Search Results (297)

To address the core industrial bottlenecks of conventional batch tannic acid-based germanium precipitation processes—high reagent consumption, long reaction cycles of several hours, severe impurity co-precipitation as well as the common mismatch between single-channel microreactor throughput and industrial production demands. This work combines numerical simulation with experimental validation to investigate microscale two-phase flow regulation, high-throughput microreactor optimization, and tannic acid precipitation intensification. Two-dimensional two-phase flow models are established for straight and zigzag microchannels, with the level set method applied to track interfacial evolution. The regulatory effects of inlet velocity and channel geometry on flow patterns, droplet behavior and mixing performance are clarified. Zigzag channels induce chaotic convection via periodic corners, achieving an order-of-magnitude improvement in mixing efficiency at low Reynolds numbers (Re < 400), which lays a fundamental basis for reaction intensification. Taking zigzag channels as core units, a bidirectional symmetric superposition scale-up strategy is proposed to break the throughput limitation of single-channel systems, and a 3D-printed high-throughput microreactor integrating 78 parallel zigzag channels is designed. 3D simulations reveal a three-stage mixing mechanism and uniform flow distribution among parallel channels, with total throughput two orders of magnitude higher than a single channel. Single-channel experiments with industrial germanium-bearing raffinate yield 91.81% precipitation efficiency under optimal conditions, reducing the reaction residence time from hours in conventional batch processes to the second scale. Staged reagent addition and two-stage serial configuration further raise the efficiency to ~98%, realizing deep germanium recovery with significantly improved reagent utilization and reduced impurity co-precipitation. This process achieves efficient intensification of the chelation precipitation process while balancing throughput and mixing performance, providing a novel and technically feasible approach for efficient low-consumption germanium recovery, and offering solid technical support for the industrial application of microreactors in the hydrometallurgy field. Full article

Germanium is a critical rare-dispersed metal with irreplaceable applications in infrared optics, fiber-optic communications, and semiconductor industries, making its efficient recovery from secondary resources of great strategic importance. This study investigates the selective recovery of germanium from complex zinc smelting leachates using a novel hydroxamic acid extractant, BGYW, in synergistic combination with P204. The feed solution contained approximately 360 mg/L Ge, 10,790 mg/L Fe²⁺, and 98,530 mg/L Zn, representing a highly complex matrix. Continuous counter-current extraction was performed in a 30-stage miniature mixer-settler. Under optimized conditions of 10% BGYW + 5% P204 in white oil, an O/A ratio of 1:1, and 8 mol/L NH₄F as strippant, the single-stage germanium extraction efficiency reached 99.4%. Over 16 consecutive cycles, the extraction system maintained stable performance with average germanium extraction above 99%. A 3-stage scrubbing section using 50 g/L H₂SO₄ effectively removed co-extracted Zn, Cu, and Al impurities. Iron co-extraction, a major challenge, was successfully mitigated through a 2–3 stage iron scrubbing step using a chloride-containing scrubbing solution, which reduced the iron concentration in the strip liquor from approximately 600 mg/L to below 4 mg/L, and decreased the Fe/Ge mass ratio from 0.197 to below 0.01. The overall germanium recovery across the entire 30-stage continuous process reached 98.82%, and the dissolution loss of BGYW in the aqueous phase was reduced by over 85% compared to the conventional YW100 extractant. Third-phase formation caused by residual organic flocculants from the leaching step was eliminated through enhanced pre-treatment, while ferric fluoride precipitation in the stripping section was resolved by incorporating the iron scrubbing stage. This study demonstrates that the BGYW-P204 extraction system with the integrated iron scrubbing step offers an efficient, stable, and industrially viable approach for germanium recovery from zinc smelting leachates, providing a practical solution to the long-standing challenge of germanium–iron separation and contributing to the sustainable supply of this critical metal. Full article

