Search Results (603)

To address the challenges of complex composition, high chemical oxygen demand (COD) content, and the difficulty of treating organic wastewater from brewery wastewater, as well as the limitations of traditional Fenton technology, including low catalytic activity and high material costs, this study proposes the use of biochemical sludge as a raw material. Coupled with iron salt activation and mechanical ball milling technology, a low-cost, high-performance iron-doped mesoporous nano-sludge biochar material is prepared. This material was employed as a particle electrode to construct a three-dimensional electro-Fenton system for the degradation of organic wastewater from sauce-flavor liquor brewing. The results demonstrate that the sludge-based biochar produced through this approach possesses a mesoporous structure, with an average particle size of 187 nm, a specific surface area of 386.28 m²/g, and an average pore size of 4.635 nm. Iron is present in the material as multivalent iron ions, which provide more electrochemical reaction sites. Utilizing response surface methodology, the optimized treatment process achieves a maximum COD degradation rate of 71.12%. Compared to the control sample, the average particle size decreases from 287 μm to 187 nm, the specific surface area increases from 44.89 m²/g to 386.28 m²/g, and the COD degradation rate improves by 61.1%. Preliminary investigations suggest that the iron valence cycle (Fe²⁺/Fe³⁺) and the mass transfer enhancement effect of the mesoporous nano-structure are keys to efficient degradation. The Fe-O-Si structure enhances material stability, with a degradation capacity retention rate of 88.74% after 30 cycles of use. When used as a particle electrode to construct a three-dimensional electro-Fenton system, this material demonstrates highly efficiency in organic matter degradation and shows promising potential for application in the treatment of organic wastewater from sauce-flavor liquor brewing. Full article

This study presents a sustainable upcycling strategy to convert “Pit,” a carbon-rich coke oven by-product from steel manufacturing, into high-purity graphite for use as an anode material in lithium-ion batteries. Despite its high carbon content, raw Pit contains significant impurities and has irregular particle morphology, which limits its direct application in batteries. We employed a multi-step, additive-free refinement process—including jet milling, spheroidization, and high-temperature graphitization—to enhance carbon purity and structural properties. The processed Pit-derived graphite showed a much-improved particle size distribution (D50 reduced from 25.3 μm to 14.8 μm & Span reduced from 1.72 to 1.23), increased tap density (from 0.54 to 0.80 g/cm³), and reduced BET surface area, making it suitable for high-performance lithium-ion batteries anodes. Structural characterization by XRD and TEM confirmed dramatically enhanced crystallinity after graphitization (graphitization degree increasing from ~13 for raw Pit to 95.7% for graphitized Pit at 3000 °C). The fully processed graphite (denoted S_Pit3000) delivered a reversible discharge capacity of 346.7 mAh/g with an initial Coulombic efficiency of 93.5% in half-cell tests—comparable to commercial artificial graphite. Furthermore, when composited with silicon oxide to form a hybrid anode, the material achieved an even higher capacity of 418.0 mAh/g under high mass loading conditions. These results highlight the feasibility of transforming industrial coke waste into value-added electrode materials through environmentally friendly physical processes. The upcycled graphite anode meets industrial performance standards, demonstrating a promising route toward circular economy solutions in both the steel and battery industries. Full article

This study presents the preparation of iron and nitrogen co-doped sludge-based biochar (FeCN-MSBC) and iron oxide-doped biochar (FeO-MSBC) by ball milling municipal sludge with different iron precursors (K₃Fe(CN)₆ and Fe₂O₃), followed by pyrolysis. These biochars were utilized to activate persulfate (PMS) for the degradation of phenolic pollutants. The results demonstrate that FeCN-MSBC, formed by the introduction of K₃Fe(CN)₆, contains Fe/N phases, with surface Fe sites exhibiting a lower oxidation state, which significantly enhances PMS activation efficiency. In contrast, FeO-MSBC, due to the aggregation of Fe₂O₃/Fe₃O₄, shows relatively lower catalytic activity. The FeCN-MSBC/PMS system degrades pollutants via a synergistic mechanism involving non-radical pathways mediated by ¹O₂ and electron transfer processes (ETP) catalyzed by surface Fe. Electrochemical oxidation and quenching experiments confirm that ETP is the dominant pathway. FeCN-MSBC, prepared at a pyrolysis temperature of 600 °C and an Fe loading of 3 mmol/g TSS, exhibited the best performance, achieving a phenol degradation rate constant (k_obs) of 0.127 min⁻¹, 4.5 times higher than that of undoped biochar (MSBC). FeCN-MSBC/PMS maintained high efficiency across a wide pH range and in complex water matrices, exhibiting excellent stability over multiple cycles, demonstrating strong potential for practical applications. This study provides an effective strategy for simultaneous Fe and N doping in sludge-derived biochar and offers mechanistic insights into Fe/N synergistic activation of PMS for practical water treatment. Full article

