Search Results (18,382)

The depletion of fossil fuel reserves and their environmental impact have driven the search for sustainable energy alternatives. Biodiesel has emerged as a promising substitute. Being a major byproduct of the coffee industry, spent coffee grounds (SCGs) offer a viable feedstock due to their abundance, high fatty acid content, and calorific value. This study explores biodiesel production from SCGs. First, oil was experimentally extracted from SCGs using Soxhlet extraction with hexane as the solvent. The oil yield varied between 12 and 13.4% with a density of 0.9 g/mL. Reactor modeling and kinetic analysis were performed, showing that CSTRs in series are favorable for the esterification and transesterification reactions. Furthermore, Aspen Plus was used to simulate the extracted oil conversion into biodiesel through a two-step esterification and purification process. The simulation results are verified against previous experimental research. Sensitivity analyses were performed to evaluate the influence of key process parameters, including methanol-to-oil ratio, reactor residence time, and transesterification temperature. The simulation results indicate an optimal biodiesel mass yield of 90.31%, with a purity of 99.63 wt%, at a methanol-to-oil ratio of 12:1 and a transesterification temperature of 60 °C. Full article

Boric acid/β-CD-based polymers (BA-CD) possess hierarchical porous structures and efficient functional groups for further molecular recognition, which are used for the adsorption of a series of cationic and anionic organic dyes. The effects of pH, contact time, initial concentration of solution, and temperature on the adsorption performance were experimentally investigated in detail. Surprisingly, the adsorption capacities of BA-CD towards RB exhibited a higher value of 733.2 mg g⁻¹ among a series of cationic and anionic dyes. The adsorption kinetics further indicated that the adsorption of dyes by BA-CD belonged to a quasi-second-order kinetic model, while the adsorption isotherms demonstrated the adsorption process as the Langmuir isotherm model. The characterization of the adsorption process was performed in the presence of monomolecular layer chemisorption. In addition, the reusability test showed that BA-CD had a high reusability rate of 90% in MG after five cycles, indicating its future potential for the treatment of dye wastewater. Full article

This work presents a high-common-mode-rejection-ratio (CMRR) and high-gain FinFET-based bio-potential amplifier with a novel CMRR reduction technique. In this paper, a feedback buffer is used alongside a capacitively coupled chopper-stabilized circuit to reduce the common-mode signal gain, thus boosting the overall CMRR of the circuit. The conventional pseudoresistor in the feedback circuit is replaced with a tunable parallel-cell configuration of pseudoresistors to achieve high linearity. A chopper spike filter is used to mitigate spikes generated by switching activity. The mid-band gain of the chopper-stabilized amplifier is 42.6 dB, with a bandwidth in the range of 6.96 Hz to 621 Hz. The noise efficiency factor (NEF) of the chopper-stabilized amplifier is 6.1, and its power dissipation is 0.92 µW. The linearity of the parallel pseudoresistor cell is tested for different tuning voltages (V_tune) and various numbers of parallel pseudoresistor cells. The simulation results also demonstrate the pseudoresistor cell performance for different process corners and temperature changes. The low cut-off frequency is adjusted by varying the parameters of the parallel pseudoresistor cell. The CMRR of the chopper-stabilized amplifier, with and without the feedback buffer, is 106.9 dB and 100.3 dB, respectively. The feedback buffer also reduces the low cut-off frequency, demonstrating its multi-utility. The proposed circuit is compatible with bio-signal acquisition and processing. Additionally, a machine learning-based arrhythmia diagnosis model is presented using a convolutional neural network (CNN) + Long Short-Term Memory (LSTM) algorithm. For arrhythmia diagnosis using the CNN+LSTM algorithm, an accuracy of 99.12% and a mean square error (MSE) of 0.0273 were achieved. Full article

