Search Results (379)

To elucidate the mechanisms of nutrient cycling in rhizosphere soil and microbial metabolism during the prolonged continuous cropping of lavender, this study examined the rhizosphere soil of lavender with different continuous cropping years (1, 4, 7, 10, 15, and 20 years) in the Ili River Valley of Xinjiang, China, measuring physicochemical properties, microbial biomass C/N/P, and eight extracellular enzyme activities. Microbial carbon use efficiency (CUE) and nutrient limitation were quantified using vector analysis, threshold elemental ratios (TERs), and two derived indices (TER_EEA and TER_L). Soil properties exhibited distinct nonlinear patterns: SOC peaked at 4 years (p < 0.05), TN was highest at 20 years, and TP was lowest at 4–7 years. MBC and MBN peaked at 20 years, whereas MBP was significantly lower than in 1-, 4-, and 10-year fields (p < 0.05). EEC and EEN were highest at 20 years, while EEP was lowest at 4 years (p < 0.05). The activity of carbon-related acquisition enzymes increases from 134.81 μmol/g·h in the first year to 393.86 μmol/g·h in the 20th year, an increase of 192%; the activity of nitrogen acquisition enzymes increases from 686.11 μmol/g·h in the first year to 1430.58 μmol/g·h in the 20th year, an increase of 108%. This indicates that the decomposition of organic matter and the nutrient cycling capacity continue to enhance. Vector analysis showed a mean VA of 46° and VL of 0.25, with VA > 45° (P limitation) at 1–4 years shifting to VA < 45° (N limitation) at 20 years. Critically, TEREEA and TERL produced opposite dominant limitations due to differing normalization frameworks—TEREEA scales by microbial biomass stoichiometry—while TERL normalizes against enzyme-derived thresholds. CUET and CUEE ranged from 0.42 to 0.56, with the minimum at 10 years and relatively high values at 15–20 years (p < 0.05). RDA identified CBH (26.2%) and NO₃⁻–N (19.8%) as primary drivers, with extractable phosphorus exhibiting the strongest regulatory effect (pseudo-F = 26.0). These results demonstrate that multi-model stoichiometric assessment is essential, as single indices may yield contradictory diagnoses. These results demonstrate that multi-model stoichiometric assessment is essential, as single indices may yield contradictory diagnoses, and the observed nonlinear shifts in dominant limitation type provide a mechanistic basis for targeted nutrient management in sustainable lavender cultivation. Full article

Sulfate roasting is a critical pyrometallurgical pre-treatment for extracting Cu and Co from low-grade Cu–Co–Fe sulfide ores, yet conventional phase diagrams provide limited quantitative guidance for process control. To address this gap, a multicomponent/multiphase thermodynamic equilibrium model based on Gibbs free energy minimization was developed to systematically investigate the oxidative roasting behavior of single sulfides (Cu₂S, CoS₂, FeS₂) and their ternary mixture, with respect to air supply, temperature, and total pressure. The model reveals that each sulfide follows distinct, temperature-dependent phase transformation pathways: Cu₂S forms the acid-leachable product CuO·CuSO₄ at temperatures ≤588 °C with a stoichiometric air supply of 11.9 mol, transitioning to oxides at ≥800 °C; CoS₂ converts completely to CoSO₄ below 727 °C and to CoO at higher temperatures; FeS₂ yields sulfate phases at low temperatures and iron oxides above 654 °C. In the ternary Cu₂S–CoS₂–FeS₂ system, competitive oxidation reactions produce refractory mixed oxides (CuO·Fe₂O₃, CoO·Fe₂O₃) whose formation is governed by temperature, air supply, and sulfide molar ratios. The results demonstrate that low-temperature roasting (≤641 °C) with precisely controlled air supply maximizes the formation of water-soluble sulfates, providing a quantitative thermodynamic basis for process optimization and enhanced recovery of Cu and Co from complex sulfide ores. Full article

