Search Results (332)

This study presents a detailed analysis of the combustion dynamics of stoichiometric H₂–air and CH₄–air mixtures in a 20 L closed vessel over an initial temperature range of 298–423 K. We integrate experimental pressure–time P(t) measurements with numerical analysis to extract laminar burning velocity (LBV) and deflagration index (K_G) values, and we assess three independent kinetic mechanisms (KiBo_MU, University of San Diego, Lund University) via simulations. For H₂–air, LBV increases from 0.50 m/s at 298 K to 0.94 m/s at 423 K (temperature exponent α ≈ 1.79), while for CH₄–air, LBV rises from 0.36 m/s to 0.96 m/s (α ≈ 2.82). In contrast, the deflagration index K_G decreases by ca. 20% for H₂–air and ca. 30% for CH₄–air over the same temperature span. The maximum explosion pressure (P_max) and peak pressure rise rate ((dP/dt)_max) also exhibit systematic increases with temperature. A comparison with model predictions shows agreement within experiments, providing data for safety modeling and kinetic mechanism validation in H₂- and CH₄-based energy systems. Full article

Fischer–Tropsch synthesis (FTS) facilitates the conversion of syngas, derived from feedstocks such as biomass, coal, and natural gas, into valuable hydrocarbons (HCs). This investigation employed optimization methods, specifically Gibbs energy minimization, to perform a thermodynamic characterization of the low-temperature Fischer–Tropsch (LTFT) reaction for HC generation. The CONOPT3 solver within GAMS 23.2.1 software was utilized for solving the developed model. To represent the complex FTS product spectrum, twenty-three compounds, encompassing C₂–C₂₀ aliphatic hydrocarbons, were considered using a stoichiometric framework. The study explored the impact of operational parameters, including temperature (350–550 K), pressure (5–30 bar), and H₂/CO molar feed ratio (1.0–2.0/0.5–1.0), on hydrocarbon synthesis. Evaluation of the outcomes focused on HC yield and product characteristics. A significant sensitivity of the reaction to operating parameters was observed. Notably, lower temperatures, elevated pressures, and a H₂/CO ratio of 2.0/1.0 were identified as optimal for fostering the formation of longer-chain HCs. The developed model demonstrated robustness and efficiency, with rapid computation times across all simulations. Full article

Exploring the mechanisms by which green agricultural production reduces emissions and enhances carbon sequestration in soil can provide a scientific basis for greenhouse gas reduction and sustainable development in farmland. This study uses a combination of meta-analysis and field experiments to evaluate the impact of different agricultural management practices and climatic conditions on soil organic carbon (SOC) and the emissions of CO₂ and CH₄, as well as the role of microorganisms. The results indicate the following: (1) Meta-analysis reveals that the long-term application of organic fertilizers in green agriculture increases SOC at a rate four times higher than that of chemical fertilizers. No-till and straw return practices significantly reduce CO₂ emissions from alkaline soils by 30.7% (p < 0.05). Warm and humid climates in low-altitude regions are more conducive to soil carbon sequestration. (2) Structural equation modeling of plant–microbe–soil carbon interactions shows that plant species diversity (PSD) indirectly affects microbial biomass by influencing organic matter indicators, mineral properties, and physicochemical characteristics, thereby regulating soil carbon sequestration and greenhouse gas emissions. (3) Field experiments conducted in the typical green farming research area of Chenzhuang reveal that soils managed under natural farming absorb CH₄ at a rate three times higher than those under conventional farming, and the stoichiometric ratios of soil enzymes in the former are close to 1. The peak SOC (19.90 g/kg) in the surface soil of Chenzhuang is found near fields cultivated with natural farming measures. This study provides theoretical support and practical guidance for the sustainable development of green agriculture. Full article

