Search Results (8,787)

2,5-bis(hydroxymethy)lfuran (BHMF), a renewable compound with extensive industrial applications, can be obtained by selective hydrogenation of the C=O group of 5-hydroxymethylfurfural (HMF), a platform molecule derived from lignocellulosic biomass. In this work, we perform kinetic modeling of the selective liquid-phase hydrogenation of HMF to BHMF over a Cu/SiO₂ catalyst prepared by precipitation–deposition (PD) at a constant pH. Physicochemical characterization, using different techniques, confirms that the Cu/SiO₂–PD catalyst is formed by copper metallic nanoparticles of 3–5 nm in size highly dispersed on the SiO₂ surface. Before the kinetic study, the Cu/SiO₂-PD catalyst was evaluated in three solvents: tetrahydrofuran (THF), 2-propanol (2-POH), and water. The pattern of catalytic activity and BHMF yield for the different solvents was THF > 2-POH > H₂O. In addition, selectivity to BHF was the highest in THF. Thus, THF was chosen for further kinetic study. Several experiments were carried out by varying the initial HMF concentration (C⁰_HMF) between 0.02 and 0.26 M and the hydrogen pressure (P_H2) between 200 and 1500 kPa. In all experiments, BHMF selectivity was 97–99%. By pseudo-homogeneous modeling, an apparent reaction order with respect to HFM close to 1 was estimated for a C⁰_HMF between 0.02 M and 0.065 M, while when higher than 0.065 M, the apparent reaction order changed to 0. The apparent reaction order with respect to H₂ was nearly 0 when C⁰_HMF = 0.13 M, while for C⁰_HMF = 0.04 M, it was close to 1. The reaction orders estimated suggest that HMF is strongly absorbed on the catalyst surface, and thus total active site coverage is reached when the C⁰_HMF is higher than 0.065 M. Several Langmuir–Hinshelwood–Hougen–Watson (LHHW) kinetic models were proposed, tested against experimental data, and statistically compared. The best fitting of the experimental data was obtained with an LHHW model that considered non-competitive H₂ and HMF chemisorption and strong chemisorption of reactant and product molecules on copper metallic active sites. This model predicts both the catalytic performance of Cu/SiO₂-PD and its deactivation during liquid-phase HMF hydrogenation. Full article

The kinetics of insulin aggregation and fibril formation were studied in vitro using Scanning Electron Microscopy (SEM) and Fourier Transform Infrared (FTIR) spectroscopy. Our investigation centered on the protein’s morphological and structural changes to better understand the transient molecular configurations that occur during the lag phase. SEM images showed that, already at early incubation stages, a network of disordered pseudo-filaments, ranging in length between 200 and 500 nanometers, develops on the surface of large aggregates. At later stages, fibrils catalyzed by protein aggregates were observed. Principal Component Analysis (PCA) of the FTIR data identified signatures of intramolecular β-sheet secondary structures forming during the lag phase and at the onset of the exponential growth phase. These absorption bands are linked to secondary nucleation mechanisms due to their transient nature. This interpretation is further supported by a chemical equilibrium model, which yielded a reliable secondary nucleation rate constant, K₂, on the order of 10⁴ M⁻² s⁻¹. Full article

Accurate and non-destructive monitoring of leaf chlorophyll content (LCC) is essential for assessing crop photosynthetic activity and nitrogen status in precision agriculture. This study introduces a phenology-aware machine learning framework that combines hyperspectral reflectance data with various regression models to estimate leaf chlorophyll content (LCC) in cotton at six key reproductive stages. Field experiments utilized synchronized spectral and SPAD measurements, incorporating spectral transformations—such as vegetation indices (VIs), first-order derivatives, and trilateration edge parameters (TEPs, a new set of geometric metrics for red-edge characterization)—for evaluation. Five regression approaches were evaluated, including univariate and multivariate linear models, along with three machine learning algorithms: Random Forest, K-Nearest Neighbor, and Support Vector Regression. Random Forest consistently outperformed the other models, achieving the highest R² (0.85) and the lowest RMSE (4.1) during the bud stage. Notably, the optimal prediction accuracy was achieved with fewer than five spectral features. The proposed framework demonstrates the potential for scalable, stage-specific monitoring of chlorophyll dynamics and offers valuable insights for large-scale crop management applications. Full article