This study investigates the valorization of refuse-derived fuel (RDF), waste glass, and mill scale for sustainable ferrosilicon alloy production, contributing to zero-waste practices. RDF was blended with anthracite at ratios of 100, 90, 80, 70, 60 and 50 wt% (designated R1–R6) and applied as a reducing agent in the carbothermic reduction of SiO₂ and Fe₂O₃, thereby decreasing reliance on conventional fossil-based reductants. Ferrosilicon synthesis was conducted at 1550 °C using glass–mill scale blends with reducing agents R1–R6, producing samples named blends A–F. XRD analysis confirmed that the metallic products consisted predominantly of the FeSi intermetallic phase, with characteristic (110) and (310) peaks at 2θ ≈ 45.02° and 78°. The metallic products appeared as numerous small, shiny droplets, with yields ranging from 14.85 to 19.47 wt%; blends D–F exhibited the highest yields. In contrast, blends A–C produced metals with higher Si contents (23.34–27.11 wt%) due to enhanced SiO₂ reduction and efficient Si incorporation into the Fe matrix. Gas analysis and oxygen removal showed that blend B achieved the highest CO generation and reduction extent. Cl removal during RDF heat treatment indicated minimal potential for dioxin and furan formation. Overall, blends A and C were identified as optimal, providing high Si content, satisfactory metallic yield, and reduced CO/CO₂ emissions, demonstrating the effectiveness of RDF-based carbons for environmentally friendly ferrosilicon production. Full article

In conventional lithium-ion batteries (LIBs), active lithium loss during solid electrolyte interphase (SEI) formation reduces coulombic efficiency and energy density. Cathode pre-lithiation can effectively compensate for this irreversible lithium consumption. To address limitations of conventional pre-lithiation agents—such as complex synthesis and air instability—a Ketjen black-coated lithium oxalate nanocomposite (Li₂C₂O₄@KB) using high-energy ball milling and spray drying was developed. This composite leverages the advantages of Li₂C₂O₄, including a mild decomposition potential (4.26 V vs. Li⁺/Li), high theoretical lithium compensation capacity (525 mAh·g⁻¹), and environmentally benign decomposition products, and significantly improves electronic conductivity and reduces particle size. When incorporated in NCM622 full cells, the initial capacity is increased by 18.21 mAh·g⁻¹ at 0.3 C, with a 29.22% enhancement in capacity retention after 50 cycles at 0.3 C. At 1 C, the initial capacity is higher by 15.79 mAh·g⁻¹, accompanied with a 7.72% improvement in retention after 100 cycles. The Li₂C₂O₄@KB composite exhibits great promise as a practical and efficient cathode pre-lithiation additive for next-generation high-energy-density LIBs. Full article

Accurately identifying the load status of a ball mill is critical for optimizing grinding efficiency and ensuring operational stability. However, the one-dimensional vibration signals collected from ball mills exhibit strong non-stationarity and a high degree of entanglement between multi-scale local transient features and long-range temporal evolution patterns. To address this, rather than relying on a purely black-box approach, this paper introduces a novel Deep Multi-scale Spatial–Temporal Feature Decoupling Network (DMSTFD-Net) guided by a clear feature decoupling philosophy to enhance model interpretability. The core of DMSTFD-Net lies in its hierarchical collaborative feature refinement mechanism. It first utilizes a one-dimensional residual network (ResNet) to adaptively capture and preliminarily decouple multi-scale spatial characteristics from the raw signal. Subsequently, the extracted high-level feature sequences are fed into a bidirectional gated recurrent unit (Bi-GRU) to decouple high-order temporal dynamic patterns. Experiments on a multi-condition dataset demonstrate that the proposed network achieves a state-of-the-art accuracy of 97.65%. Furthermore, dedicated cross-condition experiments and t-SNE visualizations validate the framework’s effectiveness. The results confirm that DMSTFD-Net provides a powerful, robust, and more interpretable solution for ball mill load identification. Full article