The present study investigates post-emplacement zeolitization processes in two widespread pyroclastic units from Central Italy: the Cimina Ignimbrite and the Sorano Ignimbrite. A total of seventy-five samples from ten outcrops were analyzed using optical and environmental scanning electron microscopy, electron probe microanalysis, X-ray powder diffraction, and inductively coupled plasma optical emission spectrometry. Analytical results allow the mineral distribution, zeolite composition, textural relationships, and geochemical features of the zeolite-bearing rocks to be defined. In the Cimina Ignimbrite, zeolitization affects the glassy portion of the groundmass, where the glass transforms into a medium- to high-temperature mineral assemblage dominated by clinoptilolite-Ca and cristobalite. This transformation is restricted to the innermost parts of the deposit. In contrast, zeolitization in the Sorano Ignimbrite involves the entire glassy fraction of pumice clasts, with extensive alteration of the glass into medium- to low-temperature zeolites such as chabazite-K and phillipsite-K. The results reveal a significant correlation between the chemical composition of the juvenile material and that of the newly formed zeolites in both types of ignimbrites, particularly in the Sorano Ignimbrite. Zeolitization in Central Italy ignimbrites likely occurs in a natural autoclave-like setting, where hot fluids remain trapped in the deposit for a long time. Full article

Efficient drying is crucial for the preservation and high-value utilization of tricholoma matsutake (TM). Traditional hot-air drying is inefficient, energy-intensive, and prone to quality degradation. This study investigates the application of microwave drying for TM, systematically analyzing its dielectric properties and moisture states, and elucidating the dielectric response mechanisms during drying. Response surface methodology (RSM) was employed to optimize key process parameters, including microwave power, drying time, and sample mass, and to validate the feasibility of the optimized process for industrial applications. Results revealed that the dehydration process of TM comprises three distinct stages, with free water evaporation contributing 69.8% of the total weight loss. Dielectric properties correlated strongly with apparent density and temperature, with the loss tangent ($\mathit{tan}\delta$) increasing by 213.0% at higher temperatures, confirming dipole loss as the primary heating mechanism. Under optimized drying conditions (power: 620.00 W, time: 2.70 min, mass: 13.2 g), a dehydration rate (DR) of 85.41% was achieved, with a 1.50% deviation from the model-predicted values. The optimized process effectively maintained the relative integrity of the microstructure of TM, with the C/O ratio increasing from 1.03 to 1.31. Steam pressure-driven moisture migration was identified as the primary mechanism facilitating microwave-enhanced dehydration. Pilot-scale experiments scaled up the processing capacity to 15 kg/h and confirmed that the new process reduced total costs by 38% compared to traditional hot-air drying. The study developed an efficient and reliable microwave drying model, supporting industrial-scale TM processing. Full article

The deep carbonate reservoirs in the Yingzhong Block of the Qaidam Basin exhibit strong vertical heterogeneity and complex natural fracture development. Conventional fracability evaluation methods struggle to accurately characterize formation features, thereby affecting the stimulation effectiveness. To enhance the evaluation accuracy of fracturing sweet spot intervals, automatic mineral scanning equipment is employed to obtain formation micro-physical property parameters at continuous depths. Considering the temperature-pressure coupling effect under deep conditions, a rock mechanics computational model based on mineral composition was established to derive macroscopic mechanical parameters such as brittleness index and in situ stress. Based on a combined algorithm of the improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP), a fracturing sweet spot prediction model integrating micro- and macro-multi-factors is established, and sweet spot index levels are classified. The research results indicate that the rock mechanics computational model demonstrates high accuracy, the calculated macroscopic parameters are reliable, and the fracturing sweet spot index model can fracability and meticulously evaluate the characteristics of deep carbonate formations. The fracturing sweet spots can be classified into three levels: Level I with an index higher than 0.50, Level II with an index between 0.35 and 0.50, and Level III with an index lower than 0.35. After using this method for layer selection, the fracture pressure decreases by 11.6%, and the sand addition success rate increases by 24%. Applying this method to guide the optimization of fracturing intervals demonstrates good on-site practical value, providing an important reference for identifying fracturing sweet spots in deep carbonate reservoirs. Full article