Internal combustion engines fueled by fossil fuels are major contributors to greenhouse gas (GHG) emissions. This study analyzes and predicts GHG emissions from hydrogen-enriched compressed natural gas (HCNG)-fueled spark-ignition (SI) engines. Experiments were conducted under stoichiometric conditions, and emissions before and after the three-way catalytic converter (TWC) were analyzed by varying hydrogen fraction (0–50%), EGR ratio (0–23%), engine speed (900 rpm–1500 rpm), engine load (25–75%), and spark timing (8–49 °CA bTDC). Before the TWC, increasing the hydrogen fraction from HCNG0% to HCNG40% at 1500 rpm, 50% load, and 23% EGR reduced total GHG emissions from 184.3 to 65.17 g/kWh. Similarly, for HCNG20% at 900 rpm and 30% load, the TWC reduced the CO₂-equivalent emissions of CO, CH₄, and NOx from 18.531, 8.149, and 9.057 g${\mathrm{C}\mathrm{O}}_{2eq}$/kWh to 7.013, 1.626, and 0.429 g${\mathrm{C}\mathrm{O}}_{2eq}$/kWh, respectively. Pearson correlation analysis revealed strong linear relationships between operating parameters and GHG emissions. Furthermore, emissions were predicted using four Gaussian process regression (GPR) models: Squared, Exponential, Matern 5/2, and Rational. Among these, the Exponential GPR demonstrated the highest predictive accuracy, achieving RMSE values of 0.098, 0.022, and 0.035, with corresponding R² values of 0.999, 0.807, and 0.996 for CO, CH₄, and NOx, respectively. The findings of this study offer valuable insights into GHG emissions and support the development of cleaner, more efficient HCNG engines. Full article

Magnetic sensors based on the colossal magnetoresistance (CMR) effect in manganite thin films are promising for high-field measurements due to their wide operating range, low magnetoresistance anisotropy, and ability to function without full saturation at extremely high magnetic fields. However, the long-term stability of their transport properties remains a key challenge for practical sensor applications. In this work, accelerated aging of nanostructured La_1−xSr_xMn_yO₃ thin films was investigated for two manganese compositions: nominally stoichiometric (y = 1.05) and Mn-excess (y = 1.15). The electrical resistivity and magnetoresistive properties strongly depended on the manganese content and substrate type. Accelerated aging was induced by annealing at 100 °C in an argon atmosphere, and the evolution of the transport properties was analyzed using a stretched-exponential relaxation model. The analysis of the extracted parameters indicated defect-related mechanisms governing transport stability. It was found that despite the increase in resistivity during thermal treatment, the magnetoresistance changes were insignificant. The results provide insights into the aging behavior of nonstoichiometric manganite films and offer guidance for optimizing stabilization procedures in CMR-based magnetic field sensors. Full article

In this study, a multiphysics finite-element model was developed for the deposition of W–C–B coatings in a hot-wall tubular CVD reactor from a gas mixture of tungsten hexafluoride (WF₆), hydrogen (H₂), and trimethylamine borane ((CH₃)₃N:BH₃) at 550 °C and 5 Torr. The aim of this work is to deepen the understanding of reactant transport mechanisms and to optimize the process parameters for obtaining targeted tungsten carbide or boride phases. The simulations were performed in COMSOL Multiphysics (ver. 6.1) using a 2D axisymmetric formulation that couples laminar flow, heat transfer, and multicomponent diffusion, accounting for heterogeneous chemical reactions at the reactor walls. The obtained spatial distributions of reactant concentrations demonstrate precursor depletion along the reactor length. A comparison of the calculated B/W and C/W stoichiometric ratios for 13 operating conditions with experimental data confirms a transition from W and W–B phases at low trimethylamine borane (TMAB) flow rates to tungsten carbide-based coatings at higher flow rates. Furthermore, a parametric sweep was utilized to determine the optimal parameter range for the synthesis of tungsten borides. Full article

