Search Results (168)

During the period from 2020 to 2024, 900 springs were mapped on the southern slopes of Medvednica Mountain Nature Park. Physicochemical parameters (temperature, pH, and electrical conductivity) were measured at 701 of these springs using a portable multimeter, and results were analyzed in relation to local lithology and human activities. This research provides the first results of this kind in this study area, aiming to expand the knowledge on local springs and to support the future protection and management of spring ecosystems. Springs on the Medvednica mountain showed substantial variation in measured parameters. The temperature ranged from 3.4 to 18.9 °C, reflecting local hydrological conditions, aquifer characteristics, and seasonal variability. Electrical conductivity (EC) ranged between 41 μS/cm and 2062 μS/cm, determined by both hydrogeological settings and anthropogenic impacts such as winter road salting. The pH values showed moderate variability, remaining mostly within neutral levels. These results emphasize the importance of continued monitoring and further research of Medvednica springs, in order to highlight their importance and to preserve their ecological and hydrological roles. Full article

Industrial effluents pose a significant concern because they contain a variety of metals and metalloids that have detrimental effects on the environment. Conventional techniques are widely used in effluent treatment plants (ETPs) to remove metallic pollutants; however, they are less effective, are costly, and generate secondary toxic waste. Mycosorbent would be a sustainable and economical alternative to conventional techniques, as it offers numerous advantages. In this study, we shed light on the development of mycosorbent, which could be potentially applicable in the treatment of industrial effluent. In a competitive (i.e., multimetal system) optimisation study, mycosorbent AD2 exhibited a maximum biosorption capacity of 3.7 to 6.20 mg/g at pH 6.0, with an initial metal ion concentration of 25 mg/L, a contact time of 2 h, at 50 ± 2 °C, and a pH_PZC of 5.3. The metal-removal capacity increased up to 1.23-fold after optimisation. The thermodynamic parameters confirmed that the AD2 mycosorbent facilitated an endothermic, feasible, and spontaneous biosorption process. The FT-IR and SEM characterisation analysis confirmed the adsorption of metals on the surface of the mycosorbent from the aqueous system. This study demonstrated that mycosorbent could be an effective tool for combating metallic pollutants in various industrial effluents. Full article

The goal of this study is to develop an efficient machine learning framework for designing high-hardness multi-metal nitride coatings, overcoming the limitations of traditional trial-and-error methods. The development of multicomponent metal nitride hard coatings via multi-arc ion plating remains a significant challenge due to the vast compositional search space. Although theoretical studies in macroscopic, mesoscopic, and microscopic domains exist, these often focus on idealized models and lack effective coupling across scales, leading to time-consuming and labor-intensive traditional methods. With advancements in materials genomics and data mining, machine learning has become a powerful tool in material discovery. In this work, we construct a compositional search space for multicomponent nitrides based on electronic configuration, valence electron count, electronegativity, and oxidation states of metal elements in unary nitrides. The search space is further constrained by FCC crystal structure and hardness theory. By incorporating a feature library with micro-, meso-, and macro-structural characteristics and using clustering analysis with theoretical intermediate variables, the model enriches dataset information and enhances predictive accuracy by reducing experimental errors. This model is successfully applied to design multicomponent metal nitride coatings using a literature-derived database of 233 entries. Experimental validation confirms the model’s predictions, and clustering is used to minimize experimental and data errors, yielding a strong agreement between predicted optimal molar ratios of metal elements and nitrogen and measured hardness performance. Of the 100 Vickers hardness (HV) predictions made by the model using input features like molar ratios of metal elements (e.g., Ti, Al, Cr, Zr) and atomic size mismatch, 82 exceeded the dataset’s maximum hardness, with the best sample achieving a prediction accuracy of 91.6% validated against experimental measurements. This approach offers a robust strategy for designing high-performance coatings with optimized hardness. Full article

The efficient electrochemical reduction of carbon dioxide to formate under mild conditions is a promising approach to mitigate the energy crisis, but requires the use of high-performance catalysts. The selectivity and activity of catalysts can be enhanced through multi-metal doping, further advancing the electrochemical reduction of CO₂ to formate. This study demonstrates a co-electrodeposition strategy for synthesizing SnBi electrocatalysts on pretreated copper foam substrates, systematically evaluating how the Sn²⁺/Bi³⁺ molar ratio in the electrodeposition solution and the applied current density affect the catalytic performance for CO₂-to-formate conversion. Optimal performance was achieved with a molar ratio of Sn²⁺ to Bi³⁺ of 1:0.5 and a deposition current density of 3 mA cm⁻², resulting in a formate Faradaic efficiency (FE_formate) of 97.80% at −1.12 V (vs. RHE) and a formate current density of 26.9 mA·cm⁻². Furthermore, the Sn₁Bi_0.50-3 mA·cm⁻² electrode demonstrated stable operation at the specified potential for 9 h, maintaining a FE_formate above 90%. Compared to previously reported metal catalysts, the SnBi catalytic electrode exhibits superior performance for the electrochemical reduction of CO₂ to HCOOH. The study highlights the significant impact of the metal ion molar ratio and deposition current density in the electrodeposition process on the characteristics and catalytic performance of the electrode. Full article