This study examines ground surface and air temperatures and their implications for periglacial activity in the Țarcu Massif, Southern Carpathians, where data on current dynamics and climate responses remain scarce despite widespread periglacial landforms. To address this, we deployed seven temperature loggers between 2018 and 2024 across a range of periglacial landforms, including non-sorted patterned ground, a periglacial hummock, protalus rampart, block stream, periglacial tor, ploughing boulder, and nival niche. We analyzed key thermal indicators such as freeze–thaw cycles, freezing and thawing degree days, frost weathering intervals, frost days, and winter equilibrium temperatures—in relation to long-term air temperature records (1961–2023), snow cover dynamics, and local topographic and substrate conditions. Results reveal a marked warming trend at the Țarcu meteorological station, particularly after 1995, along with a shift in net thermal balance beginning in the late 1990s. Since then, climatic conditions at this site have no longer been favorable for the persistence of sporadic permafrost. Ground thermal conditions varied spatially, with coarse debris sites and rock wall maintaining the lowest MAGST values—typically with 1 to 2.5 °C cooler than fine-grained sediments—and the highest potential for frost-related weathering. Despite low and variable freeze–thaw cycle frequency, the high number of frost days (around 200 per year) and sustained frost weathering potential—exceeding 50 days annually at key sites—indicate that periglacial conditions remain active for nearly half the year around 2000 m in the Southern Carpathians. Snow cover dynamics proved to be a major control on ground thermal behavior, with earlier melting and delayed onset shortening its duration but amplifying early winter cooling. These findings indicate that the Țarcu Massif is a transitional periglacial environment, where active and relict features coexist under growing climatic pressure. The ongoing decline in frost-driven processes highlights the vulnerability of mid-latitude mountain periglacial systems to climate warming and underscores the need for continued monitoring to better understand future landscape evolution in the Southern Carpathians. Full article

To unravel the link between agate geochemistry, host volcanic rocks, and ore-forming processes, this study integrated elemental correlation analysis, interaction interpretation, and interpretable machine learning (LightGBM-SHAP framework with SMOTE and 5-fold cross-validation) using 203 in-situ element datasets from 16 global deposits. The framework achieved 99.01% test accuracy and 97.4% independent prediction accuracy in discriminating host volcanic rock types. Key findings reveal divergence between statistical elemental correlations and geological interactions. Synergies reflect co-migration/co-precipitation, while antagonisms stem from source competition or precipitation inhibition, unraveling processes like stepwise crystallization. Rhyolite-hosted agates form via a “crust-derived magmatic hydrothermal fluid—medium-low salinity complexation—multi-stage precipitation” model, driven by high-silica fluids enriching Sb/Zn. Andesite-hosted agates follow a “contaminated fluid—hydrothermal alteration—precipitation window differentiation” model, controlled by crustal contamination. Basalt-hosted agates form through a “low-temperature hydrothermal fluid—basic alteration—progressive mineral decomposition” model, with meteoric water regulating Na-Zn relationships. Zn acts as a cross-lithology indicator, tracing crust-derived fluid processes in rhyolites, feldspar alteration intensity in andesites, and alteration timing in basalts. This work advances volcanic-agate genetic studies via “correlation—interaction—mineralization model” coupling, with future directions focusing on large-scale micro-area elemental analysis. Full article

The Directed Energy Deposition (DED) process has demonstrated high efficiency in manufacturing steel parts with complex geometries and superior capabilities. Understanding the complex interplays of alloy compositions, cooling rates, grain sizes, thermal histories, and mechanical properties remains a significant challenge during DED processing. Interpretable and data-driven modeling has proven effective in tackling this challenge, as machine learning (ML) algorithms continue to advance in capturing complex property structural relationships. However, accurately predicting the prime mechanical properties, including ultimate tensile strength (UTS), yield strength (YS), and hardness value (HV), remains a challenging task due to the complex and non-linear relationships among process parameters, material constituents, grain size, cooling rates, and thermal history. This study introduces an ML model capable of accurately predicting the UTS, YS, and HV of a material dataset comprising 4900 simulation analyses generated using the “JMatPro” software, with input parameters including material compositions, grain size, cooling rates, and temperature, all of which are relevant to DED-processed low-alloy steels. Subsequently, an ML model is developed using the generated dataset. The proposed framework incorporates a physics-based DED-specific feature that leverages “JMatPro” simulations to extract key input parameters such as material composition, grain size, cooling rate, and thermal properties relevant to mechanical behavior. This approach integrates a suite of flexible ML algorithms along with customized evaluation metrics to form a robust foundation to predict mechanical properties. In parallel, explicit data-driven models are constructed using Multivariable Linear Regression (MVLR), Polynomial Regression (PR), Multi-Layer Perceptron Regressor (MLPR), XGBoost, and classification models to provide transparent and analytical insight into the mechanical property predictions of DED-processed low-alloy steels. Full article