Model-based sensorless control of interior permanent-magnet synchronous motors (IPMSMs) typically employs an estimation observer with embedded position information, followed by a position extraction process. Although a type-2 phase-locked loop (PLL) is widely adopted for position and speed extraction, it suffers from steady-state tracking errors under variable-speed operation, leading to torque bias in IPMSM torque control. To mitigate this issue, this paper first proposes an adaptive bandpass full-order observer in the stationary reference frame. Subsequently, a Kalman filter (KF)-based compensation strategy is introduced for the PLL to eliminate tracking errors while maintaining system stability. Experimental validation on a 300 kW platform confirms the effectiveness of the proposed sensorless torque control algorithm, demonstrating significant reductions in position error and torque fluctuations during acceleration and deceleration. Full article

For the high-voltage AC port of power electronic transformer (HVAC-PET) with three-phase independent DC buses on the low-voltage side, a decoupling control strategy, concerning the influence of grid voltage imbalance, three-phase active-load imbalance, and high-order harmonic distortion, is proposed in this paper to simultaneously realize the functions of active power control, reactive power compensation, and active power filtering. In the outer power control loop, according to the distribution rule of decoupled average active power components in three phases, stability control for the sum of cluster average active power flows is realized by injecting positive-sequence active current, so as to control the average cluster voltage (i.e., the average of all the DC-link capacitor voltages), and by injecting negative-sequence current, the cluster average active power flows can be controlled individually to balance the three cluster voltages (i.e., the average of the DC-link capacitor voltages in each cluster). The negative-sequence reactive power component is considered to realize the reactive power compensation. In the inner current control loop, the fundamental and high-order harmonic components are uniformly controlled in the positive-sequence dq frame using the PI + VPIs (vector proportional integral) controller, and the harmonic filtering function is realized while the fundamental positive-sequence current is adjusted. Experiments performed on the 380 V/50 kVA laboratory HVAC-PET verify the effectiveness of the proposed control strategy. Full article

In this study, particles of ground grape seeds were utilized to adsorb phosphate ions from a prepared solution, aiming to reduce phosphate concentration. Through a series of adsorption experiments, the effects of the adsorbent concentration, initial phosphate ion concentration, temperature, and pH on the phosphate ion uptake were studied. The removal efficiency of the phosphate ion decreased from 77 to 61% as a 25 to 45 °C increment in temperature was observed, which indicated the exothermicity in the adsorption process. The phosphate ion movement onto the adsorbent surface that exhibited the highest uptake value favored a neutral reaction environment with a pH value of seven. The experimental results, when compared using different adsorption isotherms, showed that the best fit was exhibited by the Jovanovic isotherm, which was further confirmed owing to its high 0.974 R² value. Intraparticle diffusion and pseudo second order models describe the kinetics of phosphate adsorption onto grape seeds, with reaction constants of 8.8 × 10⁻³ (mg/g min) and 0.412 (mg/g·min^0.5), respectively. The adsorption was physiosorptive, spontaneous, exothermic, and favorable. Furthermore, the negative entropy with a value of −0.0887 kJ/mol·K revealed reduced randomness in the adsorption process system. Full article