This review proposes a “phenomenon–mechanism–regulation” framework for understanding nitrogen immobilization during the conversion of green waste into growing media. Nitrogen immobilization acts as a double-edged sword: intense short-term immobilization, typically occurring within the first 1–2 weeks after substrate establishment, can rapidly deplete mineral nitrogen and induce plant nitrogen deficiency, whereas the immobilized nitrogen is subsequently incorporated into microbial biomass and lignin-associated organic pools, forming a slow-release reservoir that enhances nitrogen retention and reduces leaching losses. Owing to its extremely high C/N ratio (often >100) and the coexistence of labile carbon fractions and recalcitrant compounds (e.g., lignin and phenolics), green waste exhibits substantially stronger immobilization potential than conventional media. Empirical evidence indicates that nitrogen immobilization can reach 10–115 mg N·L⁻¹ within a few days in wood-derived substrates, and additional fertilization of up to 100 mg N·L⁻¹ may be required to maintain crop growth. Mechanistically, nitrogen immobilization is governed by the coupling of microbial assimilation—driven by stoichiometric C/N imbalance (typically triggered when C/N > 20–25)—and abiotic chemical fixation, including reactions between NH₄⁺/NO₂⁻ and lignin-derived phenolics forming stable organic nitrogen compounds. The relative dominance of these pathways is jointly regulated by carbon quality, nitrogen form, and pH. Based on these mechanisms, regulatory strategies are summarized at multiple scales, including feedstock pretreatment to reduce labile carbon availability, substrate formulation to optimize C/N balance, and model-assisted intelligent fertigation to synchronize nitrogen supply with crop demand. Overall, this study provides a theoretical basis for improving green waste valorization and promoting sustainable horticultural production. Full article

Hydrogen-enriched fuel injection in staged gas-turbine combustors is commonly achieved through jet-in-crossflow (JICF) configurations, where flame stabilization is governed by a local balance between flow-induced strain/mixing and chemical reaction rates. This work investigates turbulent reacting JICF relevant to staged combustion conditions using high-fidelity simulations with adaptive mesh refinement (AMR) and differential-diffusion effects together with Lagrangian particle statistics. Chemistry model uncertainties are incorporated by using a projection method that maps uncertainty estimates from detailed mechanisms into the model used in this work. Results show that the macroscopic flame topology remains in a stable two-branch regime (lee-stabilized and lifted) and is primarily controlled by the jet momentum–flux ratio J. Visualization of the normalized scalar dissipation rate reveals that the flame front resides on the low-dissipation side of intense mixing layers, occupying an intermediate region between over-strained and under-mixed regions. While hydrogen content does not significantly change the global stabilization mode for the cases studied, uncertainty analysis reveals composition-dependent differences that are not apparent in the mean behavior alone. In particular, visualization in Eulerian (χ, T) state-space analysis and particle statistics conditioned on the stoichiometric surface demonstrate that higher-hydrogen cases observe a lower scalar dissipation rate and exhibit substantially reduced variability in OH production under kinetic-parameter perturbations, whereas lower-hydrogen blends experience higher dissipation and amplified chemical sensitivity. These findings highlight that, even in globally similar JICF regimes, the hydrogen content can modify the local response of the flame to kinetic-parameter uncertainty, motivating uncertainty-aware interpretation and design for hydrogen-fueled staging systems. Full article

Organoselenium chemistry has undergone remarkable development over the past five decades, evolving from its initial association with high toxicity into a field with pivotal contributions to materials science, organic synthesis, catalysis, and Medicinal Chemistry. Among the diverse biological activities displayed by organoselenium compounds, their redox behaviour is particularly compelling, as many of these molecules act as efficient mimetics of the antioxidant enzyme glutathione peroxidase (GPx). In this work, we investigated the GPx-like activity of a series of N,N′-diaryl selenoureas toward the depletion of H₂O₂ and cumene hydroperoxide (CumOOH) as model ROS. Their reactivity was correlated with the electronic nature of the aryl substituents using a Hammett-type analysis, revealing a strong dependence of the reaction rate on remote electronic perturbations within the aromatic ring. Combined UV and NMR studies provided mechanistic evidence supporting a catalytic cycle in which selenoureas, operating at sub-stoichiometric loadings (1 mol%) and using a thiol as a cofactor-like molecule, can be used to efficiently scavenge ROS with half-lives of only a few minutes (~10–60 min). Furthermore, these selenoureas exhibited potent antiproliferative activity across several human solid tumour cell lines. Overall, these results offer mechanistic insight into the ROS-eliminating pathways of selenoureas and highlight their potential as chemopreventive or anticancer agents. Full article