Objectives: Sleep staging from polysomnographic (PSG) recordings is clinically critical for diagnosing sleep-related disorders, yet manual scoring by certified technologists remains time-consuming, costly, and subject to inter-rater variability. Methods: This study presents an automated, explainable, and multimodal framework for five-class sleep stage classification using simultaneously acquired electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG) signals. A total of 1946 annotated 30 s epochs from 30 healthy adult recording sessions (Sleep-EDF Expanded and Sleep Cassette subset) were processed through a 37-dimensional multimodal feature extraction pipeline encompassing temporal amplitude statistics, frequency-domain spectral band powers, nonlinear entropy and complexity measures, and Daubechies-4 discrete wavelet transform (DWT) energy coefficients. Four classical machine learning classifiers -Random Forest (RF), Support Vector Machine with radial basis function kernel (SVM-RBF), Gradient Boosting (GB), and K-Nearest Neighbours (KNN, k = 7) were benchmarked under stratified five-fold cross-validation. Results: SVM-RBF achieved the highest macro-averaged F1-score of 0.7322 (Cohen’s kappa 0.6784, overall accuracy 75.18%). N3 deep slow-wave sleep achieved the highest per-class F1 of 0.879, while N1 light sleep was the most challenging (F1 = 0.668). SHapley Additive exPlanations (SHAP) and RF mean decrease in Gini impurity (MDGI) analysis jointly identified EMG root mean square amplitude (MDGI = 0.0805), gamma band power (0.0784), and permutation entropy (0.0434) as the three most discriminative features. As a novel methodological contribution, sixteen categories of signal sculpting visualisations were developed, translating abstract multivariate features into clinically interpretable graphical representations. Conclusions: The proposed framework achieves substantial kappa agreement approaching the lower bound of expert inter-rater reliability (0.76–0.82) while providing full model transparency, with direct implications for wearable sleep monitoring device design. Full article

Commercial aluminum (Al) metals contain unavoidable impurities, such as iron (Fe) and silicon (Si). Due to its low solubility and high chemical affinity to Al, Fe exists in the form of Fe-containing intermetallic compounds (Fe-IMCs), which are crucial in solidification processes, determining the micro-structure and consequently the mechanical performance of the cast parts. Meanwhile, Si, as an impurity or addition, may join the binary Fe-IMCs. Here, we investigate the Si stabilization effects on the frequently observed Al-rich Fe-IMCs in a comprehensive and systematic way using a first-principles density-functional theory (DFT) approach. The study reveals different Si stabilization effects on the cubic α- and hexagonal α’-phase, as well as other binaries: Al₁₂Fe, η-Al₆Fe, τ₄-, β-, and θ-phases. The enhancement of stability for the α-phase is moderate, while it is strong for the α’-phase. For the stability series (from higher to lower) is θ-Al₁₃Fe₄ > η-Al₆Fe > α-Al₄.₇₅Fe in the binary system, while it becomes τ₄-(Al,Si)₅Fe > β-Al₄.₅SiFe > α’-(Al,Si)₄.₁₇₄Fe for the ternary Fe-IMCs. The information obtained here helps understand the formation of Fe-IMCs particles during casting of Al-Si alloys, and the design of novel Al alloys of fine micro-structures and desired mechanical performances of the products from the primary Al and the scraps and wastes. Full article

China is rich in magnetite mineral resources, but they are generally characterized by low grade, fine dissemination size, and a high content of harmful impurities. Wet low-intensity magnetic separation (WLIMS) is an important method for processing fine-grained magnetite. However, during the separation process, fine magnetite particles are prone to magnetic agglomeration, which makes it difficult for conventional WLIMS separators to achieve high-selectivity separation. To address this issue, a novel WLIMS separator based on a rotating magnetic system was developed in this investigation, and its separation characteristics were systematically investigated through a combined approach comprising CFD–DEM–FEM multiphysics coupling simulations and experimental validation. Simulation results indicate that the rotating magnetic system significantly reduces the chain length and the structural stability of magnetic agglomerates just as magnetite particles enter the magnetic field region. Furthermore, under the rotating action of the magnetic system, the magnetic chains only enclose a portion of the intergrowth minerals, while gangue minerals remain unattached, which positively contributes to improved separation selectivity. Both laboratory-scale experimental results and industrial production data indicate that, compared to the conventional WLIMS separator, the rotating magnetic system WLIMS separator achieves significantly superior separation performance. For a magnetite ore with a grade of 57.68%, the rotating magnetic system WLIMS separator achieved an optimal concentrate grade of 65.43% (with a recovery of 94.78%), whereas the conventional WLIMS separator attained only 60.32% at a similar recovery rate. This investigation provides an important basis for the large-scale industrial application of rotating magnetic system WLIMS separators and the efficient development and utilization of fine-grained magnetite resources. Full article