Stoichiometric single crystals of Mn₄Al₁₁ were synthesized from the elements using Sn as a flux. The crystal structure of Mn₄Al₁₁ was investigated using single crystal X-ray diffraction and showed a complex triclinic structure with a relatively small unit cell and interpenetrating networks of Mn and Al atoms. While our results generally agree with the previously reported data in the basic structure features such as triclinic symmetry and structure type, the atomic parameters differ significantly, likely due to different synthetic techniques producing off-stoichiometry or doped crystals used in the previous works. Our structural analysis showed that the view of the Mn substructure as isolated zigzag chains is incomplete. Instead, the Mn chains are coupled in corrugated layers by long Mn-Mn bonds. The high quality of the crystals with the stoichiometric composition also enabled us to study magnetic behavior in great detail and reveal previously unobserved magnetic ordering. Our magnetization measurements showed that Mn₄Al₁₁ is an antiferromagnet with T_N of 65 K. The presence of the maximum above T_N also suggests strong local interactions indicative of low-dimensional magnetic behavior, which likely stems from lowered dimensionality of the Mn substructure. Full article

Frame-wise steganalysis is a crucial task in low-bit-rate speech streams that can achieve active defense. However, there is no common theory on how to extract steganalysis features for frame-wise steganalysis. Moreover, existing frame-wise steganalysis methods cannot extract fine-grained steganalysis features. Therefore, in this paper, we propose a frame-wise steganalysis method based on mask-gating attention and bilinear codeword feature interaction mechanisms. First, this paper utilizes the mask-gating attention mechanism to dynamically learn the importance of the codewords. Second, the bilinear codeword feature interaction mechanism is used to capture an informative second-order codeword feature interaction pattern in a fine-grained way. Finally, multiple fully connected layers with a residual structure are utilized to capture higher-order codeword interaction features while preserving lower-order interaction features. The experimental results show that the performance of our method is better than that of the state-of-the-art frame-wise steganalysis method on large steganography datasets. The detection accuracy of our method is 74.46% on 1000K testing samples, whereas the detection accuracy of the state-of-the-art method is 72.32%. Full article

Prolonged drought and soil degradation severely affect soil fertility and limit crop productivity. Superabsorbent hydrogels offer an effective solution for improving water retention in soil and supporting plant growth. In this work, we examined the performance of superabsorbent hydrogels based on sodium alginate, acrylic acid (AA), and poly (ethylene oxide) (PEO) cross-linked with 12.5 kGy using e-beam irradiation. The hydrogels were assessed in various aqueous environments by examining network characteristics, swelling capacity, and swelling kinetics to evaluate the impact of water’s electrical conductivity (which ranges from 0.05 to 321 μS/cm). Morphological and chemical structure changes were evaluated using SEM and FTIR techniques. The results demonstrated that water conductivity significantly affected the physicochemical properties of the hydrogels. Swelling behavior showed notable sensitivity to electrical conductivity variations, with swelling degrees reaching 28,400% at 5 μS/cm and 14,000% at 321 μS/cm, following first-order and second-order kinetics. FTIR analysis confirmed that structural modifications correlated with water conductivity, particularly affecting the O–H, C–H, and COOH groups sensitive to the ionic environment. SEM characterization revealed a porous morphology with an interconnected microporous network that facilitates efficient water diffusion. These hydrogels show exceptional swelling capacity and are promising candidates for sustainable agriculture applications. Full article

With the rapid development of information technology and semiconductor technology, the iteration speed of electronic devices has accelerated in an unprecedented manner, and the market demand for miniaturized, highly integrated, and highly intelligent devices continues to rise. But when these electronic devices operate at high power, the electronic components generate a large amount of integrated heat. Due to the limitations of existing heat dissipation channels, the current heat dissipation performance of electronic packaging materials is struggling to meet practical needs, resulting in heat accumulation and high temperatures inside the equipment, seriously affecting operational stability. For electronic devices that require high energy density and fast signal transmission, improving the heat dissipation capability of electronic packaging materials can significantly enhance their application prospects. In order to improve the thermal conductivity of composite materials, hexagonal boron nitride (h-BN) was selected as the thermal filling material in this paper. The BMI resin was structurally modified through molecular structure design. The results showed that the micro-branched structure and h-BN synergistically improved the thermal conductivity and insulation performance of the composite material, with a thermal conductivity coefficient of 1.51 W/(m·K) and a significant improvement in insulation performance. The core mechanism is the optimization of the dispersion state of h-BN filler in the matrix resin through the free volume in the micro-branched structure, which improves the thermal conductivity of the composite material while maintaining high insulation. Full article