Trees in arid mountainous forests adapt to seasonal water variability through dynamic eco-physiological adjustments. This study investigated the spatiotemporal dynamics and environmental drivers of such adaptations in Picea schrenkiana Fisch. et Mey, a keystone conifer in China’s Tianshan Mountains. We monitored key indicators—including osmoregulatory substances, antioxidant enzyme activities, and stoichiometric traits—across three regions (eastern, central, western) and three seasons (spring, summer, autumn) during the 2023 growing season. The results revealed significant seasonal shifts in all the measured traits (p < 0.05). Spring was characterized by high carbon allocation toward soluble sugars and starch, supporting growth; summer triggered elevated antioxidant enzyme activities to mitigate oxidative stress; and autumn favored nitrogen accumulation and proline synthesis, indicating preparatory storage for winter. Soil factors were primarily positively associated with antioxidant enzyme activity (path coefficient = 0.51; p < 0.001), whereas microenvironmental factors were more complex and often negatively correlated. The partial least squares path model confirmed that osmoregulatory substances centrally link stoichiometric adjustments with antioxidant defense, revealing an integrated physiological strategy. These findings elucidate the mechanism underlying the resilience of P. schrenkiana in arid highlands and provide a framework for its conservation under environmental change. Full article

Although the dissolution of sparingly soluble salts is of interest to many fields, such as material science, dentistry, and geochemistry, the simplicity of these reactions provides its own motivation for study. Three features of these reactions are examined in this paper: (i) the unusual forms of the kinetic expression that have been used to describe their rates of reaction, (ii) the observation that the rate of dissolution is correlated with the potential difference across the solid-solution interface, and (iii) the observation of non-stoichiometric dissolution. Mechanistic descriptions of the kinetics of dissolution in current use do not account for all these factors, while the surface vacancy model does. In this paper, it is shown that linear kinetics arise from a symmetry of the rates of removal and deposition of anions and cations. On the other hand, non-linear kinetics arise from an asymmetry in the rates of removal and deposition of anions and cations. Because the surface vacancy model is an electrochemical model, the influence of potential difference on the rate of reaction is inherent to the model. A transient, or non-stationary state, version of the model is used to explain how non-stoichiometric dissolution arises. Full article

Methane oxy-combustion is a promising carbon capture pathway due to the high CO₂ concentration in the exhaust; however, combustion in pure oxygen produces excessively high flame temperatures that impair ignition and operational stability. To mitigate these effects, steam dilution is commonly applied, but it significantly prolongs ignition delay time (IDT). To address these limitations, hydrogen enrichment is proposed as a reactivity-enhancement strategy. The objective of this study is to quantify the combined effects of steam dilution and hydrogen enrichment on ignition behaviour, carbon species formation, and flame temperature in methane oxy-combustion, considering both ignition onset and equilibrium combustion states. A detailed numerical investigation is conducted using zero-dimensional constant-pressure simulations with detailed chemical kinetics implemented in Cantera, formulated in mixture-fraction space. IDT, CO/CO₂ formation, and adiabatic flame temperature are analysed over steam dilution levels of 0–40%, hydrogen enrichment up to 5% by mass, and initial temperatures between 1050 and 1200 K. The model is validated against experimental data for adiabatic flame temperature and key radical species. Results demonstrate that steam dilution effectively reduces the peak adiabatic flame temperature (by more than 300 K at 40% steam) and enhances the CO₂ mass fraction in the equilibrium state near the stoichiometric mixture fraction, but increases IDT by approximately 100–200% across the mixture-fraction range. Hydrogen enrichment strongly counteracts this inhibition, reducing IDT by up to one order of magnitude under high steam dilution (30–40%) while simultaneously suppressing CO. At the stoichiometric mixture fraction, H₂ addition decreases equilibrium CO₂ formation, indicating a trade-off between enhanced ignition reactivity and ultimate carbon conversion under equilibrium conditions. The use of steam dilution as a temperature-control strategy and hydrogen enrichment as a reactivity enhancer identifies a favourable mixture-fraction window. Full article

Solid-phase peptide synthesis (SPPS) allows for the sequential assembly of diverse peptide sequences. Alongside its scalability and capacity for automation, this makes it the method of choice for the synthesis of peptide-based pharmaceuticals. SPPS reaction pathways, however, suffer from a negative environmental footprint due to the super-stoichiometric quantities of reagents and high solvent use required to ensure reaction completion. In this paper, we propose the use of charge-based measurements as a complement to optical methods for measuring reaction completion. We extend the capabilities of our hybrid modeling framework to a representative four-step SPPS pathway on SiO₂, showing each reaction intermediate, its molecular encoding, and the resulting modeled surface potential (${\psi }_0$). We show that the simulated ${\psi }_0\left(\mathrm{pH}\right)$ plots are separable for three of the four key reaction steps in the representative pathway, indicating that charge-based measurements may help verify protection/deprotection steps. Full article

