlates with broader FTIR peaks, reflecting enhanced solute–solvent interactions. The untreated FS-930 membrane demonstrates significant intensity peaks linked to semi-crystalline domains, indicating strong baseline conductivity. SEM analysis revealed surface roughness variations in FS-930 linked to different water-to-DMSO volume ratios. DMSO-rich mixtures produced dense, hydrophobic PFSA membrane structures, whereas water-rich mixtures increased water uptake and ionic conductivity. Fumapem F-14100 showed superior hydration and proton conductivity compared to FS-930 because it contains more sulfonic acid groups. These findings are critical to understanding how membrane properties relate to solvent composition, aiding in the optimization of membrane fabrication for better performance and durability in fuel cells. Full article

Community-Based Fire Management (CBFiM) integrates local governance and ecological stewardship, yet the social drivers shaping its effectiveness remain poorly understood. This study examines the relationships among leadership perception, community participation, and surface fuel conditions in two community forests in Lampang Province, northern Thailand: Ban Pong and Ban Rong Ta. Forest floor fuel data were collected through destructive fuel sampling during the 2025 dry season, and social data were gathered through structured questionnaires measuring leadership perception using the Crew Member Perceived Leadership Scale and participation across seven fire management activities. Ban Rong Ta showed lower fuel loads but higher fire occurrence (nine fire detections recorded in 9 of 10 study years), lower leadership perception across all dimensions, and reduced participation in activities. The brigade–community participation gap reflects patterns documented across Southeast Asian community forestry programs, pointing to a structural challenge in fire governance. These findings suggest that awareness and informational participation alone do not reduce wildfire risk, and that integrating social and ecological indicators is essential for designing effective community-based fire governance systems. Full article

The urgent need to reduce greenhouse gas emissions has driven the search for sustainable alternatives to fossil fuels. In this context, cocoa residues emerge as a promising feedstock for bioethanol production. This study evaluated the influence of a catalytic biorefinery treatment on the bioethanol production potential from cocoa pod husks. Both raw and catalytically treated biomass were characterized using SEM, pore size distribution analysis, and TGA. Subsequently, enzymatic hydrolysis was performed using various cellulase and hemicellulase loadings, followed by anaerobic fermentation with Saccharomyces cerevisiae. Bioethanol production was modeled using the modified Gompertz equation. The results evidenced changes in the structure and composition of the lignocellulosic matrix following catalytic treatment, increasing surface area and reducing hemicellulose content. Although total sugar release during hydrolysis was comparable between the two samples, the biomass processed via the catalytic biorefinery promoted higher sugar consumption and bioethanol concentration, reaching 3.36 g/L with a yield of 112 g kg⁻¹ of dry biomass. The kinetic model showed a strong fit (R² between 0.94 and 0.97). These findings demonstrate that the integration of catalytic biorefinery, enzymatic hydrolysis, and fermentation constitutes a viable alternative for the valorization of cocoa residues. Full article