This study characterized the physicochemical properties of cocoa butter (CB) extracted from cocoa beans of the Criollo Nativo (Peru), Criollo (Mexico), and Forastero (Brazil) varieties subjected to spontaneous fermentation under traditional local conditions in each country. Cocoa samples were collected at 24-h intervals, and CB was extracted to evaluate its lipid composition through fatty acid profiling and spectroscopic techniques (FT-IR and NMR). Also, the thermal and structural properties via differential scanning calorimetry (DSC), including melting and crystallization profiles, crystallization kinetics, and polymorphism, were determined. The results revealed that stearic, oleic, and palmitic acids were predominant in all varieties, while trace levels of myristic and pentadecanoic acids contributed to molecular packing. FT-IR identified bands associated with glycerol chain formation in TAGs, which were confirmed by NMR through chemical shifts linked to the distribution of POS, SOS, and POP species. CB exhibited melting temperatures between 19.6 and 20.5 °C, favoring polymorphic transitions toward more stable forms. Form I (γ) predominated during early fermentation, while Forms II (α) and III (β′₂) were subsequently identified, particularly in Criollo varieties. These findings demonstrate that fermentation time significantly influences the chemical composition, oxidative stability, and crystalline structure of CB, providing valuable insights for optimizing cocoa processing and the development of high-quality chocolate products. Full article

Electromagnetic induction (EMI) devices have become increasingly popular for their soil bulk properties, soil nutrient status, and use in taking non-invasive soil salinity measurements. However, the high cost of data acquisition (DAQ) systems has been a significant barrier to the widespread adoption of these devices. In this study, we addressed this challenge by developing a cost-effective, easy-to-use, open-source DAQ system, transferable to the end user. This system employs a Raspberry Pi 4 model, paired with various components, to monitor the speed and position of the EM38 (Geonics Ltd, Mississauga, ON, Canada) and compare these with a proprietary CR1000 system. Through our results, we demonstrate that the low-cost DAQ system can successfully extract the analogical signal from the device, which is strongly responsive to the variation in the soil’s physical properties. This cost-effective system is characterized by increased flexibility in software processes and provides performance comparable to the proprietary system in terms of its geospatial data and ECb measurements. This was validated by the strong correlation (R² = 0.98) observed between the data collected from both systems. With our zoning analysis, performed using the Kriging technique, we revealed not only similar patterns in the ECb data but also similar patterns to the Normalized Difference Vegetation Index (NDVI) map, suggesting that soil physical characteristics contribute to variability in crop vigor. Furthermore, the developed web application enabled real-time data monitoring and visualization. These findings highlight that the open-source DAQ system is a viable, cost-effective alternative for soil property monitoring in precision farming. Future enhancements will focus on integrating additional sensors for plant vigor and soil temperature, as well as refining the web application, supporting zone classification based on the use of multiple parameters. Full article

Numerical simulation of metal forming processes is finding increasingly wide applications in advanced industry for the optimization of material processing conditions and prediction of process parameters, finally delivering a reduction of production costs. This work presents a comparison between simulation results of radial shear rolling (RSR) of VT3-1 titanium alloy (Ti-Al-Mo-Cr-Fe-Si) and results of experimental RSR at 1060 °C, 980 °C, and 900 °C in one, three, and five passes, respectively. The digital model (DM) demonstrates a high convergence of the calculation results (calculation error of less than 5%) with the actual geometric parameters of the experimental bars, their surface temperature, and rolling time during the experiment, which indicates a good potential for its application in the selection of deformation modes. Based on the simulation and experimental data, the conditions providing for the formation of differently sized grains in the bar cross-section have been identified. All of the as-rolled bars exhibit a gradient distribution of macrostructure grain size number (GSN), from the smallest one at the bar surface (2–4) to the greatest one in the center (4–6). The macrostructure GSN correlates with the workpiece temperature, which is the highest in the axial zone of the bars, and with the experimentally observed high plastic strain figures in the surface layers. It was found that, depending on the temperature conditions and reduction ratio per pass, any minor change in the values of process parameters can lead to the formation of macrostructures with different grain size numbers. Full article