Pyrometallurgical recycling of lithium-ion batteries presents distinct advantages including streamlined processing, simplified pretreatment requirements, and high throughput capacity. However, its industrial implementation faces challenges associated with high energy demands and lithium loss into slag phases. This investigation develops an integrated reduction smelting–chloridizing volatilization process for the comprehensive recovery of strategic metals (Li, Mn, Cu, Co, Ni) from spent ternary lithium-ion batteries; calcium chloride was selected as the chlorinating agent for this purpose. Thermodynamic analysis was performed to understand the phase evolution during reduction smelting and to design an appropriate slag composition. Preliminary experiments compared carbon and aluminum powder as reducing agents to identify optimal operational parameters: a smelting temperature of 1450 °C, 2.5 times theoretical CaCl₂ dosage, and duration of 120 min. The process achieved effective element partitioning with lithium and manganese volatilizing as chloride species, while transition metals (Cu, Ni, Co) were concentrated into an alloy phase. Process validation in an induction furnace with N₂-O₂ top blowing demonstrated enhanced recovery efficiency through optimized oxygen supplementation (four times the theoretical oxygen requirement). The recovery rates of Li, Mn, Cu, Co, and Ni reached 94.1%, 93.5%, 97.6%, 94.4%, and 96.4%, respectively. This synergistic approach establishes an energy-efficient pathway for simultaneous multi-metal recovery, demonstrating industrial viability for large-scale lithium-ion battery recycling through minimized processing steps and maximized resource utilization. Full article

Highly efficient photocatalysts for solar energy conversion require effective charge carrier separation and rapid interfacial transport kinetics to maximize electron availability. Two-dimensional Ti₃CNT_x, a novel conductive material in the MXene family with exceptional electrical conductivity, has emerged as an ideal electron transfer mediator due to its large specific surface area and abundant active terminal groups. In this work, we strategically integrated the 2D multi-metal sulfide Cu-Zn-In-S (CZIS) with 2D Ti₃CNT_x nanosheets through physical mixture, constructing a heterostructured 2D/2D CZIS/Ti₃CNT_x composite photocatalyst for the hydrogen evolution reaction. The unique architecture significantly accelerates electron migration from CZIS to Ti₃CNT_x, while synergistically promoting the spatial separation and directional transfer of photogenerated electron–hole pairs (e⁻/h⁺). When the hydrogen evolution reaction is carried out under identical conditions, the hydrogen yield rate is 4.3 mmol g⁻¹ h⁻¹ with pristine CZIS but is improved dramatically to 14.3 mmol g⁻¹ h⁻¹ when the composite containing an adequate amount of 2D Ti₃CNT_x is used. This study offers new insight into the rational design and controllable synthesis of Ti₃CNT_x-based composite photocatalytic systems for efficient photocatalytic hydrogen production. Full article

Objectives: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with an unclear etiology. This study aimed to assess chronic heavy metal exposure in ALS patients in South Korea by comparing hair concentrations of common (Hg, Pb, Cd) and rare (U, Th, Pt) metals with healthy controls. Methods: Hair samples were collected from 66 ALS patients and 70 healthy individuals at Rodem Hospital between 2022 and 2025. Metal concentrations were measured using inductively coupled plasma mass spectrometry (ICP-MS) following standardized washing and digestion protocols. Results: ALS patients showed significantly higher levels of Hg, Pb, Cd, Al, As, and U than controls (p < 0.05). Notably, 40% of ALS patients had Hg levels exceeding 50% of the reference upper limit, compared to only 10% of controls. Elevated levels of uranium and other rare metals were also observed in specific ALS cases. Conclusions: These findings suggest a possible association between heavy metal exposure and ALS in South Korea. Hair analysis may serve as a useful tool for identifying environmental factors contributing to ALS pathogenesis. Full article