Grassland fire is one of the major disasters threatening regional ecological security. Its occurrence, development, and spread are closely related to the spatial distribution and coverage of surface vegetation and snow cover across grassland areas. As the primary combustible fuel source, higher vegetation coverage increases fuel load and continuity, thereby directly determining grassland fire danger levels and accelerating fire spread velocity. In contrast, snow cover imposes an indirect regulatory effect on the spatiotemporal pattern of fire danger factors: it lowers surface temperature, raises near-surface humidity, and restricts the germination and growth of herbaceous vegetation in cold seasons, which effectively reduces available combustible materials and weakens regional fire hazard conditions. Therefore, accurately obtaining the coverage status of vegetation (direct combustible fuel factor) and snow cover (indirect fire-regulating factor) in complex grassland scenarios is the essential premise for reliable grassland fire danger monitoring, early warning, disaster prevention and control, and regional ecological management. Aiming at the practical problems in complex grassland scenarios (such as undulating terrain, uneven vegetation growth, large differences in snow depth, and complex lighting conditions), including difficulty in extracting vegetation and snow-covered areas, blurred and confusing boundaries, and low accuracy in coverage calculation, which seriously restrict the technical bottleneck of precise monitoring of grassland fire danger factors, this study takes near-ground images collected by grassland fire danger factor monitoring stations as the core data source, and proposes an improved UNet image segmentation model combined with image segmentation technology and deep learning methods to realize precise extraction of vegetation and snow-covered areas and efficient calculation of coverage in complex scenarios. To improve the model’s feature extraction ability, boundary localization accuracy, and reduce model parameters and computational overhead, the CBAM-ASPP (Convolutional Block Attention Module—Atrous Spatial Pyramid Pooling) module is integrated at the end of the encoding path. The attention mechanism is used to enhance the weight of key features, and the multi-scale receptive field of atrous spatial pyramid pooling is utilized to strengthen the model’s ability to fuse features of vegetation and snow areas of different scales. The residual attention mechanism is introduced in the upsampling stage to effectively alleviate the gradient disappearance problem, improve the model’s ability to accurately locate the boundaries of vegetation and snow areas, and reduce segmentation errors. In the training process, a dynamically weighted hybrid loss function is adopted to dynamically adjust the weights according to the segmentation difficulty of different types of samples during training, optimize the model training effect, and improve the segmentation accuracy and generalization ability. Experiments were conducted using near-ground images of typical complex grassland scenarios as the dataset, and the performance of the proposed model was verified through comparative experiments. The results show that in the vegetation segmentation task, the mean Intersection over Union (mIoU) of the model reaches 84.70%, and the accuracy rate is 91.28%, which are 1.48 and 1.58 percentage points higher than those of the standard UNet model, respectively. In the snow segmentation task, the mIoU of the model reaches 92.74%, and the accuracy rate is 94.19%, which are 2.39 and 2.36 percentage points higher than those of the standard UNet model, respectively. At the same time, the number of parameters of the model is reduced by 12.85% compared with the standard UNet. Also, its comprehensive performance is significantly better than that of mainstream image segmentation models such as FCN, SegNet, and DeepLabv3+. Based on the standardized time-series data retrieved by the optimized segmentation model, this study further constructs a Grassland Fire Risk Index (GFRI) using the Analytic Hierarchy Process (AHP). Pearson correlation verification confirms that the GFRI has an extremely significant positive correlation with historical fire frequency, accurately capturing the seasonal dynamic rhythm of regional grassland fire occurrence. This integrated framework of intelligent segmentation and fire risk quantification provides a reliable technical solution for grassland fire factor monitoring, dynamic fire risk assessment, early warning systems, and refined regional ecological management. Full article

Diesel engines are commonly used in transportation and power generation, but their operation is associated with incomplete combustion and emissions. In this research, four different nanocatalyst additives including Ag, Ag/TiO₂, Ce/TiO₂, and Ag/CeO₂/TiO₂ were studied as diesel fuel additives to improve combustion efficiency and minimize regulated emissions. These nanoparticles were synthesized and characterized by employing X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR) techniques. The prepared fuel blends were tested in a single-cylinder diesel engine at additive concentrations of 50, 75, and 100 ppm under varying engine loads. Among the tested formulations, the ternary Ag/CeO₂/TiO₂ blend demonstrated the highest performance. When compared with the baseline diesel fuel, it reduced CO emissions by 32.5%, HC emissions by 27.8%, and NO_x emissions by 29.4%. At the same time, the amount of CO₂ emission has increased by 18.81%, which shows that the combustion is more complete. Also, the same formulation has decreased brake-specific fuel consumption (BSFC) by 18.7% and increased brake thermal efficiency (BTE) by 16.3%. The improved performance is due to the cooperative effect of CeO₂ oxygen buffering, TiO₂ surface-assisted oxidation, and oxidation activity of the silver species. The findings show that the ternary nanocatalyst formulation is an effective approach for optimizing diesel fuel combustion and emissions. Full article