Mechanized harvesting of subsurface crops has long been confronted with the critical engineering dilemmas of high damage rates and high impurity rates. Traditional taxonomic classification methods based on botanical families and genera fail to provide effective guidance for the engineering research and development of harvesting machinery. From an engineering perspective, this paper proposes a novel classification logic that categorizes subsurface crops into three major types based on their soil burial depth and physical distribution characteristics: shallow-soil clustered growth type (0–20 cm), mid-soil scattered growth type (20–40 cm), and deep-soil vertically rooted type (>40 cm). The harvesting bottlenecks of representative crops within these strata, including potato, onion, peanut, sweet potato, cassava, and yam, are systematically elucidated. Furthermore, this review provides an in-depth analysis of the current state of frontier core technologies, such as bionic drag reduction excavation, flexible multi-stage separation, microscopic discrete element method (DEM) simulation, kinematic optimization, and AI-based visual perception. This paper aims to reveal the common bottlenecks in subsurface crop harvesting and prospect future developmental trends centered on the deep integration of machinery and agronomy as well as intelligent perception and adaptation, thereby providing a solid theoretical foundation and engineering reference for the innovation of global agricultural machinery. Full article

Silicon carbide (SiC) is the leading wide-bandgap semiconductor material, providing mature doping and device fabrication. Additionally, SiC hosts a multitude of optically active point defects (color centers) and is relevant for many applications in quantum technologies. A crucial step towards harnessing the full potential of the SiC platform includes technologies to create color centers with defined localization and density, e.g., to facilitate their coupling to nano-photonic structures and to observe cooperative effects. Here, silicon vacancy centers and divacancies stand out, as no impurity atom is needed, and high-thermal budget annealing steps can be avoided. We characterize the effect of localized, femtosecond laser irradiation of SiC, investigating surface modifications and photoluminescence, including Raman spectroscopy and optical lifetime measurements. We employ commercial, high-purity, semi-insulating substrates and an industrial-grade laser system to explore broader applicability of the method. As a novel approach, we apply femtosecond laser irradiation to SiC substrates with an epitaxial graphene layer and find that the threshold for photoluminescence due to laser treatment is lowered. Full article

Floods are among the most destructive natural hazards, posing a significant risk to lives and infrastructure worldwide. Effective flood risk management demands precise, multidimensional approaches. In this direction, the current study aims to evaluate and compare two methods for flood-risk assessment: the scenario-based Ordered Weighted Averaging (OWA) and the data-driven Random Forest (RF) method. To this end, the Great Karun watershed in Iran was chosen due to its complex hydrological and climatic conditions. Hydro-climatic, hydrological, topographic, land-cover datasets, and actual flood observations were applied and analyzed based on fifteen influencing factors recommended by expert opinion. In the OWA approach, while factor weights were determined using the Best-Worst Method, flood-risk maps were produced based on five scenarios: very optimistic, optimistic, intermediate, pessimistic, and very pessimistic. In the RF approach, factor importance index was calculated via the mean decrease impurity algorithm, and the model was trained to generate flood-risk maps. Results showed that distance from rivers and slope were the most influential factors in OWA, while precipitation and flow accumulation dominated in RF. Prediction rate for OWA scenarios ranged from 1.4 to 3.5%, while RF achieved 16.0%, and the Area Under the Curve (AUC) was 0.984. Optimistic scenarios overestimated, and pessimistic scenarios underestimated risk, with the OWA intermediate scenario most closely matching RF results. RF demonstrated superior performance for flood-risk classification, highlighting its applicability for precise flood management. Overall, this study presents a novel comparative framework by integrating scenario-based OWA-BWM and Random Forest approaches to investigate the effects of decision-maker preferences and data-driven learning on flood susceptibility mapping. Full article

Direct cyanide leaching of high-carbon gold ores typically results in poor gold extraction due to the “preg-robbing” effect of carbonaceous matter. This study investigates a refractory high-carbon gold ore from Laos. Based on detailed process mineralogy that identified the occurrence state of gold and the distribution of harmful impurities, a combined process consisting of flotation pre-concentration, oxidative roasting, and a Mill–Leach Coordination (MCL) process was developed. The MCL process, which integrates simultaneous grinding and leaching, continuously renews the particle surface and eliminates product-layer diffusion resistance, thereby significantly enhancing gold dissolution efficiency. Kinetic studies demonstrate that the leaching reaction follows the Avrami model and is controlled by internal diffusion, with a derived semi-empirical kinetic equation. This research provides a novel and efficient technical route for treating high-carbon gold ores and offers theoretical guidance for industrial scale-up. Full article