Heavy metals in lead refining waste slag pose persistent environmental risks, challenging conventional treatment methods that struggle to balance long-term stabilization with resource recovery potential. To address this issue, we developed a sustainable stabilization strategy. The simultaneous and long-lasting stabilization of Zn, Pb, and As heavy metals in lead refining waste slag was achieved by using an Fe-S(II) stabilizer, and the leaching toxicity of Zn, As and Pb was less than 1 mg/L, which is lower than the concentration limit of the Identification standards for hazardous wastes–Identification for extraction toxicity (GB5085.3-2007). The samples were analyzed by characterization before and after stabilization, and it was found that Fe-S(II) formed a protective layer of sulfide capsule on the surface of the samples. This stabilization mechanism, which has been termed the “nucleation-capture-sulfide encapsulation” process, involves after the oxidation of Fe⁰ to form a core–shell structure for trapping metal ions, where the external oxide layer undergoes mineralization via S(II) sulfide reduction. This microencapsulation-based passivation not only ensures long-term heavy metal immobilization but also preserves the slag’s potential for secondary resource recovery, aligning with circular economy principles. By minimizing environmental leakage risks while retaining metal reclamation feasibility, this approach offers a green and sustainable solution for heavy-metal-laden industrial waste management. Full article

In smart meters, loose contact at screw terminals can lead to prolonged overheating and arcing, posing significant fire hazards. To mitigate these risks through early fault detection, this study proposes a data-driven framework integrating the Local Outlier Factor (LOF) and Multiple Linear Regression (MLR) algorithms. Voltage differentials, extracted from operational data collected via a simulated multi-meter metering enclosure, are leveraged to diagnose terminal contact degradation. Specifically, LOF identifies arc faults, characterized by abrupt and transient voltage deviations, by detecting outliers in voltage differentials, while MLR quantifies contact resistance through regression analysis, enabling precise loose contact detection, a condition associated with gradual and persistent voltage changes due to increased resistance. Extensive validation demonstrates the framework’s robustness, outperforming conventional centralized methods in diagnostic accuracy and adaptability to diverse load conditions. Full article

Towards the bioremediation of toxic compounds from aquatic environments using living microalgae, Chlorella vulgaris has emerged as a promising candidate for the removal of heavy metals. The present study advances the scale-up of the microalga’s culture and investigates its efficiency in multi-metal removal (Cu, Cd, Ni, Pb, and Zn at 1 ppm each). Two aeration conditions were investigated: standard/conventional aeration (SA), and an innovative, custom-built micro-bubble aeration (MBA), which optimizes CO₂ residence time to enhance photosynthesis. Conducted in a pilot-scale 30 L photobioreactor (PBR) over a cultivation period of 7 days, control and multi-metal treated cultures were monitored for pH, cell population growth, and pigment content. Heavy metal removal efficiency was evaluated by means of atomic absorption spectroscopy (AAS) on Days 3 and 7 of cultivation. The comparative results reveal that MBA significantly enhances both the population and the photosynthetic pigment content of the cultures. Furthermore, the heavy metal removal efficiency under MBA reached up to 95% even by Day 3 of cultivation, remarkably higher than the 67% of the SA treated culture. These findings not only demonstrate Chlorella vulgaris’s effectiveness in multi-metal treated systems but also highlight the potential of advanced aeration systems to enhance bioremediation efficiency in larger-scale aquatic environments. Full article

Potentially toxic elements (PTE), such as cadmium (Cd), lead (Pb), and arsenic (As), threaten rice (Oryza sativa L.) crop productivity and pose significant risks to human health when they are present in soil. This review summarizes the current understanding of soil and rice contamination with As, Cd, and Pb to provide an in-depth understanding of the dynamics of these contaminants and the mechanisms regulating their flow from soil to plants. It focuses on the following aspects: (1) these metals’ geochemical distribution and speciation in soil–rice systems; (2) factors influencing the transformation, bioavailability, and uptake of these metals in paddy soils; (3) metal uptake, transport, translocation, and accumulation mechanisms in rice grains; and (4) the roles of transporters involved in metal uptake, transport, and accumulation in rice plants. Moreover, this review contributes to a clearer understanding of the environmental risks associated with these toxic metals in soil–rice ecosystems. Furthermore, it highlights the challenges in simultaneously managing the risks of As, Cd, and Pb contamination in rice. The study findings may help inspire innovative methods, biotechnological applications, and sustainable management strategies to mitigate the accumulation of As, Cd, and Pb in rice grains while effectively addressing multi-metal contamination in paddy soils. Full article