The catalytic modification of combustion processes using nanoparticle additives has emerged as a promising strategy to improve fuel oxidation and reduce pollutant formation in compression ignition (CI) engines. In this study, the catalytic effects of nano-sized boron oxide (B₂O₃) on biodiesel combustion were systematically investigated. Jojoba oil, a non-edible and drought-resistant feedstock, was transesterified to produce second-generation biodiesel and blended with diesel fuel. Among the tested blends, J10 (10% biodiesel and 90% diesel) was selected as the base fuel blend due to its favorable combustion and emission characteristics. To explore catalytic enhancement mechanisms, B₂O₃ nanoparticles were introduced at concentrations of 25, 50, and 75 ppm. The high surface area and oxygen buffering capacity of B₂O₃ nanoparticles are expected to enhance oxidation reactions and promote radical formation during combustion. This catalytic effect contributes to improved combustion efficiency, as evidenced by a significant reduction in incomplete combustion products. Compared with diesel fuel (D100), HC emissions were reduced by up to 53.34%, while CO emissions decreased by 24.42–41.98% depending on the operating conditions and fuel blends. In addition, a noticeable improvement in combustion quality was reflected in the brake thermal efficiency (BTE), where variations of up to 11.61% were observed across different fuel blends. Response Surface Methodology (RSM) was employed to quantify the interaction between nanoparticle concentration and engine load and to identify optimal catalytic operating conditions. The optimal parameters were determined as 12.14 ppm B₂O₃ and 1.36 kW load, yielding a desirability of 0.7128. Under these conditions, the engine achieved a BSFC of 458.83 g/kWh and BTE of 22.01%, with emissions reduced to 0.041% CO, 14.29 ppm HC, and 346.44 ppm NO_x. The results demonstrate that nano B₂O₃ functions as a combustion catalyst by enhancing oxidation pathways and improving fuel-air interaction, thereby increasing combustion efficiency and reducing harmful emissions. Full article

Wildland fire scientists have made substantial advances in measuring fire behavior, but properly collecting data is often beyond the capacity of prescribed fire managers and by definition all but impossible for wildfire events. While a method for the immediate assessment of burn severity has been developed around multispectral imagery from space-based Earth observation systems, there has been little comparison of these post hoc metrics to actual fire behavior. Meanwhile, the application of research results from experimental prescribed burns to rangeland affected by wildfire can be impeded by a lack of understanding of how immediate burn severity differs between wildfires and prescribed burns, especially in rangelands. Overall, much of what is known about wildland fire behavior, severity, and effects comes from forests, whereas rangelands are characterized by having lower fuel loads comprised of fine vegetation that promotes high rates of spread and brief residence time. This paper provides rangeland-specific information on the relationships between direct field-based fire behavior measurements and a space-based index of burn severity (differenced Normalized Burn Ratio, ΔNBR, from Sentinel-2 imagery), and uses those data to compare burn severity across 54 prescribed burns in North Dakota, USA, and 28 nearby wildfires in the US Northern Great Plains. In prescribed burns, remotely sensed burn severity increased with rate of spread and flame temperature 15 cm above the ground, but had no statistically significant relationship with soil surface temperature. In the semi-arid western zone of the Northern Great Plains, wildfires and prescribed burns had similar, low–moderate severity; wildfires in the eastern zone tended to be of moderately high severity and thus greater than the low severity of the experimental prescribed burns. By describing meaningful gradients in surface fire behavior in rangelands with ΔNBR, even those without the capacity to measure fire behavior in the field can monitor prescribed fire effectiveness and incorporate burn severity in adaptive management plans. Understanding the relationship between burn severity across wildfires and prescribed burns is a critical step in applying knowledge gained from research on prescribed fires to areas impacted by wildfire. Resistance to prescribed burning might be overcome by increasing livestock managers’ experience with post-fire forage resources through grazing areas burned in unintentional wildfires, but current practice and policy discourage or outright prevent ranchers from doing so. Future research ought to connect burn severity with ecosystem recovery metrics to ensure post-fire grazing does not impair rangeland sustainability. Full article