Community detection in complex networks plays a pivotal role in modern scientific research, including in social network analysis and protein structure analysis. Traditional community detection methods face challenges in integrating heterogeneous multi-source information, capturing global semantic relationships, and adapting to dynamic network evolution. This paper proposes a novel unsupervised multimodal community detection algorithm (UMM) based on fractal iteration. The core idea is to design a dual-channel encoder that comprehensively considers node semantic features and network topological structures. Initially, node representation vectors are derived from structural information (using feature vectors when available, or singular value decomposition to obtain feature vectors for nodes without attributes). Subsequently, a parameter-free graph convolutional encoder (PFGC) is developed based on fractal iteration principles to extract high-order semantic representations from structural encodings without requiring any training process. Furthermore, a semantic–structural dual-channel encoder (DC-SSE) is designed, which integrates semantic encodings—reduced in dimensionality via UMAP—with structural features extracted by PFGC to obtain the final node embeddings. These embeddings are then clustered using the K-means algorithm to achieve community partitioning. Experimental results demonstrate that the UMM outperforms existing methods on multiple real-world network datasets. Full article

The removal of high-concentration m-cresol from industrial wastewater remains a significant challenge due to its toxicity and persistence. In this study, a commercially available functionalized resin with a high BET surface area (1439 m² g⁻¹) and hierarchical pore structure was employed for the adsorption of pure m-cresol at an initial concentration of 20 g L⁻¹, representative of coal-based industrial effluents. Comprehensive characterization confirmed the presence of oxygen-rich functional groups, amorphous polymeric structure, and uniform surface morphology conducive to adsorption. Batch experiments were conducted to evaluate the effects of resin dosage, contact time, temperature, and equilibrium concentration. Under optimized conditions (0.15 g resin, 60 °C), a maximum adsorption capacity of 556.3 mg g⁻¹ and removal efficiency of 71% were achieved. Kinetic analysis revealed that the pseudo-second-order model best described the adsorption process (R² > 0.99). Isotherm data fit the Langmuir model most closely (R² = 0.9953), yielding a monolayer capacity of 833.3 mg g⁻¹. Thermodynamic analysis showed that adsorption was spontaneous (ΔG° < 0), endothermic (ΔH° = 7.553 kJ mol⁻¹), and accompanied by increased entropy (ΔS° = 29.90 J mol⁻¹ K⁻¹). The good agreement with the PSO model is indicative of chemisorption, as supported by other lines of evidence, including thermodynamic parameters (e.g., positive ΔH° and ΔS°), surface functional group characteristics, and molecular interactions. The adsorption mechanism was elucidated through comprehensive modeling of adsorption kinetics, isotherms, and thermodynamics, combined with detailed physicochemical characterization of the resin prior to adsorption, reinforcing the mechanistic understanding of m-cresol–resin interactions. Full article

In this study, a novel direct laser beam turning (DLBT) approach is proposed for the precision machining of AISI 308L austenitic stainless steel, which eliminates the need for cutting tools and thereby eradicates tool wear and vibration-induced surface irregularities. A nanosecond-pulsed Nd:YAG fiber laser (λ = 1064 nm, spot size = 0.05 mm) was used, and Ø1.6 mm × 20 mm cylindrical rods were processed under ambient conditions without auxiliary cooling. The experimental framework systematically evaluated the influence of scanning speed, pulse frequency, and the number of laser passes on dimensional accuracy and material removal efficiency. The results indicate that a maximum diameter reduction of 0.271 mm was achieved at a scanning speed of 3200 mm/s and 50 kHz, whereas 0.195 mm was attained at 6400 mm/s and 200 kHz. A robust second-order polynomial correlation (R² = 0.99) was established between diameter reduction and the number of passes, revealing the high predictability of the process. Crucially, when the scanning speed was doubled, the effective fluence was halved, considerably influencing the ablation characteristics. Despite the low fluence, evidence of material evaporation at elevated frequencies due to the incubation effect underscores the complex photothermal dynamics governing the process. This work constitutes the first comprehensive quantification of pass-dependent diameter modulation in DLBT and introduces a transformative, noncontact micromachining strategy for hard-to-machine alloys. The demonstrated precision, repeatability, and thermal control position DLBT as a promising candidate for next-generation manufacturing of high-performance miniaturized components. Full article