Reliable tools for cultivar discrimination and ripening stage evaluation are critical to optimize harvest timing and support milling process focused on olive oil quality. This research examines the spectral properties of olive drupes throughout different maturation stages, ranging from green to full purple-black pigmentation, across 29 distinct cultivars. High-resolution spectrometric analysis was conducted within the 380–1080 nm wavelength range. Multiple analytical approaches were employed to optimize wavelength selection from hyperspectral reflectance data to obtain discriminating tools for olive classification. A Biologically Informed Wavelength Extraction method (BIWE) was developed, focusing on cultivar and ripening stages identification, and pivoted on biologically informed single wavelengths and Vegetation Indices (VIs) selection. The methodology integrated multi-scale spectral analysis with biochemically weighted scoring and a multi-criteria evaluation framework, employing a two-iteration refinement process to identify optimal spectral features with high discriminatory power and biological relevance. Analysis revealed spectral variations associated with maturation. A characteristic reflectance peak at approximately 550 nm observed during early ripening stages underwent a notable shift, developing into distinct spectral behavior within the 700–780 nm range in intermediate and advanced ripening stages and reaching a plateau for all the samples between 800 and 950 nm. The BIWE method achieved exceptional efficiency in olive classification, utilizing only 25 single wavelengths compared to 114 required by Principal Component Analysis (PCA) and 131 by Recursive Feature Elimination (RFE), representing 4.6-fold and 5.2-fold reductions, respectively. Despite this reduction, BIWE’s overall accuracy (0.5634) remained competitive compared to RFE (−10%) and PCA (−8%) alternative approaches requiring larger wavelengths dataset acquisition. The integration of biochemically relevant VIs enhanced accuracy across all methodologies, with BIWE demonstrating notable improvement (+19.2%). BIWE demonstrated effective olive identification capacity with a reduction in required wavelengths and VIs dataset, affecting the technological needs (spectrometer offset and real-time classification applications) for a tool oriented to olive cultivars and ripening stage discrimination. Full article

This study tackles the challenge of achieving high-precision robotic machining of elastic materials, where elastic recovery and overcutting often impair accuracy. To address this, a novel milling strategy, RAPSO, is introduced by combining an adaptive particle swarm optimization (APSO) algorithm with a reinforcement learning (RL)-based compensation mechanism. The method builds a material-specific milling model through residual error characterization, incorporates a dynamic inertia weight adjustment strategy into APSO for optimized toolpath generation, and integrates a Proximal Policy Optimization (PPO)-based RL module to refine trajectories iteratively. Experiments show that RAPSO reduces residual material by $33.51\%$ compared with standard PSO and APSO methods, while offering faster convergence and greater stability. The proposed framework provides a practical solution for precision machining of elastic materials, offering improved accuracy, reduced post-processing requirements, and higher efficiency, while also contributing to the theoretical modeling of elastic recovery and advanced toolpath planning. Full article

Marine mammals serve as indicator species for environmental and human health. However, they are increasingly exposed to pressure from human activities and climate change. The white-beaked dolphin (Lagenorhynchus albirostris) (WBD) is among the species negatively affected by these conditions. To support conservation and management efforts, a deeper understanding of their behavior and movement patterns is essential. One approach is drone-based monitoring combined with artificial intelligence (AI), allowing efficient data collection and large-scale analysis. This study aims to: (1) investigate the use of drone imagery and AI to monitor and analyze marine mammal behavior, and (2) test the application of machine learning (ML) to identify behavioral patterns. Data were collected in Skjálfandi Bay, Iceland, between 2021 and 2023. Three behavioral types were identified: Traveling, Milling, and Respiration. The AI_RGB model showed high performance on Traveling behavior (precision 92.3%, recall 96.9%), while the AI_gray model achieved higher precision (97.3%) but much lower recall (9.5%). The model struggled to classify Respiration accurately (recall 1%, F1-score 2%). A key challenge was misidentification of WBDs due to visual overlap with birds, waves, and reflections, resulting in high false positive rates. Multimodal AI systems may help reduce such errors in future research. Full article