This study examines the emissions generated by a tall ship of 81.36 m length under various operating conditions, focusing particularly on carbon dioxide emissions at different navigation speeds. The main purpose of the paper is to establish theoretical and practical methods for calculating and measuring the level of CO₂ emitted by the ship engines. Additionally, this article compares the results of carbon dioxide emission calculations based on theoretical methods with the results of real measurements. The paper verifies and assesses the carbon dioxide emission calculation methods compared to the emissions measured in real conditions for diesel engines. A comparative analysis of several methods for determining CO₂ emissions leads to much more accurate and conclusive results close to reality. The results obtained through empirical and theoretical methods for determining CO₂ emissions from the main engine demonstrate that the difference between these values is more accurate at lower engine loads but shows discrepancies at higher loads due to real-world inefficiencies, combustion variations, and model simplifications. The measured CO₂ emission values for auxiliary engines at 60% load demonstrate consistency and closely reflect real operating conditions, while analytical calculations tend to be higher due to theoretical losses and model assumptions. Stoichiometric values fall in between, assuming ideal combustion but lacking adjustments for real variables. This highlights the efficiency of the diesel generator and the importance of empirical data in capturing actual emissions more accurately. The investigation aims to provide a detailed understanding of CO₂ emission variations based on the ship’s operating parameters, including the study of these emissions at the level of the main diesel propulsion engine as well as the auxiliary engines. By analyzing these methods for determining engine emissions, conclusions can be reached about aspects such as the following: engine wear condition, efficiency losses, or incomplete combustion. This analysis has the potential to guide the implementation of new policies and technologies aimed at minimizing the carbon footprint of a reference ship, considering the importance of sustainable resource management and environmental protection in a viable long-term manner. Full article

The spontaneous deposition of platinum ($Pt$) atoms on $Au\left(111\right)$ surfaces is systematically investigated through kinetic Monte Carlo simulations within the Embedded Atom Model framework. The kinetic model aims to capture both stoichiometric, atomic-scale interactions and the more relevant processes that describe the kinetics of a physical problem. Various deposition rates are examined, encompassing a thorough exploration of $Pt$ adsorption up to a coverage degree of $\theta =0.25$. The resulting 2D islands exhibit a ramified structure, mirroring the experimental methodologies. For the first time, this study extensively analyzes the dependence of both the mean island size and island density on spontaneous deposition, thereby offering valuable insights into the intricate dynamics of the system. Full article

Carbon (C), nitrogen (N), and phosphorus (P) act as pivotal regulators of biogeochemical cycles, steering organic matter decomposition and carbon sequestration in terrestrial ecosystems through the stoichiometric properties of photosynthetic organs. Deciphering their multi-scale spatiotemporal dynamics is central to unraveling plant nutrient strategies and their coupling mechanisms with global element cycling. In the current study, we modeled biogeochemical parameters (C/N/P contents, stoichiometry, and pools) in plant aboveground parts by using the growing mean temperature, total precipitation, total radiation, and maximum normalized difference vegetation index (NDVImax) across nine models (i.e., random forest model, generalized boosting regression model, multiple linear regression model, artificial neural network model, generalized linear regression model, conditional inference tree model, extreme gradient boosting model, support vector machine model, and recursive regression tree) in Xizang grasslands. The results showed that the random forest model had the highest predictive accuracy for nitrogen content, C:P, and N:P ratios under both grazing and fencing conditions (training R² ≥ 0.61, validation R² ≥ 0.95). Additionally, the random forest model had the highest predictive accuracy for C:N ratios under fencing conditions (training R² = 0.84, validation R² = 1.00), as well as for C pool and P content and pool under grazing conditions (training R² ≥ 0.62, validation R² ≥ 0.90). Therefore, the random forest algorithm based on climate data and/or the NDVImax demonstrated superior predictive performance in modeling these biogeochemical parameters. Full article

Energy and mobility are currently powered by conventional fuels, and for the specific case of spark ignition (SI) engines, gasoline is dominant. Converting these power-units to hydrogen is an efficient and cost-effective choice for achieving zero-carbon emissions. The use of this alternative fuel can be combined with a circular-economy approach that gives new life to the existing fleet of engines and minimizes the need for added components. In this context, the current work scrutinizes specific aspects of converting a small-size passenger car to hydrogen fueling. The approach combined measurements performed with gasoline and predictive 0D/1D models for correctly including fuel chemistry effects; the experimental data were used for calibration purposes. One particular aspect of H₂ is that it results in lower volumetric efficiency compared to gasoline, and therefore boosting requirements can feature significant changes. The results of the 0D/1D simulations show that one of the main conclusions is that only stoichiometric operation would ensure the reference peak power level; lean fueling featured relative air–fuel ratios too low for ensuring the minimum value of 2 that would allow mitigating NO_x formation. Top speed could be instead feasible in lean conditions, with the same gearbox, but with an extension of the engine speed operating range to 7000 rpm compared to the 3700 rpm reference point with gasoline. Full article