Aluminum nitride (AlN) is a material of wide interest in the optoelectronics and high-power electronics industry. The deposition of AlN thin films at elevated temperatures is a well-established process, but its implementation on flexible substrates with conductive oxides, such as ITO-glass or ITO-PET, poses challenges due to the thermal degradation of these materials. In this work, the deposition and characterization of AlN thin films by reactive sputtering at a low temperature (RT and 100 °C) on ITO-glass and ITO-PET substrates are presented. The structural, optical, and electrical properties of the samples have been analysed as a function of the sputtering power and the deposition temperature. XRD analysis revealed the absence of peaks of crystalline AlN, indicative of the formation of an amorphous phase. EDX measurements performed on the ITO-glass substrate with a radiofrequency power applied to the Al target of 175 W confirmed the presence of Al and N, corroborating the deposition of AlN. SEM analyses showed the formation of homogeneous and compact layers, and transmission optical measurements revealed a bandgap of around 5.82 eV, depending on the deposition conditions. Electrical resistivity measurements indicated an insulating character. Overall, these findings confirm the potential of amorphous AlN for applications in flexible optoelectronic devices. Full article

Microwave-assisted rock fragmentation has been considered as one of the most promising technologies in rock excavation, but due to the fact that excavation is usually carried out in water-rich environments, understanding the dynamic fracture properties of rocks with different water contents after microwave irradiation is thus desirable. This study employed an enhanced split Hopkinson pressure bar (SHPB) system to perform dynamic fracture tests on pre-cracked semi-circular bending (SCB) specimens. It systematically explores the changes in the mechanical properties of sandstone under both dry and saturated conditions after exposure to 700 W of microwave radiation for 10 min. Infrared thermal imaging was utilized to capture the temperature distribution across the specimens, while digital image correlation (DIC) and high-speed photography were used to simultaneously record the crack propagation process. Based on the principle of energy conservation, the analysis of energy dissipation during fracture was performed, and the micro-damage evolution mechanism of the material was revealed through scanning electron microscopy (SEM). The results demonstrated that saturated sandstone exhibited a more rapid heating response and significantly lower dynamic fracture toughness and fracture energy compared to dry samples after microwave irradiation. These findings indicate that water saturation amplifies the weakening effect induced by microwaves, making the rock more susceptible to low-stress fractures. The underlying damage mechanisms of microwave radiation on water-bearing sandstone were interpreted with the theory of pore water pressure and structural thermal stresses. Full article

A soybean meal is a key protein source in human foods and animal feed, yet its digestibility is constrained by endogenous trypsin inhibitors (TIs). Thermal processing is the mainstream tool for TI inactivation, but high-intensity heat treatments increase energy consumption and can potentially denature proteins, diminishing nutritional quality. Reducing the thermal input while maintaining nutritional quality is, therefore, a critical challenge. One promising strategy is the use of soybean cultivars bred for low-TI expression, which may allow for milder processing. However, the performance of these low-TI cultivars under reduced heat conditions remains unstudied. This study treated soybean samples under four different temperatures (60, 80, 100, and 121 °C) for 10 min and investigated the impact of heat treatment on TI concentration, in vitro protein digestibility, and nutritional properties of meals from a conventional high-TI variety (Glenn) and a novel low-TI variety (VT Barrack). Results showed that heat treatment at 100 °C significantly improved protein digestibility and lower TI concentrations in both varieties. A negative correlation was observed between protein digestibility and TI concentration in both soybean varieties. At 100 °C, the low-TI variety achieved 81.4% protein digestibility with only 0.6 mg/g TIs, whereas the high-TI variety required 121 °C to achieve comparable protein digestibility and a TI reduction. These findings highlight that low-TI soybeans can lower the necessary thermal treatment to 100 °C to minimize TIs while simultaneously preserving protein quality and cutting energy demand, offering a practical, cost-effective approach to producing higher-quality soybean meals. Full article