To address prevalent industrial challenges, including the high cost of fabricating microstructures via photolithography and 3D printing, impurity residues easily generated by conventional physical/chemical pore-forming techniques, and the limited sensitivity of regular capacitive sensors, this paper innovatively proposes an integrated low-temperature in situ gas foaming strategy using ammonium bicarbonate for the fabrication of porous TPU-based ionic gels. Relying on the complete gaseous decomposition property of ammonium bicarbonate upon heating, a three-dimensionally interconnected continuous porous network is spontaneously constructed inside the polymer matrix. Thermoplastic polyurethane (TPU) is selected as the continuous polymer phase, and [EMIM][TFSI] imidazolium ionic liquid is blended as the ion source to synthesize composite ionic gel substrates. A PDMS composite slurry filled with graphene is employed to prepare flexible substrates, followed by low-temperature oxygen plasma surface modification to introduce polar functional groups such as hydroxyl and carboxyl onto electrode surfaces. A standard sandwich-structured ionic pressure sensor with the configuration of “top modified electrode—porous ionic gel dielectric layer—bottom modified electrode” is finally assembled. The porous framework and modified electrodes constitute a dual synergistic enhancement system: the porous structure markedly reduces the equivalent elastic modulus of the gel and improves its compressive deformation capacity; polar-modified electrodes optimize the interfacial compatibility between electrodes and gels, shorten ion migration paths and lower interfacial contact resistance. Systematic calibration of multiple batches of parallel samples reveals that the as-fabricated sensor achieves a high sensitivity of 25.3 kPa⁻¹ across the full measuring range from 0 to 1000 kPa with a linear fitting coefficient R² = 0.992. The loading response time and unloading recovery time of the device are 60 ms and 80 ms respectively, with a performance degradation of less than 3% after 1000 consecutive loading–unloading cycles, featuring low hysteresis error and excellent signal repeatability. Multi-scenario in vivo wearable tests on human subjects verify that the device can precisely capture subtle fluctuations of radial artery pulse and periodic laryngeal deformation during swallowing, distinguish characteristic waveform patterns of various English words according to differences in vocal cord vibration, and accurately detect bending motions when attached to finger joints. The entire fabrication process adopts common chemical raw materials and standard laboratory equipment without expensive micro-nano processing facilities, featuring convenient raw material procurement and high process fault tolerance, which enables large-area coating-based mass production. This work delivers a novel technical route for the low-cost large-scale production of high-performance ionic flexible sensors and bears significant industrialization reference value for applications in wearable medical monitoring, bionic robotic electronic skin, flexible human–machine interactive touch panels and other related fields. Full article

The lithium battery recycling industry is developing rapidly, and the rapid oxidation and degradation of dimethyl carbonate (DMC) in the wastewater generated by this industry is of crucial importance. In this study, Fe and Cu dopants were controlled and the C-SiO₂ framework with porous structures was constructed to synthesize FeCuC-SiO₂ and C-SiO₂ catalysts. The former could achieve 91.65% of DMC degradation within 60 min through peroxymonosulfate (PMS) activation, and the degradation rate was increased to 4.44 times compared to C-SiO₂ without Fe and Cu doping. And under optimized conditions, a DMC degradation rate of 90.57% can be achieved within 10 min by FeCuC-SiO₂. The catalyst has good stability and the catalytic activity can be maintained during reuse process for five times with over 70% of DMC degradation rate, 58.9% of mineralization rate, and a relatively low amount of metal leaching. Moreover, the degradation rate can still remain above 70% with the existence of impurity anions, demonstrating a strong salt resistance. Hydroxyl radicals (OH^•), sulfate radicals (SO₄^•−), and ¹O₂ were found to dominant the reaction in the FeCuC-SiO₂-PMS system, which were involved in both free radical and non-free radical pathways and led to excellent catalytic oxidation performance and environmental adaptability. In general, a novel design for a Fenton-like catalyst was presented, providing a theoretical basis for the improvement of oxidation efficiency and the regulation of reaction pathways in Fenton-like reactions. Full article

Severe foaming and a significant decrease in desulfurization performance were noted in a novel UDS solvent applied in a natural gas field in western Sichuan, China. The effects of hydrocarbon and ionic impurities on foaming behavior and the purification performance of candidate adsorbents were investigated. An extraction-gas chromatography method was established and validated for determining total hydrocarbons in amine solutions, enabling quantitative evaluation of hydrocarbon contamination. Controlled contamination experiments revealed that hydrocarbons had the strongest effect on foaming, while sulfate and chloride strongly promoted foam formation; organic acid anions showed only minor effects. Fixed-bed screening identified A-98FM anion-exchange resin as the most effective for anionic impurity removal and AC-02 activated carbon as the best candidate for hydrocarbon purification, with a cumulative adsorption capacity q_0–12 of 14.86 mg/g over 12 h. Pore-structure and thermal-release analyses suggested that conventional pore descriptors alone could not fully explain the dynamic purification performance, while hydrocarbon-related loadings in spent AC-02 occupied accessible pore space and contributed to performance decay. Treatment of a field-aged UDS lean solvent further showed that reductions in target impurities were accompanied by lower foam height and shorter defoaming time. This work provides experimental support for impurity monitoring, foaming-risk identification, and adsorptive purification of UDS desulfurization solvent under flowback-contamination conditions. Full article