To probe the properties of single atoms is a challenging task, especially from the experimental standpoint, due to sensitivity limits. Nevertheless, it is sometimes possible to achieve this by making corresponding choices and adjustments to the experimental technique and sample under investigation. In the present case, the absolute value of the electronic charge the Fe atoms acquire when they are adsorbed on the surface of aluminum oxide α-Al₂O₃(0001) was measured by a set of surface-sensitive techniques: low-energy ion scattering (LEIS), Auger electron spectroscopy (AES), low-energy electron diffraction (LEED), and work function (WF) measurements, in combination with density functional theory (DFT) calculations. The main focus was the submonolayer coverage of Fe atoms in situ deposited on the well-ordered stoichiometric α-Al₂O₃(0001) 7 nm thick film formed on a Mo(110) crystal face. An analysis of the evolution of the Fe LVV Auger triplet upon variation of the Fe coverage shows that there is electronic charge transfer from Fe to alumina and that its value gradually decreases as the Fe coverage grows. The same trend is also predicted by the DFT results. Extrapolation of the experimental Fe charge value versus coverage plot yields an estimated value of a single Fe atom adsorbed on α-Al₂O₃(0001) of 0.98e (electron charge units), which is in reasonable agreement with the calculated value (+1.15e). The knowledge of this value and the possibility of its adjustment may be important points for the development and tuning of modern sub-nanometer-scale technologies of diverse applied relevance and can contribute to a more complete justification and selection of the corresponding theoretical models. Full article

The majority of extant studies concentrate on the reactivation of dormant Anammox biomass or the recovery of activity under specific storage conditions. Research on rehabilitation strategies for anaerobic ammonium oxidation (Anammox) systems is limited, with the exception of research on inhibitory factors. The recovery characteristics of biofilm systems after collapse induced by varying degrees of ammonia-nitrogen and small-molecular organic compound composite shocks have not been thoroughly elucidated. This study addresses the collapse of Anammox biofilm systems caused by sodium acetate inhibition through multi-phase rehabilitation strategies, stoichiometric analysis, and microbial community succession dynamics. Two regression algorithms—Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost)—were employed to construct predictive models for Total Nitrogen Removal Efficiency (TNRE) and Total Nitrogen Removal Rate (TNRR) in the CANON system, with model performance evaluated via coefficient of determination (R²) and root mean square error (RMSE). Results demonstrated that after terminating moderate-to-high sodium acetate dosing (300 mg/L and 500 mg/L), reactors R300 and R500 achieved TNRE recovery to 57.98% and 58.86%, respectively, and TNRR of 0.281 and 0.275 kgN/m³·d within 60–100 days, indicating the reversibility of high-concentration sodium acetate inhibition but a positive correlation between recovery duration and inhibition intensity. Microbial community analysis revealed that Planctomycetota (including Candidatus_Kuenenia) rebounded to 46–49% relative abundance in R100, synchronized with TNRE improvement. In contrast, R300 and R500 exhibited ecological niche replacement of denitrifiers (Denitratisoma) and partial TNRE restoration despite enhanced performance. Model comparisons showed SVR outperformed XGBoost in TNRE prediction, whereas XGBoost demonstrated superior TNRR prediction accuracy with R² approaching 1 and RMSE nearing 0, significantly surpassing SVR. This work provides critical insights into recovery mechanisms under organic inhibition stress and establishes a robust predictive framework for optimizing nitrogen removal performance in CANON systems. Full article

The objective of the present work was to investigate a possible processing route for a currently discarded material, niobium mineral tailings containing rare earth elements, from the largest niobium producer worldwide (CBMM), for use as a raw material for valuable products such as ferroniobium and rare earth concentrate. A study was conducted on the kinetics of the carbothermic reduction from hematite to magnetite (magnetizing roasting). Thermogravimetric tests performed in duplicate or triplicate were conducted at three different temperatures (700 °C, 800 °C and 900 °C) for 1 h with two times the stoichiometric quantity of the reductant (charcoal). Scanning electron microscopy (SEM) and X-ray diffraction (XRD) showed small amounts of magnetite in the samples reduced at 700 °C and 800 °C. At 900 °C, in accordance with the XRD analysis (Rietveld), almost all hematite was reduced to magnetite. The kinetic model that showed the best fitting was the Ginstling–Brounshtein model. The apparent activation energy was evaluated to be 206 kJ/mol, which is similar to the values reported in the literature for the activation energy of the Boudouard reaction. Full article