The acid dissociation constant (pK_a) is a fundamental parameter governing the environmental fate of organic compounds. Accurate pK_a prediction remains challenging, as traditional binary Morgan fingerprints (B-MF) fail to capture stoichiometric information critical for modeling substituent effects. This study developed an interpretable machine learning framework for pK_a prediction by integrating count-based Morgan fingerprints (C-MF) with ensemble algorithms. Through systematic comparison across four algorithms (Catboost, XGBoost, GBDT, RF), C-MF consistently outperformed B-MF due to its ability to quantify functional group multiplicity. Subsequent SHAP-based recursive feature elimination (SHAP-RFE) optimized the model, identifying Catboost with only 81 features as the optimal architecture, achieving a test-set R² of 0.890 and RMSE of 1.026. SHAP analysis revealed that the model’s decisions are driven by chemically intuitive features, forming a hierarchical framework where primary ionizable sites set the baseline pK_a and electronic modifiers fine-tune it. The applicability domain, defined using the AD_SAL method, yielded high-confidence predictions (R² = 0.926). External validation on an independent open-source dataset containing 6876 acidic compounds, combined with results from AD_SAL application domain characterization, enabled accurate pK_a prediction for 390 compounds within the application domain (R² = 0.890, RMSE = 0.942). This further confirms the model’s strong generalizability. This work provides a robust and generalizable tool for high-performance pK_a prediction, with significant potential for applications in environmental risk assessment. Full article

In this work, a study of the chemical composition of the compound Sb₂(S_1−xSe_x)₃ used in thin-film solar cell fabrication, based on correlating data obtained from XRD and XPS analyses, is presented. This approach enables us to propose a mathematical expression for evaluating stoichiometric variations in the material, showing how the variable x evolves as a function of the diffraction angle 2θ. To establish this model, we analyzed the most intense diffraction peak, corresponding to the (221) plane. To validate the proposed method, a series of Sb₂(S_1−xSe_x)₃ thin films with different compositions were synthesized using RF-magnetron sputtering followed by conventional heat treatments in a controlled-atmosphere reaction furnace. The XRD results reveal a systematic 2θ shift in the crystalline diffraction peaks toward the positions of the binary precursor phases—from Sb₂Se₃ to Sb₂S₃—caused by the increased sulfur content during synthesis. XPS measurements confirm the presence of Sb, Se, and S, and high-resolution spectra indicate a decrease in selenium content as the sulfur fraction increases. These results allowed us to elucidate the stoichiometric behavior of antimony sulfoselenide Sb₂(S_1−xSe_x)₃ using trend curves fitted to the characterization data. Full article

Perovskite oxides show excellent catalytic performance for thermochemical CO₂ splitting, with A/B-site cation substitution further enhancing redox activity. While traditional first-principles methods are computationally expensive, machine learning (ML) provides an efficient approach to perovskite optimization. In this paper, machine learning is employed to investigate and predict the performance of perovskite catalysts in CO₂ decomposition reactions. Based on 227 perovskite compositions (A₁A₂)(B₁B₂)O₃ curated from experimental literature, a total of five ML models are used, including Decision Tree, Bagging, Random Forest, Extra Trees, and Gradient Boosting Regression (GBR). The Random Forest model performed best. After hyperparameter optimization, the Random Forest model achieved an R² of 0.910 and an MAE of 41.528 on an independent test set. SHAP analysis indicated that the thermal reduction temperature (T₁) and the B1-site stoichiometric fraction (C_b₁) are the most influential features governing the predicted CO yield. A higher CO yield is predicted when C_b₁ ranges from 0.6 to 0.8, and T₁ exceeds 1300 °C. This behavior can be attributed to the enhanced formation of oxygen vacancies at elevated temperatures and the optimized electronic structure induced by appropriate B-site stoichiometry. Full article