Actual printing and dyeing wastewater (APDW), as one of the most difficult types of wastewater to treat, has become a significant environmental risk due to its toxicity and the challenges associated with its degradation. Microalgae-based treatment of APDW is a promising, eco-friendly, and cost-effective strategy. In this study, a cyanobacterium, Synechocystis aquatilis, was isolated from APDW. The strain demonstrated good environmental tolerance and the capacity to remove pollutants and valorize biomass simultaneously. Under optimized conditions, it removed COD (120.27 mg·L⁻¹·d⁻¹), NH₄-N (0.89 mg·L⁻¹·d⁻¹), and total phosphorus (9.52 mg·L⁻¹·d⁻¹), while achieving substantial decolorization. The strain concurrently accumulated lipids (373.08 mg/g), polysaccharides (167.85 mg/g), and proteins (72.05 mg/g). Mechanistic analyses revealed that S. aquatilis microalgae adsorb dyes and impurities via bioadsorption and then biodegrade dyes and nitrogen and phosphorus compounds via NADPH generation, glutamate and butyrate metabolism, and oxidoreductase activity. This study presents a promising application of S. aquatilis as a novel and environmentally friendly treatment method for APDW, enabling simultaneous wastewater treatment and resource recovery. Full article

The rapid expansion of crystalline silicon photovoltaic (PV) modules has increased the demand for sustainable and high-value recycling strategies for end-of-life (EOL) modules. A significant challenge is the removal of impurities such as carbon, oxygen, and non-metallic inclusions introduced into silicon solar cells during the dissociation of PV laminates. To address this, we propose a non-consumable electrode electroslag remelting (NCE-ESR) process to effectively eliminate inclusions. In this process, the reverse flow of alloy droplets and the extensive contact area are crucial during the reverse flow slag washing. Initially, we studied the occurrence characteristics of inclusions in silicon solar cells obtained after pyrolysis from enterprises. Pyrolysis facilitated the formation of inclusions like Si-O, C-O, Al-O, and Si-N, particularly in the fine size range below 5 μm. To enhance impurity removal, the recycled Si was alloyed with Cu, which increased the melt density and impurity activity. Based on optimized thermodynamics and physical properties, we designed a novel electroslag composition of 40%CaO-40%SiO₂-20%CaF₂ suitable for silicon alloy refining. Notably, during the reverse flow slag washing of the Cu-Si alloy, the maximum removal rate of inclusions reached 77.42%. The average diameter of inclusions was reduced to below 6 μm, and the removal rates of impurity elements such as Al, O, and C exceeded 98.09%, 94.86%, and 86.08%, respectively. Finally, we independently developed the NCE-ESR equipment and conducted a kilogram-scale amplification test. The results indicated that the impurity removal rates of Al and O exceeded 97%, and the final inclusion size was less than 10 μm. This study demonstrates a scalable and environmentally friendly approach for the high-value recycling of silicon resources from decommissioned PV modules. Full article

This work presents a novel one-step plasma–solution synthesis of Prussian Blue (PB) and copper hexacyanoferrate (Cu-PBA) nanoparticles via underwater pulsed DC discharge. For the first time, the direct plasma-assisted formation of these coordination polymers is reported. The obtained materials were examined by X-ray diffraction, Fourier-transform infrared spectroscopy, Raman spectroscopy, and scanning electron microscopy (SEM). These analyses confirmed that the desired phases had formed, along with small amounts of oxide byproducts (α-Fe₂O₃, CuO) arising from the erosion of the electrodes. Photocatalytic activity was evaluated through the degradation of organic dyes (Reactive Red 6C, Rhodamine B, and Methylene Blue) under UV-light irradiation. Both catalysts achieved complete dye degradation within 90 min of UV irradiation (after an initial 30 min dark adsorption step, total experiment time 120 min). Notably, selective performance was observed: PB exhibited higher activity toward the cationic dye Methylene Blue, while Cu-PBA was more effective for the anionic dye Reactive Red 6C. This selectivity is attributed to the specific oxide impurities forming heterojunctions that facilitate charge separation and generate distinct reactive oxygen species. The plasma–liquid method offers a rapid and environmentally benign route to functional PBA-based composites, with potentially scalable characteristics pending further engineering optimization. These findings highlight the potential of utilizing synthesis-induced impurities to tailor photocatalytic selectivity for water purification applications. Full article