The growing demand for lithium-ion batteries (LIBs) has led to an urgent need for sustainable recycling strategies for spent cathode materials. In this study, a mechanochemical approach was developed for the recovery of lithium and cobalt from end-of-life LiCoO₂ cathodes using high-energy ball milling. For the first time, aluminum and carbon were employed as internal reducing agents, facilitating the in situ decomposition of LiCoO₂ into CoO, Li₂O, and metallic Co. X-ray diffraction analysis confirmed significant structural disorder, phase transitions, and the formation of CoO, AlCo, and spinel-like CoAl₂O₄. The Taguchi method was applied to optimize milling parameters, identifying 800 rpm, 60 min, and a ball-to-powder ratio of 50:1 as the most effective conditions for structural activation. Subsequent ammonia leaching under fixed conditions (3.0 M NH₃·H₂O, 1.0 M (NH₄)₂CO₃, 60 °C, 25 mL/g, 6 h) demonstrated high recovery efficiencies: up to 94.6% for lithium and 83.7% for cobalt in the best-performing samples. These results highlight the synergistic benefits of mechanical activation and reductant-assisted phase engineering for enhancing metal recovery. The proposed method offers a simple, scalable, and eco-friendly route for the hydrometallurgical recycling of LIB cathodes without requiring extensive chemical pretreatment. Full article

Selecting appropriate milling equipment is an important means to reduce carbon emissions and improve the efficiency of part-machining processes, as the process of machining the same part on different milling machines varies greatly. Traditional milling machine selection approaches only involve a static analysis of their advantages and disadvantages without considering the dynamic changes in the production process, making them difficult to adapt to the requirements of the new era. To solve this problem, we establish a milling machine selection model based on the new quality productivity (NQP) concept; propose a calculation method considering carbon emissions, efficiency, and quality (expressed as surface roughness) in the production process; and quantitatively analyze the process objectives of different milling machines according to the changes in the machining process. The spindle speed, feed rate, cutting width, and cutting depth are taken as the optimization variables, and the cutting parameters are optimized using the egret swarm algorithm (ESA) to obtain the Pareto frontier solutions providing low-carbon and high efficiency process parameters. The method was verified through a plane milling example. After ESA optimization, the processing time was increased by 5.6%, the surface roughness accuracy was improved by 12.9%, and the carbon emissions were reduced by 13.1%, demonstrating the effectiveness of the proposed method. Full article

Understanding the regulatory mechanisms of water–nitrogen coupling effects on soil–plant–microbe systems in arid regions is crucial for sustainable agricultural development. This study systematically investigated the interactive effects of field capacity (75% vs. 45%) and nitrogen application rates (100 vs. 300 kg ha⁻¹) combined with different enhanced-efficiency nitrogen fertilizers (EENFs) on rhizosphere soil fertility and microbial community structure of Jujube (Ziziphus jujuba Mill.) seedlings through a two-year pot experiment. Two-year-old jujube seedlings were employed with five treatments: NS (urea), NM (urease inhibitor), XH (nitrification inhibitor), W (microbial fertilizer), and CK (control), to analyze soil physicochemical properties and microbial community responses. Soil available N accumulated under high-N/adequate moisture but declined under drought. NM curbed NH₃ volatilization by 32.38–43.22%, while XH increased NH₄⁺-N by 35.76%. Drought raised microbial α-diversity (bacteria + 33.88–37.5%, fungi + 43.62–68.75%). NM demonstrated optimal performance in ammonia volatilization (32.38–43.22% reduction), while XH showed notable efficacy in ammonium-N regulation (35.76% enhancement). Microbial α-diversity exhibited enhanced responses under drought stress, with bacterial and fungal community improvements reaching 33.88–37.5% and 43.62–68.75%. Redundancy analysis showed environmental factors explained more community variance under water stress (bacteria: 79.19→88.76%; fungi: 64.64→92.52%). These findings provide theoretical support for jujube cultivation in arid zones, demonstrate the potential of targeted EENFs, and offer new insights for precision water–fertilizer and microbial management. Full article