The catalytic performance of copper in Fenton-like processes was investigated under conditions of elevated chloride concentrations. Model solutions were prepared containing four target pollutants (50 mg/L each), Cu (II) at 50 mg/L, and a stoichiometric dose of hydrogen peroxide sufficient for complete oxidation of the organic matter. Chloride levels ranged from low concentrations to those representative of both synthetic and natural seawater (36 g/L NaCl). An increase in chloride concentration consistently led to greater pollutant removal efficiency. The influence of pH on process performance was also assessed in saline and real seawater matrices. An optimal pH range between 6 and 7 was identified in both cases, where the reactivity of copper–chloride complexes was maximized while the formation of insoluble, catalytically inactive copper species was suppressed. Monitoring of pH, soluble copper concentration, and hydrogen peroxide consumption supported the conclusion that real seawater provides the most favorable conditions for copper–chloride catalyzed Fenton-like reactions. These results demonstrate the high potential of copper-based advanced oxidation processes in saline environments, particularly in applications where traditional methods exhibit limited efficiency. Full article

This study utilizes a zero-dimensional, constant-pressure, perfectly stirred reactor (PSR) model within the Cantera framework to examine the combustion characteristics of hydrogen, methane, methanol, and propane, both singly and in hydrogen-enriched mixtures. The impact of the equivalence ratio (ϕ = 0.75, 1.0, 1.5), fuel composition, and residence duration on temperature increase, heat release, ignition delay, and emissions (NO_x and CO₂) is methodically assessed. The simulations are performed under steady-state settings to emulate the ignition and flame propagation processes within pre-chambers and primary combustion zones of internal combustion engines. The results demonstrate that hydrogen significantly improves combustion reactivity, decreasing ignition delay and increasing peak flame temperature, especially at short residence times. The incorporation of hydrogen into hydrocarbon fuels, such as methane and methanol, enhances ignition speed, improves thermal efficiency, and stabilizes lean combustion. Nevertheless, elevated hydrogen concentrations result in increased NO_x emissions, particularly at stoichiometric equivalence ratios, due to higher flame temperatures. The examination of fuel mixtures at varying hydrogen concentrations (10–50% by mole) indicates that thermal performance is optimal under stoichiometric settings and diminishes in both fuel-lean and fuel-rich environments. A thermodynamic model was created utilizing classical combustion theory to validate the heat release estimates based on Cantera. The model computes the heat release per unit volume (MJ/m³) by utilizing stoichiometric oxygen demand, nitrogen dilution, fuel mole fraction, and higher heating values (HHVs). The thermodynamic estimates—3.61 MJ/m³ for H₂–CH₃OH, 3.43 MJ/m³ for H₂–CH₄, and 3.35 MJ/m³ for H₂–C₃H₈—exhibit strong concordance with the Cantera results (2.82–3.02 MJ), thereby validating the physical consistency of the numerical methodology. This comparison substantiates the Cantera model for the precise simulation of hydrogen-blended combustion, endorsing its use in the design and development of advanced low-emission engines. Full article

Designing high-performance crosslinked polymers requires overcoming the inherent stiffness–toughness trade-off through precise control of the network topology. Using epoxy resin as a model system, we establish a multiscale simulation framework to investigate curing reaction kinetics, network evolution, and structure–property relationships. By employing m-phenylenediamine (mPDA) and 1,3-bis(3-aminophenoxy)benzene (DABPB) as competing short- and long-chain curing agents, we demonstrate how network architecture dictates mechanical performance. Simulations reveal that mPDA produces a dense, heterogeneous network with enhanced stiffness, whereas DABPB forms a more uniform structure with greater chain mobility, leading to improved toughness. Through stoichiometric tuning, we achieve fine control over crosslink density and mechanical properties. Furthermore, we decouple cavity formation mechanisms into pendant chain slippage and bond rupture, offering molecular-level insights for the rational design of epoxy resins with programmable mechanical behavior. Full article