Nuclear technology, while offering significant benefits across various sectors, poses potential health risks due to uranium (U) contamination, particularly through its internalization and subsequent interactions with biological systems. This study investigates the binding of uranyl (UO₂²⁺) and zinc (Zn²⁺) ions to Human Serum Albumin (HSA) that is already bound to fatty acids (FAs), using all-atom molecular dynamics (MD) simulations. The analysis focuses on the structural and dynamic alterations in the protein’s multi-metal binding site (MBS-A) caused by FA binding. Results reveal that FA binding induces a conformational change in HSA, disrupting the pre-formed MBS-A binding site, while still allowing uranyl and zinc ions to interact with residue D249 through strong Coulombic interactions. Secondary binding sites, associated with calcium and zinc binding, remain largely unaffected by FAs, providing alternative coordination for metal ions. This study also explores the binding and unbinding pathways of the metal ions using well-tempered meta-dynamics (WT-MtD), showing that while FA binding disrupts the primary metal binding site, it does not completely inhibit the binding of both uranyl and zinc ions. These findings offer new insights into the nature of uranium’s interactions with blood serum proteins and the role of fatty acids in modulating these interactions, which may help in designing future strategies for managing uranium contamination in biological systems. Full article

In electric vehicles (EVs), bearings in traction motors are increasingly prone to electrical damage under operational currents and voltages, leading to accelerated wear and reduced lifespan. This study examines the extent of bearing damage under low-speed, electrically charged conditions to understand wear behavior at boundary lubrication better. Bearings were driven at low speed by a motor, with inner and outer rings connected to a pulsed power supply’s positive and negative terminals, simulating real-world shaft voltage conditions. The applied electrical parameters included voltages from 5 V to 240 V and frequencies of 10 kHz, leading to voltages at the bearing peaking between 0.1 and 12 V measured by an oscilloscope and multimeter. The tested bearings were disassembled, and scanning electron microscopy (SEM) was used to assess the damage associated with varying electrical stresses. The results revealed distinct wear patterns and degradation effects when the shaft current and peak voltage reached 2.5 A and 12 V, emphasizing the critical need for protective strategies. Future work will focus on evaluating the impact of higher rotational speeds and controlled power supply conditions to analyze the effects of increased power supply settings and compare outcomes to low-speed scenarios. Full article

Leachates from electronic waste, slag dusts generated during the processing of electronic waste, sweeping jewelry, and municipal solid-waste incineration residues contain a myriad of base metals, such as aluminum (Al: 10–2000 mg/L), copper (Cu: 10–1000 mg/L), iron (Fe: 10–500 mg/L), nickel (Ni: 0.1–500 mg/L), lead (Pb: 1–500 mg/L), tin (Sn: 1–100 mg/L), and zinc (Zn: 5–500 mg/L), which are present at much higher quantities than Au (0.01–10 mg/L), which raises several drawbacks to the efficient recycling of Au with high purity using hydrometallurgical strategies. The aim of this work was to study the efficiency and selectivity of two strong basic anion exchange (DOW^TM XZ-91419.00 and Purogold^TM A194) resins to recover Au from a chloride multi-metal solution containing these metals. For both resins, the adsorption kinetic and equilibrium parameters for Au(III), determined at 1.12 mol/L HCl, Eh = 1.1 V, and 25 °C, proceeded according to a pseudo-second order and a Langmuir isotherm (qmax was 0.94 and 1.70 mmol/g for DOW^TM XZ-91419.00 and Purogold^TM A194 resins, respectively), respectively. Continuous adsorption experiments of Au (48 µmol/L; 2.0%) from a chloride multi-metal solution evidenced high Au retention capacity and selectivity to Au over Al, Cu, Fe, Ni, and Zn but low selectivity to Au over Ag and Sn for both resins. Concentrated (>3.3 mmol/L) and pure (>94%) Au eluates were obtained for both resins. Full article

The existing electricity infrastructure in Cartwright depends on diesel generators and needs renewable energy integration and remote monitoring. This project aims to enhance the proposed hybrid system with IoT-based optimization by leveraging a low-cost open-source SCADA system and accomplished monitoring and control capabilities. Electrical data were collected and analyzed from the energy system via sensors using the Arduino UNO R4 Wi-Fi as an RTU. The designed SCADA system would optimize Cartwright’s energy system, allowing for real-time remote tracking and control via the Arduino IoT cloud platform. The voltage and current values obtained with the setup were accurate and close to the actual multimeter values over the measurement range. The project outcome included efficient real-time data acquisition and visualization on remote dashboards, enabling cloud monitoring of key electrical parameters. An alert mechanism was incorporated as a buzzer alarm in the event of under-voltage readings to trigger intervention from operators to take swift action to ensure system reliability and safety. One observation made was that, while the buzzer is not directly tied to current readings, it can be programmed to signal more issues like over-current. Full article