This study presents an experimental engine map investigation of waste swine oil biodiesel (WSO B100) in a single-cylinder, four-stroke variable compression ratio compression ignition engine, quantifying the coupled effects of compression ratio and load on brake thermal efficiency, brake-specific fuel consumption, torque, brake power, and regulated emissions of NOx, CO, HC, and CO₂. Compression ratios of 12, 14, 16, and 18 were evaluated at dynamometer loads of 25%, 50%, and 75% under steady-state operation. The study’s primary contribution is a structured compression ratio–load mapping framework that produces consistent performance emission response surfaces and, supported by statistical modeling and sensitivity analysis, resolves main and interaction effects to identify operating regions that balance efficiency and emissions. Methodological traceability is strengthened by attaching fuel energy and mass flow calculations to batch-specific fuel properties, including viscosity and density, and by using calorimetry-derived heating value in efficiency calculations. Increasing the compression ratio from 12 to 18 improved brake thermal efficiency by 3–10% at low load and reduced brake-specific fuel consumption, while NOx increased by 20–30% across the load range. Increasing load raised brake thermal efficiency from 29% at 25% load to 42% at 75% load and reduced brake-specific fuel consumption from 309 to 215 g/kWh; NOx peaked at 488 ppm at 75% load and compression ratio 18. CO and HC decreased with both load and compression ratio, reaching minima of 0.15% and 30 ppm, whereas CO₂ increased primarily with load. Relative to diesel, WSO biodiesel showed 8–12% higher brake-specific fuel consumption and 2–4% lower peak brake thermal efficiency, but achieved substantial CO and HC reductions. Generally, WSO biodiesel operates effectively across a wide compression ratio range with broadly comparable performance to diesel. However, increased NOx and reduced low-load efficiency indicate the need for targeted calibration or emission control. Full article

The high demand for fossil fuels in human activities and industrial production has intensified environmental pollution, global warming, and energy shortages, making the development of alternative energy and energy-storage technologies imperative. Among these approaches, photocatalytic conversion of solar energy into hydrogen is regarded as a sustainable solution to the energy and environmental crises. However, the rapid recombination of photogenerated charge carriers and the lack of effective active sites severely limit photocatalytic performance. To address these challenges, heterojunction engineering is often employed to suppress electron-hole recombination and enhance photocatalytic H₂ evolution efficiency. A MoS₂/ZnIn₂S₄ heterojunction was constructed via the in situ growth of MoS₂ nanorods on the surface of ZnIn₂S₄. The introduction of MoS₂ not only broadens the light-absorption range of ZnIn₂S₄, but also suppresses the recombination of photogenerated charge carriers, thereby significantly enhancing the photocatalytic H₂ evolution performance of ZnIn₂S₄. The optimal MoS₂ loading was 30 wt%, at which the photocatalytic H₂ evolution rate reached 11.52 mmol·g⁻¹·h⁻¹, nearly 2.5 times that of pure MoS₂. In addition, the catalyst maintained nearly unchanged activity after five consecutive cycles, indicating good stability and that photocorrosion was effectively suppressed in the presence of sacrificial reagents. The heterojunction formed between MoS₂ and ZnIn₂S₄ shortens the charge-transfer pathway and improves the separation efficiency of photogenerated electrons and holes, thereby suppressing charge-carrier recombination and accelerating the photocatalytic H₂ evolution re photocorrosion action. Full article

Pre-sowing tillage under irrigated agriculture is associated with high energy demand and increased risk of soil structural degradation, particularly in heterogeneous loam soils of arid and semi-arid regions. This study presents the engineering optimization and field validation of a combined implement for single-pass rotary strip tillage and precision seeding developed for irrigated sierozem soils of Southern Kazakhstan. The research integrates analytical modeling of soil–blade interaction, optimization of rotary blade geometry, and comparative field experiments using an experimental prototype (FS-2.1). Analytical optimization identified an optimal blade installation angle of 54–56°, resulting in an approximately 22% reduction in specific cutting area. Field results demonstrated that the single-pass system formed a high-quality seedbed, with 85.2% of soil aggregates smaller than 25 mm and a surface leveling deviation below 5 mm. Compared with a conventional multi-pass technology, traction load, fuel consumption, and total energy input were reduced by 38%, 43%, and 54.5%, respectively. The results confirm that combining optimized rotary blade geometry with strip-based soil disturbance enables substantial energy savings without compromising agronomic performance. The proposed engineering solution provides a reproducible framework for low-traction, resource-efficient tillage–seeding systems suitable for irrigated agriculture in Southern Kazakhstan and comparable agroecological regions. Full article