Litter serves as a crucial source of soil nutrients in sandy land ecosystems. Soil enzyme activities and their stoichiometric ratios act as essential “bridges” linking microbial metabolism with nutrient cycling, thereby reflecting the availability of soil nutrients and the sensitivity to microbial substrate limitations. To investigate the effects of litter quality changes on soil nutrients, enzyme activities, and stoichiometric ratios in sandy land, leaf litter and surface soil were collected from four sand-fixing forests in the Mu Us Sandy Land, including YC (Corethrodendron fruticosum), NT (Caragana korshinskii), ZSH (Amorpha fruticose), and SL (Salix cheilophila). These samples were then used for indoor cultivation. Experiments with these four leaf litter types were carried out; one treatment with no litter added served as the control. Our aim was to systematically study the changing characteristics of enzyme activities related to soil carbon, nitrogen, and phosphorus with different litter inputs. The results indicate the following: (1) Compared to the control treatment with no litter added (CK), the addition of all four types of litter significantly increased soil organic carbon, total nitrogen, and alkaline nitrogen contents. The addition of NT and YC litter significantly increased dissolved organic carbon, microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN). (2) The addition of the four types of litter had different effects on the soil enzyme activity, showing increasing trends overall. A chemical analysis of the enzyme activity revealed that the soil was limited in nitrogen and phosphorus. After the addition of the ZSH, NT, and YC litter, the enzymatic C/P acquisition ratio (E_C/P) and enzymatic N/P acquisition ratio (E_N/P) decreased significantly, alleviating the limitation of phosphorus. After the addition of the NT litter, the enzymatic C/N acquisition ratio (E_C/N) increased significantly, alleviating the limitation of soil nitrogen. (3) A correlation analysis showed that the soil nutrients had varying degrees of correlation with enzyme activity and their stoichiometric ratio. The redundancy analysis results show that MBN, TN, MBC/MBN, organic carbon, and available nitrogen were key factors influencing soil enzyme activity and stoichiometric ratios. These results provide a reference for nutrient cycling during sandy soil restoration, and they provide essential data support for the development of fragile ecosystem models in the context of global change. Full article

The purpose of this study was to evaluate the effects of different nitrogen (N), phosphorus (P), and potassium (K) fertilization ratios on the carbon (C), N, and P contents and their ecological stoichiometric characteristics in the leaf–soil–microbial system of Sapindus saponaria and elucidate their relationship with yield. A “3414” experimental design was employed in a 6-year-old Sapindus saponaria woodland located in Fujian Province of China. Fourteen N–P–K fertilization treatments with three replicates were established. Leaf, soil, and microbial samples were collected and analyzed for C, N, and P contents. Redundancy Analysis (RDA), Partial Least Squares Path Modeling (PLS–PM), and the entropy-weighted technique of ranking preferences by similarity to optimal solutions (TOPSIS) were utilized to assess the relationships among variables and determine optimal fertilization strategies. It was found through research that different fertilization treatment methods have a significant impact on both the soil nutrient content and the C, N, and P contents of soil microorganisms. Compared with the control group, soil organic C, total N, and total P, and microbial C, N, and P contents increased by 14.25% to 52.61%, 3.90% to 39.84%, 9.52% to 150%, 6.65% to 47.45%, 11.84% to 46.50%, and 14.91% to 201.98%, respectively. Results from Redundancy Analysis (RDA) indicated that soil organic C, total N, and total P exerted a significant influence on the leaf nutrients. PLS-PM demonstrated that fertilization indirectly affected leaf nutrient accumulation and yield by altering soil properties, with soil total phosphorus and leaf phosphorus being key determinants of yield. Additionally, soil microbial entropy impacted yield by regulating microbial biomass stoichiometric ratios. The entropy-weighted TOPSIS model identified the N₂P₂K₂ treatment (600 kg/ha N, 500 kg/ha P, and 400 kg/ha K) as the most effective fertilization strategy. Optimizing N–P–K fertilization ratios significantly enhances leaf nutrient content and soil microbial biomass C, N, and P, thereby increasing Sapindus saponaria yield. This research clarifies the underlying mechanisms through which fertilization exerts an impact on the C–N–P stoichiometry within the leaf–soil–microbial system. Moreover, it furnishes a scientific foundation for the optimization of fertilization management strategies in Sapindus saponaria plantations. Full article