This study investigates how phospholipid headgroups influence passive membrane penetration and structural impact of four nitrogen-, sulfur-, and oxygen-containing heterocycles (NSO-HETs)—N-methyl-2-pyrrolidone (PIR), 1,4-dioxane (DIOX), oxane (OXA), and phenol (PHE). Using all-atom molecular dynamics simulations combined with Accelerated Weight Histogram free energy calculations, the passive transport of NSO-HETs across DPPC, DPPE, DPPA, and DPPG bilayers was characterized. DPPG showed the highest membrane affinity, increasing permeability (logP_memb/bulk) by 27–64% compared to DPPE, associated with the lowest permeability and tightest lipid packing. Free energy barriers are also decreased in DPPG relative to DPPE; PIR’s central barrier dropped from 19.2 kJ/mol (DPPE) to 16.6 kJ/mol (DPPG), while DIOX’s barrier decreased from 7.2 to 5.2 kJ/mol. OXA exhibited the lowest central barriers (1.2–2.2 kJ/mol) and uniquely accumulated at higher concentrations in the bilayer center than in bulk water, with free energy ranging from −3.4 to −5.9 kJ/mol. PHE and OXA caused significant bilayer thinning (up to 11%) and reduced lipid tail order, especially in DPPE and DPPA. Concentration effects were most pronounced in DPPE, where high solute loading disrupted lipid order and altered free energy profiles. These results highlight the crucial role of headgroup identity in modulating NSO-HET membrane permeability and structural changes. Full article

Background/Objectives: To investigate acute caffeine (CAF: 375 mg, ≈4.8 mg/kg body mass) effects on energy expenditure (EE) and substrate kinetics during high-intensity interval exercise in individuals with high (HBAT) versus low (LBAT) brown adipose tissue activity using time-trend polynomial modeling. Methods: This is a randomized, double-blind crossover study in which 35 highly-trained males [HBAT-CAF, HBAT-PLA (Placebo), LBAT-CAF, LBAT-PLA] performed 30-min treadmill HIIE. Infrared thermography (IRT) assessed BAT activity by measuring supraclavicular skin temperature (SST). Breath-by-breath ergospirometry measured EE (kcal/min) and carbohydrate (CHO), lipid (LIP), and protein (PTN) oxidation. We applied second- and third-order polynomial regression models to depict the temporal trajectories of metabolic responses. Results: HBAT groups showed 25% higher sustained EE versus LBAT (p < 0.001), amplified by CAF. CHO oxidation exhibited biphasic kinetics: HBAT had 40% higher initial rates (0.75 ± 0.05 vs. 0.45 ± 0.04 g/min; p < 0.001) with accelerated decline (k = −0.21 vs. −0.15/min; p = 0.01). LIP oxidation peaked later in LBAT (40 vs. 20 min in HBAT), with CAF increasing oxidation by 18% in LBAT (p = 0.01). HBAT-CAF uniquely showed transient PTN catabolism (peak: 0.045 g/min at 10 min; k = −0.0033/min; p < 0.001). Conclusions: BAT status determines EE magnitude and substrate-specific kinetic patterns, while CAF exerts divergent modulation, potentiating early glycogenolysis in HBAT and lipolysis in LBAT. The HBAT-CAF synergy triggers acute proteolysis, revealing BAT-mediated metabolic switching. Full article