Ultrasonic-assisted machining (UAM) has emerged as a transformative technology for increasing material removal efficiency, improving surface quality and extending tool life in precision manufacturing. This review specifically focuses on the application of it to titanium aluminide (TiAl) alloys. These alloys are widely used in aerospace and automotive sectors due to their low density, high strength and poor machinability. This review covers various aspects of UAM, including ultrasonic vibration-assisted turning (UVAT), milling (UVAM) and grinding (UVAG), with emphasis on their influence on the machinability, tool wear behavior and surface integrity. It also highlights the limitations of single-energy field UAM, such as inconsistent energy transmission and tool fatigue, leading to the increasing demand for multi-field techniques. Therefore, the advanced machining strategies, i.e., ultrasonic plasma oxidation-assisted grinding (UPOAG), protective coating-assisted cutting, and dual-field ultrasonic integration (e.g., ultrasonic-magnetic or ultrasonic-laser machining), were discussed in terms of their potential to further improve TiAl alloys processing. In addition, the importance of predictive force models in optimizing UAM processes was also highlighted, emphasizing the role of analytical and AI-driven simulations for better process control. Overall, this review underscores the ongoing evolution of UAM as a cornerstone of high-efficiency and precision manufacturing, while providing a comprehensive outlook on its current applications and future potential in machining TiAl alloys. Full article

Rich in cellulose, the agro-industrial residue of “Cocos nucifera L.” stands out due to its high global production. In view of this, this research into the development of cellulose nanofibrils from green coconut fiber residue evaluated the fiber produced from an alkaline pre-treatment associated with a grinding process using a colloidal mill, which produced pure and renewable cellulose with characteristics similar to those of commercial celluloses. FTIR and XRD spectroscopy analyses showed that the methodologies established for coconut fiber are efficient in removing amorphous groups. The XRD corroborated the spectrogram and revealed a peak at 2θ = 22°, corresponding to the crystalline region of cellulose I. Both analyses were preceded by thermal analysis showing a reduction in lignin and an increase in the cellulose fraction. The AFM and SEM morphological micrographic analyses confirm the efficiency of the mechanochemical treatment in producing nanometric fibers, which, when submitted to rheology analyses, presented the desired gel profile. Full article

During machining, most of the mechanical energy is converted into heat. A substantial part of this heat is transferred to the cutting tool, causing a rapid rise in tool temperature. Excessive thermal loads accelerate tool wear and lead to displacement of the tool center point, reducing machining accuracy and workpiece quality. This challenge is particularly pronounced when machining titanium alloys. Due to their low thermal conductivity, titanium alloys impose significantly higher thermal loads on the cutting tool compared to conventional carbon steels, making the process more difficult. To reduce temperatures in the cutting zone, cutting fluids are widely employed in titanium machining. They have been shown to significantly extend tool life. Cutting fluids are broadly categorized into cutting oils and water-based cutting fluids. Owing to their distinct thermophysical properties, these fluids exhibit notably different cooling and lubrication performance. However, current research lacks comprehensive cross-comparative studies of different cutting fluid types, which hinders the selection of optimal cutting fluids for process optimization. This study examines the influence of three cutting fluids—emulsion, cutting oil, and synthetic oil-free fluid—on tool wear, temperature, surface quality, and energy consumption during flood-cooled end milling of Ti-6Al-4V. A novel experimental setup incorporating embedded thermocouples enabled real-time temperature measurement near the cutting edge. Tool wear, torque, and surface roughness were recorded over defined feed lengths. Among the tested fluids, emulsion achieved the best balance of cooling and lubrication, resulting in the longest tool life with a feed travel path of 12.21 m. This corresponds to an increase of approximately 200% compared to cutting oil and oil-free fluid. Cutting oil offered superior lubrication but limited cooling capacity, resulting in localized thermal damage and edge chipping. Water-based cutting fluids reduced tool temperatures by over 300 °C compared to dry cutting but, in some cases, increased notch wear due to higher mechanical stress at the entry point. Power consumption analysis revealed that the cutting fluid supply system accounted for 60–70% of total energy use, particularly with high-viscosity fluids like cutting oil. Complementary thermal and CFD simulations were used to quantify heat partitioning and convective cooling efficiency. The results showed that water-based fluids achieved heat transfer coefficients up to 175 kW/m²·K, more than ten times higher than those of cutting oil. These findings emphasize the importance of selecting suitable cutting fluids and optimizing their supply to enhance tool performance and energy efficiency in Ti-6Al-4V machining. Full article